This study evaluates the impact of integrated nutrient management (INM) practices on potato yield and quality in the coastal regions of Bangladesh. Potatoes are a vital crop in Bangladesh, and optimizing nutrient management is crucial for enhancing productivity and ensuring sustainability. The research was conducted in multiple agro-ecological zones in Gopalganj, where field trials were established with three treatment groups: traditional fertilization practices, organic fertilizers, and a combination of organic and inorganic fertilizers (INM). The results demonstrated that INM practices significantly improved potato yield and quality compared to traditional practices. Specifically, the INM group exhibited a 20% increase in yield and enhanced tuber quality, characterized by higher nutrient content and reduced incidence of common diseases. Soil health parameters, including microbial activity, nutrient availability, and organic matter content, were also positively influenced by INM practices. Economic analysis revealed that, despite higher initial costs, INM practices were more cost-effective in the long run due to reduced fertilizer input and improved crop yield. This study highlights the importance of adopting sustainable nutrient management strategies to improve potato production in Bangladesh. The findings underscore the potential of INM practices to enhance food security and support sustainable agricultural development. By integrating organic and inorganic fertilizers, farmers can achieve higher yields, better-quality produce, and maintain soil health, making INM a viable strategy for the future of potato farming in Bangladesh.

Introduction: The COVID-19 pandemic prompted a global shift to distance learning, profoundly affecting children's mental health. In Saudi Arabia, remote education was implemented on March 8, 2020, impacting students across various factors, such as age, vaccination status, and school system. This shift lasted between 1.5 to 2.5 years, influencing students’ experiences differently. This study explores the psychological impact of distance learning on children and adolescents, with the goal of informing mental health advocacy and guiding educational strategies in scenarios where distance learning becomes essential. Methods: We conducted a cross-sectional study using an online survey distributed to parents of children aged 6 to 18 in the major metropolitan areas of Jeddah and Riyadh. The survey collected demographic information and included the validated Arabic version of the Vanderbilt ADHD Diagnostic Rating Scale. This tool assesses behavioral challenges, anxiety, and symptoms of attention deficit hyperactivity disorder (ADHD). Results: The study found that 71.6% of families reported a positive experience with distance learning. A significant correlation was identified between parents' marital status and children's ability to cope with remote education. Interestingly, students without ADHD symptoms experienced three times more negative outcomes than those with ADHD. However, despite reporting fewer negative experiences, children with ADHD symptomatology faced heightened symptom severity and academic difficulties. The results further indicated that 5.4% of students were classified with ADHD predominantly inattentive presentation, 1.8% with predominantly hyperactive/impulsive presentation, and 3.9% with combined ADHD presentation. Additionally, 7.2% of students screened positive for oppositional defiant disorder, 1.5% for conduct disorder, and 6.6% for anxiety or depression. Conclusion: This study is the first comprehensive, multi-city investigation in Saudi Arabia to analyze the relationship between distance learning experiences, sociodemographic factors, and mental health symptoms in children. The findings provide essential insights for future research and offer a foundation for developing targeted interventions to support children’s mental health and education in the region.

This study presents the development of a cutting-edge virtual reality (VR) application aimed at education in industrial and technical environments. Using the Meta Quest 3 system and the ShapesXR platform, the application allows users to interactively disassemble and assemble 3D models in an immersive environment. Detailed models were developed through parametric modeling tools such as Autodesk Fusion 360 and 3D optimization software like Blender, optimized for a high-performance VR environment. The feasibility of the platform as an innovative educational tool was evaluated, and areas for improvement for future iterations of the system were identified.

I report on the first observed self-amplification by stimulated emission of 0.2THz and 1.63THz radiation using InGaAs/GaAs HEMT operating in the deep saturation regime at room temperature. I demonstrate both theoretically and experimentally that the Sub-THz and THz FETs response is due to rectification of the nonlinear dependence of the device current-voltage characteristics. FETs do operate as a nonlinear THz mixers and rectifiers and its open-drain responsivity is given by a similar expression to that of zero-bias Schottky diode detector. However, operating FETs deep in the saturation regime does allow the accurate tuning of the device to the resonance condition or the negative resistance mode at room temperature, hence FETs can be tuned in the deep saturation regime to enable sub-THz and THz lasing effect. This observed sub-THz and THz laser phenomena using FETs will revolutionize human technology in all fields of life in the near future.

Accurate prediction of pregnancy risk levels is essential for preventing maternal and fetal complications. This study explores the application of ensemble machine learning models combined with oversampling techniques to predict pregnancy risk levels. We utilized a publicly available dataset from the UCI Machine Learning Repository, performing extensive feature engineering, including the introduction of new features like Pulse Pressure and Mean Arterial Pressure. To address class imbalance, we employed the Adaptive Synthetic Sampling (ADASYN) method. We conducted comprehensive hyperparameter tuning to enhance model performance and achieve optimal predictive results. Additional evaluation metrics, including sensitivity, specificity, and precision-recall curves, were used to assess model per- formance comprehensively. Our findings demonstrate that the Voting Classifier, particularly when combined with ADASYN oversampling and optimized hyperpa- rameters, achieves an accuracy of 87.19% and a macro F1 score of 87.66%, effec- tively distinguishing between ’low risk’ and ’mid risk’ pregnancy cases. This work contributes to the field by enhancing prediction accuracy, providing insights into important features influencing pregnancy risk, and addressing ethical considerations in deploying machine learning models in healthcare.

Background: Recent advancements have highlighted the potential of cannabis and its phy-tocannabinoids (pCBs) in skin health applications. These compounds, through their interac-tion with the endocannabinoid system (ECS), show promise for skin health products. Their ability to regulate inflammation, oxidative stress, and cellular proliferation makes them bene-ficial in addressing skin issues like inflammation, scarring, healing, acne, and aging, posi-tioning them as valuable tools for innovative skincare solutions. Methods: We investigated the cellular and molecular effects of proprietary pCB-based for-mulations on ECS modulation, inflammation, and skin regeneration. Using human dermal fibroblasts (HDF) and keratinocytes (HaCat), we assessed the formulations in both pre-treatment and treatment scenarios following exposure to stress-inducing agents. Key molecular markers were analyzed to tackle their efficacy in mitigating inflammation and promoting structural integrity and regeneration. Results: Our findings show that these formulations significantly reduce inflammation, pro-mote skin regeneration, and improve structural functions. In vivo studies confirmed that the formulations were well-tolerated and led to noticeable improvements in skin health, including enhanced barrier function. Conclusions: This study demonstrates the safety and efficacy of pCB-based formulations for cosmeceutical applications. By combining molecular analysis with in vivo testing, our research provides new insights into the therapeutic potential of pCBs for managing various skin con-ditions.

Seagrass beds and salt marshes play a crucial role in preserving and restoring estuarine ecosystems, providing essential services such as carbon sequestration, water filtration, and habitat for diverse marine life. Mapping these areas, particularly in remote locations, is challenging but essential for effective conservation efforts. This study explores the use of drones equipped with multispectral cameras to map seagrass meadows and salt marshes in estuarine environments. The methodology involved composing images into a multilayer multidimensional dataset, performing exploratory data analysis and principal component analysis, and utilizing Random Forest supervised classification to generate detailed maps. Applied to a case study of the Mira River in southern Portugal, this approach achieved mapping accuracies as high as 94% with kappa values of 0.91. These results demonstrate the efficacy of drone-based multispectral imaging and advanced analytical techniques in accurately mapping and monitoring critical estuarine habitats, offering a valuable tool for conservation and restoration initiatives.

Bimetallic PdCu nanoparticles with different Pd:Cu ratios and morphologies can be synthesized and immobilized on a variety of support materials. Accordingly, PdCu nanoparticles can be efficiently applied as heterogeneous catalysts in a large number of organic transformations including C-C coupling and cross-coupling reactions. As related to their favorable electronic and structural interactions, the catalytic performance of PdCu bimetallic nanoparticles may be superior to the monometallic species. The heterogeneous catalysts can be recovered and reused and the presence of copper tends to reduce the cost of the expensive Pd catalyst, which would be beneficial for industrial applications.

We present a synchronization transition study of the locally coupled Kuramoto model on extremely large graphs. We compare regular 405 and 1004 lattice results with those of 120002 lattice substrates with power-law decaying long links (ll). The latter heterogeneous network exhibits ds>4 spectral dimensions. We show strong corrections to scaling and mean-field type of criticality at d=5, while logarithmic corrections at d=4. Contrary, the ll model exhibits a non-mean field smeared transition, with oscillating corrections, suggesting the network heterogeneity is relevant, causing frustrated synchronization, akin to Griffiths effects.

This review provides a comprehensive summary of the latest international research on detection methods for glucosinolates in cruciferous plants. The article examines various analytical tech-niques, including high-performance liquid chromatography (HPLC), liquid chromatog-raphy-mass spectrometry (LC-MS), and enzyme-linked immunosorbent assay (ELISA), while highlighting their respective advantages and limitations. Additionally, the review delves into recent advancements in sample preparation, extraction, and quantification methods, offering valuable insights into the accurate and efficient determination of glucosinolate content across diverse plant materials. Furthermore, it underscores the critical importance of standardization and validation of these methodologies to ensure reliable glucosinolate analysis in both scientific research and industrial applications.

Cardiolipin (CL), an essential phospholipid, is localized in the mitochondrial inner membrane, where it plays a critical role in oxidative phosphorylation (OXPHOS) and energy metabolism. This study reviews current findings on cardiolipin's function within neurodegenerative diseases (NDDs), focusing on Alzheimer's and Parkinson's diseases, along with Barth Syndrome. Using lipidomics and protein-lipid interaction analysis, we examine the role of CL in mitochondrial integrity and neuronal function. The primary objective is to analyze how alterations in CL composition and distribution can serve as biomarkers and therapeutic targets for NDDs. Results highlight significant insights into the potential for CL-targeted therapies.

Duchenne muscular dystrophy (DMD) is a severe genetic muscle disease due to mutations of dystrophin gene. There is no cure for DMD. Using a dystrophin-/-utrophin-/- (DKO-Hom) mouse model, we investigated the PGE2/EP2 pathway in the pathogenesis of dystrophic muscle and its potential as a therapeutic target. We found that Ep2, Ep4, Cox-2, 15-Pgdh mRNA, and PGE2 are significantly increased in DKO-Hom mice compared to wild-type (WT) mice. The EP2 and EP4 receptors are mainly expressed in CD68+ macrophages and are significantly increased in the muscle tissues of both dystrophin-/- (mdx) and DKO-Hom mice compared to WT mice. Osteogenic and osteoclastogenic gene expression in skeletal muscle also increased in DKO-Hom mice, which correlates with severe muscle heterotopic ossification (HO). Treatment of DKO-Hom mice with the EP2 antagonist PF04418948 for 2 weeks, increased body weight, reduced HO and muscle pathology by decreasing both total macrophages (CD68+) and senescent macrophages (CD68+P21+), while increasing endothelial cells (CD31+). PF04418948 also increased bone volume/total volume (BV/TV), trabecular thickness (Tb.Th) of the tibia trabecular bone and the cortical bone thickness of both the femur and tibia without affecting spine trabecular bone microarchitecture. In summary, our results indicate that targeting EP2 improves muscle pathology and maintains bone mass.

Dhaka, one of the fastest-growing megacities, is highly vulnerable to urban flooding due to rapid urbanization, inadequate drainage systems, and climate change-induced extreme weather events. This study describes the development of a comprehensive urban flood forecasting system for Dhaka City using the MIKE+ hydrodynamic model. The system integrates hydrological and hydraulic components, modeling complex interactions among surface runoff (pluvial), riverine overflow (fluvial), drainage networks, and overland flow dynamics. The model utilizes real-time data from river gauges, rainfall stations, and pump stations to enhance forecasting accuracy. Sensitivity analysis identified ten key parameters influencing model performance, which were adjusted during calibration. The model was calibrated and validated using historical flood events from 2019 to 2022, achieving strong performance metrics, including Nash-Sutcliffe Efficiency (NSE) greater than 0.65, Coefficient of Determination (R²) above 0.80, and Mean Absolute Error (MAE) less than 0.1 meters. The system was further tested using significant flood events from 2020 and 2021, cross-validated with flood marks and community-reported observations, ensuring reliability. This advanced urban flood forecasting tool supports decision-making and emergency response by providing early warnings and flood scenarios, enabling proactive flood management for a rapidly urbanizing city like Dhaka.

1) Background: Impeded resolution of inflammation contributes substantially to the pathogenesis of Alzheimer's disease (AD); consequently, resolving inflammation is pivotal to the amelioration of AD pathology. This can potentially be achieved by the treatment with specialized pro-resolving lipid mediators (SPMs), which should resolve neuroinflammation in brains. 2) Methods: Here, we report the effects of long-term treatment with a SPM, maresin like 1 (MarL1), on AD pathogenesis in a transgenic 5xFAD mouse model. 3) Results: MarL1 treatment reduced Aβ overload, curbed the loss of neurons in brains especially cholinergic neurons associated with cleaved-caspase-3-associated apoptotic degeneration, reduced microgliosis and the pro-inflammatory M1 polarization of microglia, curbed the AD-associated decline in anti-inflammatory Iba1⁺Arg-1⁺-M2 microglia, inhibited phenotypic switching to pro-inflammatory N1 neutrophils, promoted the blood–brain-barrier-associated tight-junction protein claudin-5 and decreased neutrophil leakage in 5xFAD brains, and induced the switch of neutrophils toward the inflammation-resolving N2 phenotype. 4) Conclusion: Long-term administration of MarL1 mitigates AD-related neuropathogenesis in brains by curbing neuroinflammation and neurodegeneration. These findings provided the preclinical leads and mechanistic insights for the development of MarL1 into an effective modality to ameliorate AD pathogenesis.

Planting plays a significant role in commercial forestry. Labour-related issues (scarcity, increased wages, absenteeism, ageing and high turnover), inconsistent work quality, increased operational costs, and poor ergonomics prompted the development of innovative planting techniques. This study aimed to assess the productivity (plants/PMH), worker productivity (plants/worker PMH), work quality and tree survival of a manual and a semi-mechanised planting method. Two study sites at Flatcrown and Kwambonambi, consisting of 37 plots, were planted at 1333 stems per ha, alternating across the study sites. Block-level and elemental-level time studies were conducted, followed by work quality assessments. The manual method planted 7.23 ha/shift (Flatcrown) and 5.89 ha/shift (Kwambonambi), whilst the semi-mechanised method planted 4.72 ha/shift (Flatcrown) and 3.19 ha/shift (Kwambonambi). The manual method was 50 to 60% more productive (plants/PMH) than the semi-mechanised method. In terms of plants/worker PMH, higher worker efficiency of 30 - 40% was observed at the Flatcrown site, which was characterised by level terrain and low residue presence compared to the Kwambonambi site, which had a gentle (< 20%) terrain and medium residue presence. Planting quality was conducted according to planting specifications. The two methods did not significantly differ, although the study suggests that the proportion of good-quality plantings could be somewhat higher for the manual method. There was no significant difference in tree survival across methods and sites after one month.

The development of advanced photochemical technologies requires a synergy of scientific innovation and business acumen. This study explores the pivotal role of business analytics in accelerating the development and commercialization of photochemical technologies. By integrating data-driven insights with chemical engineering principles, businesses can optimize research and development (R&D) investments, streamline process efficiencies, and identify high-potential applications. This research employs a mixed-methods approach, combining case studies, surveys, and statistical analysis to investigate the impact of business analytics on photochemical technology development. Key findings highlight the benefits of analytics-driven decision-making, including enhanced productivity, reduced time-to-market, and improved return on investment (ROI). Furthermore, this study identifies critical factors influencing the effective adoption of business analytics in photochemical technology development, including data quality, organizational culture, and cross-functional collaboration. The results provide valuable implications for researchers, practitioners, and policymakers seeking to harness the potential of business analytics to drive innovation and growth in the photochemical industry

This review investigates the integration of machine learning (ML) and blockchain technology, highlighting their combined potential to drive transformation across sectors such as healthcare, finance, and supply chain management. ML offers data-driven insights through predictive analytics, automation, and intelligent decision-making, while blockchain provides a secure, decentralized framework with transparency and immutability. Together, these technologies present solutions to critical challenges, including fraud detection, operational inefficiencies, and data privacy, promising significant advancements across various industries. ML enhances diagnostics and treatment personalization in healthcare, while blockchain protects sensitive medical data. In finance, blockchain fosters transparency within decentralized finance (DeFi), while ML strengthens fraud detection and trading strategies. In supply chain management, blockchain ensures traceability, while ML optimizes logistics and demand forecasting. Despite their synergies, integrating ML with blockchain faces challenges, notably in scalability, computational efficiency, and privacy. This review critically examines recent advancements, practical use cases, and barriers to adoption, synthesizing strategies that can address these challenges to unlock the transformative potential of these emerging technologies.

Congenital Dermatofibrosarcoma Protuberans (DFSP) is an extremely rare low-grade soft tissue sarcoma arising from the dermis. Accounting for less than 0.1% of all malignancies and approximately 1% of soft tissue sarcomas, congenital DFSP presents a significant diagnostic challenge due to its atypical presentation in neonates and infants. Often mimicking benign skin lesions such as hemangiomas, vascular malformations or pigmented moles, it is frequently misdiagnosed, leading to delays in appropriate treatment. This narrative review highlights the importance of early recognition and accurate diagnosis of congenital DFSP to prevent aggressive local growth and reduce the risk of recurrence. We discuss the epidemiology, clinical presentation and diagnostic hurdles associated with this condition. Histopathological examination remains the gold standard for diagnosis, with characteristic findings of spindle-shaped cells in a storiform pattern and strong CD34 positivity on immunohistochemical staining. Imaging modalities such as MRI may be helpful in assessing the extent of the lesion. Surgical excision with clear margins, preferably using Mohs micrographic surgery, is the cornerstone of treatment, balancing oncological control with functional and aesthetic considerations in paediatric patients. A multidisciplinary approach involving dermatologists, paediatric surgeons, oncologists and pathologists is essential for optimal management. We also emphasise the need for clinician vigilance, early biopsy of suspicious lesions, patient education and long-term follow-up to improve outcomes and quality of life for affected children.

To promote a sustainable coffee industry, Davao Oriental has branded its coffee in association with Mt. Hamiguitan. This study evaluated the smallholder coffee farmers’ practices along the Mt. Hamiguitan buffer zone, highlighting women’s roles, and their climate change awareness and adaptation strategies. Fifty-seven purposively selected farmers (28 females, 29 males) were interviewed using a validated semi-structured questionnaire. Data were analyzed using ARCGIS software, thematic analysis, and descriptive statistics. Most farmers (70%) were old (>51) with over 40 years of experience (51%). Non-participation in farmers’ associations (73.7%) surpassed active membership (26.3%), with only 14% attending training. Most farmers (65%) intercropped 500–600 Robusta coffee trees/ha, with many farms established in the 1980s (36%). Farmers per-formed clearing (82%) and pruning (94%) of their coffee. Women played a vital role, especially during drying and harvesting. Challenges faced by farmers included long distances to buyers (91%), low prices (84%), poor farm-to-market roads (83%), and a lack of buyers (39%). All respondents acknowledged climate change and employed adaptation strategies such as intercropping, pruning, using ipil-ipil for shade, and utilizing nets. Recommendations for fostering sustainable practices while improving livelihoods include joining cooperatives, providing training for enhanced climate change adaptation strategies, and increasing women's participation in coffee production.

The rapid advancement of digital technologies has led to a corresponding increase in fraudulent activities and harmful content across online platforms, posing significant challenges to both cybersecurity and content integrity. Traditional methods of fraud detection and content moderation, often based on rule-based systems, have proven inadequate in addressing the dynamic and sophisticated nature of these threats. This paper explores the application of machine learning (ML) techniques to enhance the effectiveness of fraud detection and content moderation systems. By leveraging supervised, unsupervised, and deep learning models, ML provides a more adaptive and scalable approach to identifying fraudulent transactions, detecting anomalies, and moderating content on digital platforms. Key methodologies discussed include the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for content analysis and the application of clustering algorithms for anomaly detection in financial transactions. The paper also addresses the challenges of implementing ML-based systems, such as data quality, bias, and the need for real-time processing, and proposes solutions to mitigate these issues. Furthermore, the ethical implications of using ML for these purposes are considered, with a focus on fairness and transparency. The findings demonstrate that ML significantly improves the accuracy and efficiency of fraud detection and content moderation, making it a critical tool in the fight against digital threats. Continued research and innovation in this field are essential to keep pace with evolving threats.

The increasing impact of digital technologies on society, marked by rapid and disruptive changes, including more efficient and modern processes, is pushing companies to adopt digital solutions. However, companies have limited options, and they can rarely avoid being affected by the speed and challenges introduced by digital technologies. Researchers have observed that the cybersecurity risks associated with these technologies are not fully apparent, despite the advantages they bring. In this context, this study aims to uncover the associated risks of using Digital Transformation technologies, which may lead to outcomes that fall short of initial expectations. A systematic literature review was conducted as the research methodology, involving a search for scientific articles in bibliographic databases to identify, organize, and classify these risks. This review offered visibility into the risks companies face when adopting digital technologies and provided a framework that highlights the most relevant risks. IT specialists validated these risks through a questionnaire, thereby aligning the systematic literature review with market realities regarding the risks companies are exposed to when using digital technologies. As a contribution to both the industry and academia, this work offers a framework to support companies in their Digital Transformation journey, helping them to recognize and manage the cybersecurity risks associated with digital technologies. Future studies could expand on this framework by developing a model for risk prioritization and mitigation.

Drying is one of the most effective preservation methods for extending the shelf-life of perishable foods. Microbial safety of the final products has not been a concern until outbreaks reported over the past decade in products contaminated with bacterial pathogens, in particular Salmonella. There is now an urgent need to understand the influence of process conditions on the thermal inactivation of pathogens in various drying operations. This study aimed to develop a predictive model for Salmonella inactivation in diced apples during hot air drying and in high-humidity heating in closed environment. Fresh-cut apple cubes (6 mm) were inoculated with a cocktail of Salmonella enterica strains (Enteritidis PT30, Montevideo 488275, and Agona 447967). The samples were placed in a preheated sample treatment box within a convection oven under three conditions: 1) open air drying, the sample box was open in the oven pre-set at 90°C (Drying-90); 2) closed-box heating, the oven was also set at 90°C (Preheating-90); and 3) closed-box heating, the oven was at 70°C (Pre-heating-70). For open-air drying, hot air at the oven temperature was circulated through the samples, facilitating the heating and drying. In closed box heating, the sample box maintained a high-humidity environment, retaining moisture evaporated from the apple samples and allowing heat conduction through the box walls. In each test, air temperature, relative humidity (RH), and sample temperatures were monitored, and Salmonella survival was measured at multiple time points. After 10 minutes of heating, the air RH levels in the closed-box configurations were 66% (Preheating-90) and 74% (Preheating-70), much higher than in the Drying-90 condition (30%). Results indicated that in-box heating achieved a 5-log reduction Salmonella after 8.5 min at 90°C, compared to 28.7 min in open air drying at the same temperature. It took 14 min to achieve 5-log reduction at 70°C in in-box heating. A mathematical model was developed to estimate sample surface RH based on sample temper-ature and ambient absolute humidity, with further adaptation to predict microbial inactivation over time using surface temperature and RH profiles. This model accurately predicted Salmonella inactivation across all treatments (RMSE = 0.92 log CFU/g, R² = 0.86), with thermal death param-eters comparable to literature values from isothermal studies. This study highlights the im-portance of air humidity in enhancing bacterial inactivation during drying and suggests that a preheating step under high-humidity conditions may serve as an effective control measure, en-suring microbial safety before conventional drying. Additionally, this work demonstrates the potential of a nonlinear mathematical model to predict microbial inactivation under complex drying conditions with fluctuating temperature and humidity.

China has formally applied to join the CPTPP. It is a realistic issue to comprehensively assess the possible impact of CPTPP labor provisions on China and explore effective countermeasures and measures. Vietnam, as the only socialist country among CPTPP member states, has revised its domestic labor law internally to build a dual labor union system, concluded FTAs externally and fought for a transitional period in the form of signing side letters to fulfill its obligations under the CPTPP, which has certain reference value for China. In recent years, the economic and trade agreements signed by China have adopted a more open attitude towards labor rules, and the current domestic laws have basically implemented the ILO’s requirements on core labor standards at the level of textual provisions, but there is still a certain gap between the concrete implementation and the substantive requirements of the CPTPP labor rules at the level of dispute settlement. In particular, influenced by the “aggressive interpretation” of labor obligations in the dispute settlement panel report of the EU-Korea labor case, the binding dispute settlement mechanism of the CPTPP may raise the requirements of labor obligations, and China should make thorough and comprehensive risk assessment and preparation for this, and actively participate in a multi-level and differentiated plan based on China’s actual needs, partial freezing and continuous follow-up.

(1) Background: This paper examines the organizational readiness of Polish energy enterprises in the face of these challenges, exploring factors [1-2] that influence their ability to undertake exploratory innovation and meet the demands of the energy-climate nexus. The study aims to investigate the organizational readiness of Polish energy enterprises, focusing on their capacity to explore and adopt innovative solutions in response to the dual pressures of advancing energy technologies and addressing climate imperatives [3-4]; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: The model illustrates the impact of each interaction among the three types of affordances, presenting a corresponding set of results specific to digital affordances and entrepreneurial dynamics.; (4) Conclusions: Furthermore, it offers policy recommendations to support the sector’s adaptive capacity and proposes frameworks for energy firms to enhance their exploration capabilities in response to both market and regulatory imperatives [5]. The research objective is to assess the current state of organizational readiness among Polish energy enterprises for implementing and scaling new technologies that contribute to both enhanced energy efficiency and resilience to climate change [6-7].

Background/Objectives: Physician-scientists are crucial in bridging gaps in medical knowledge and improving clinical care. Training for physician-scientists is critical in developing research skills and fostering advancements in healthcare. However, the number of physician-scientists is declining due to various challenges. This study aims to explore these challenges, identify solutions, and promote the integration of research into the medical profession and education. Methods: A nationally representative structured survey was conducted among physicians and residents at one of Lebanon’s central university hospitals to explore the perceptions, challenges, and outlook of physician-scientists. Multiple Correspondence Analysis (MCA) was used to further explore relationships among the key variables. Results: A total of 78 physicians and residents agreed to participate. The study underscores the importance of medical research, particularly clinical, in improving care and advancing science. MCA analysis highlighted distinct clusters of challenges including time constraints, limited funding, and balancing clinical duties with research commitments. Strategies to overcome these hurdles and better integrate research into medical education are proposed, emphasizing the importance of securing grants, institutional support, and early research integration into the medical curriculum. A positive association between physicians’ evaluation of research, their active practice, and encouragement of students, is highlighted. Conclusions: The study emphasizes the importance of medical research in improving care and advancing knowledge, identifies barriers to its integration into medical education, and proposes solutions. The perception of research's value positively influences its incorporation into practice and teaching, underlining the need for greater support for physician-scientists.

In recent years, the advent of Single Electron Transistor (SET) technology has opened new avenues for low power and high density circuit design. This paper presents an optimized design of an Arithmetic Logic Unit utilizing Single Electron Transistor MOSFET (SET-MOSFET) technology. The proposed design leverages the unique properties of SETs such as Coulomb blockade and quantum tunneling to achieve significant reductions in power consumption and increased speed. Through comprehensive simulations and analysis the performance metrics of the SET-MOSFET based ALU architecture includes optimized arithmetic and logical units are compared with conventional MOSFET based designs demonstrating the superiority of the proposed approach in terms of energy efficiency and operational speed. Comprehensive simulations demonstrate that the SET-MOSFET based ALU achieves a 99.97 % to 99.99 % reduction in power consumption, a 5 to 50 times increase in speed compared to conventional MOSFET based designs.

Dengue in neonates is an infrequent yet significant cause of neonatal sepsis in endemic areas, often underdiagnosed due to its nonspecific clinical presentation and the lack of specific guidelines for this population. We present a series of three cases of neonatal dengue in patients with fever and signs of late-onset sepsis, who were initially treated with antibiotics without identifying a bacterial agent. Following the diagnosis of dengue through positive IgM, antibiotics were discontinued, and the patients showed favorable clinical outcomes with early hospital discharges. This study highlights the importance of considering dengue as a differential diagnosis in neonates with late-onset sepsis, which could help reduce unnecessary antibiotic use and its adverse consequences. We propose implementing a specific diagnostic algorithm for neonates in endemic regions, including the addition of serological and molecular testing for dengue, to improve management and outcomes in this population.

Challenges persist in implementing inclusive education policies, particularly in rural provinces of South Africa. The challenges are compounded by the existing imbalance in the distribution of physiotherapists where more of them are in the health sector. Physiotherapists possess the competency to assess activity limitations and provide individualised therapeutic support to people living with physical disabilities. In addition, physiotherapists understand how the classroom and school environment can be modified to maximise participation of learners. This study explored the knowledge of education physiotherapists and challenges experienced in implementing the policy to Screen, Identify, Assess, and Support learners with physical disabilities. A qualitative, single exploratory case study design using virtual focus group discussion collected data from seven physiotherapists employed by the provincial Department of Education. Data were analysed with a six-step approach to inductive thematic data analysis on ATLAS.ti version 19 software. Three themes emerged. (1) Lack of knowledge due to insufficient training, policy misinterpretation and unclear roles and responsibilities (2) Lack of support structures with regards to the School-Based Support Teams, Circuit-Based Support Teams and District-Based Support Teams. (3) Shortage of physiotherapists’ posts in special schools. Strategies like ongoing in-service training, clarification with policy interpretation, defining roles and responsibilities of physiotherapists, improving the functioning of support structures, and offering competitive incentives to attract skilled education physiotherapists are reported essential for effective policy implementation.

This randomized controlled prospective trial evaluated the impact of neuro‐athletic training (NAT) on flexibility, serve speed, and upper extremity performance in elite volleyball players. Thirty professional players aged 18‐35 were initially recruited and randomly assigned to either the NAT intervention or control group. However, the study was completed with a final sample of 26 participants. Performance assessments, including the Sit and Reach Test, Pocket Radar Ball Coach for serve speed, and Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST), were conducted pre‐ and post‐intervention. The NAT group showed significant improvements in flexibility (p=0.005, d=2.32), serve speed (p<0.001, d=2.54), and upper limb performance (p<0.001, d=2.61), while the control group displayed no significant changes. These findings highlight NATʹs effectiveness in enhancing neuromuscular function and coordination, emphasizing its potential as a training approach in competitive volleyball.This randomized controlled prospective trial evaluated the impact of neuro‐athletic training (NAT) on flexibility, serve speed, and upper extremity performance in elite volleyball players. Thirty professional players aged 18‐35 were initially recruited and randomly assigned to either the NAT intervention or control group. However, the study was completed with a final sample of 26 participants. Performance assessments, including the Sit and Reach Test, Pocket Radar Ball Coach for serve speed, and Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST), were conducted pre‐ and post‐intervention. The NAT group showed significant improvements in flexibility (p=0.005, d=2.32), serve speed (p<0.001, d=2.54), and upper limb performance (p<0.001, d=2.61), while the control group displayed no significant changes. These findings highlight NATʹs effectiveness in enhancing neuromuscular function and coordination, emphasizing its potential as a training approach in competitive volleyball.

Despite being a rare disease worldwide, rabies has the highest morbidity and mortality rates, with nearly all symptomatic cases leading to coma and death. Rabies represents an infectious disease caused by the Rabies virus (RABV), which is part of the Lyssavirus group and the Rhabdoviridae family, and it mainly spreads through the bite and scratch of an infected mammal, but particularly of wild animals, such as bats, foxes, wolves and racoons, and of domestic animals, such as dogs and cats, in rabies-prone areas of the world. Airborne transmission has been deemed as extremely rare, and no clinical case as such has been recorded worldwide yet, except in the enclosed environment, such as research laboratories and caves where infected bats are present. Domestic mammals, such as dogs and ferrets, represent other important reservoirs of disease transmission, and the human cases of Asia and Africa amount approximately 95% of all human cases worldwide. Infected animals most commonly start transmitting the virus once the first symptoms have occurred, and if they experience disease aggravation and death within 10 days, a case of rabies is registered, more easily if the incidence occurred in the urban area and then, any person or animal that had been potentially exposed are strongly recommended to receive the inoculation. It is rare for asymptomatic mammals to transmit the illness. Most First-World and several Second-World countries have recently been declared dog rabies-free by the World Health Organization. The disease can only be treated prophylactically, with three doses of a vaccine containing an inactivated form of RABV, or with five doses of the vaccine and two doses of anti-RABV immunoglobulins within 28 days if the patient is believed to have been exposed to the virus beforehand. It has been projected that, once the viral load reaches elements of the central nervous system, prophylactic approaches are no longer effective, even if symptoms have not begun yet, and this highlights the urgent trait of the medical condition, strongly recommending exposed people to receive the prophylactic doses immediately after the potential exposure to the virus. The pathogen first infects the bodily fluids, before reaching the peripheral nervous system, from where it will gradually move toward the spinal cord or the encephalon, at a speed of movement ranging from 1 to 40 cm per day. It was also found, in extremely rare circumstances, to infect the nasopharyngeal cavity and the lungs. The primary cause of a successful, gradual advance of the viral load toward the point of clinical no-return for the patient - the CNS - is a complex mechanism of induced innate immune evasion, with the interferon system being heavily targeted and silenced by RABV proteins. The ‘Milwaukee’ protocol is locally believed to decrease the mortality rate of the clinical illness to approximately 80%, although significantly more research is required in this sense. First-line immune evasion represents the central mechanism developed by viruses during their evolutionary process to gain control over human immunity, so it could be the development and adjustment of a counter-offensive to this evolutionary operating system that could address the core elements of the problem. Human recombinant Type I and Type III Interferons were found to be significant vaccine adjuvants and to considerably delay the clinical onset of the disease. Despite their central role in natural immunity-based prophylaxis, vaccine support and, in often cases, vaccination per se, a local administration of IFNs as such may not be enough to tackle the core problem of the endemic disease, and a specific and systemic treatment of potential host cells with IFN I and III, as well as IFN-stimulating proteins, may constitute a major research requirement in the coming years of disease investigation, as the inoculation efforts with the inactivated virus and immunoglobulin administration continue. The administration of a relatively low dosage of somatic Natural Killer cells, gamma-interferon and perhaps, of somatic helper CD4+ and somatic cytotoxic CD8+ T-lymphocytes treated with alpha-, beta- and lambda-interferon could be merged with the administration of a similar dosage of alpha-, beta- and lambda-interferon during the efforts to develop an effective and less costly prophylactic vaccine against rabies. A combination of a nasal substance containing a low dosage of IFN I and III with a reduced concentration of neutralised RABV copies, and/or with a low dose of anti-RABV IgA antibodies, could also be tested for humans for the purposes of pre- and post-exposure prophylaxis. It is important to acknowledge that are existing clinical signs that early alpha-interferon-based therapies alone are not effective in curbing the mortality rate of the disease, which could be explained by the fact that, once symptoms begin, the virus has already reached and started distributing its load in critical areas of the host organism - the Central Nervous System (CNS) - and that the vast majority of the cases are detected after clinical symptoms start occurring. Nonetheless, researchers did find a broader window of opportunity with regards to early antiviral therapy, with methods such as RNA interference, as well as the development of the Favipiravir antiviral agent and of new targeted molecular therapies, which perhaps could be used alongside recombinant Type I, Type II and/or Type III Interferons, as well as the other mentioned innate and adaptive immune elements mentioned. Overall, there could be an application of a concept called “a United Immune System” into the practical, clinical world, that will significantly help in mounting an effective, long-term evolutionary response against advanced microbial immune evasion, changing the connection between innate and adaptive immunity from a “national road” to a “motorway”.

This study investigates the incidence of prematurity and its impact on maternal and neonatal health at Maternity Hospital, Ouargla, in 2021. Using a multidisciplinary approach, the research explores global and regional trends in prematurity, focusing on challenges and opportunities within Algeria and the broader African context. The analysis includes maternal health indicators and fetal biomarkers to identify risk factors and enable early predictive measures. Key findings discuss the physical characteristics of preterm infants, organ immaturity complications, and long-term outcomes for both mothers and newborns. Additionally, the study considers cultural and societal influences on the perception and management of prematurity and highlights advancements in prenatal and neonatal care, including early detection tools and neonatal resuscitation techniques. Emphasizing family-centered care and the transition to home-based support, the research reviews cutting-edge treatments such as pharmacological and non-pharmacological interventions, stem cell therapies, and respiratory support advancements. Socioeconomic and environmental impacts on prematurity rates are also assessed, underscoring the importance of maternal education and social support networks. The study concludes with evidence-based recommendations for healthcare providers, policymakers, and researchers to improve maternal and neonatal outcomes and reduce prematurity rates.

This is a very brief overview of the ongoing research program of unification known as the octonionic theory. We highlight the quantum foundational origins for the theory, and the seven key ingredients which go into its making.

In this article, we showcase PSL(3,4) as the automorphism group for a specific class of three linear binary codes C1, C2 and C3 with dimension 9. The demonstration involves leveraging the action of the group PSL(3,4), represented by invertible matrices of size 9 by 9 up to isomorphism, on the vector space F29. Additionally, we establish that these codes exhibit a 3−weight self-orthogonal property. All computations presented in this paper were conducted using guava package of GAP (Groups, Algorithms, Programming) a system designed for computational discrete algebra.

Biofortification is a promising, cost effective and sustainable strategy to mitigate micronutrient deficiencies and related health complications. The principal aims of the study encompassed the quantification of the carotenoid content of 147 maize inbred lines, the identification of variable regions within the ZEP1 gene, the correlation of these observed variances in the presence of this gene with carotenoid content, and to identify lines harboring the favorable alleles for the crtRB1 gene. The correlations observed among the carotenoids synthesized by specific branch of the biosynthetic pathway were both significant and positive. Utilizing gel-based genotyping, 24 lines with contrasting carotenoid profiles were selected, evaluated and sequenced. Analysis of the variation in the sequence classified these lines into distinct groups. The findings highlight that inbred lines both group 1 and group 8 exhibited significant associations with the carotenoid content of the lines. Specifically, ZEP1_7852, a discernible variation belonging to group 8, was significantly associated with zeaxanthin content and total carotenoid content. Twenty-five inbred lines with provitamin A content surpassing 15 μg/g that harbor the favorable alleles of the crtRB1 gene were identified using KASP SNP zm0016. These lines can be used as donor parents for further enhancement of provitamin A in maize.

Electronic cigarettes (e-cigs) have increased in popularity and usage over the last few decades. While e-cigs were originally introduced as a healthy alternative to cigarette smoking, recent research has demonstrated the cytotoxic effects of nicotinic e-cig devices^[1]. E-cigs cause damage on multiple cell types, including epithelial and endothelial cells, along with causing dysregulation of inflammatory pathways. This review will discuss the harmful effects of e-cigs on the human body, highlighting the physiological effects of e-cigs on pulmonary, cardiovascular, and cerebrovascular health.

Moreover, this review will highlight the potential therapeutic effects of bixin, an apocarotenoid found in the seeds of Bixa orellana, also known as the achiote tree. Bixin has innate anti-inflammatory, antioxidant, and anti-cancer activities that have been demonstrated in recent research. Nanotechnology has surfaced in the past few decades as a powerful tool for medicinal practice. Specifically, nanoparticles serve as a potential method for treating a wide variety of conditions and diseases. Bixin nanoparticles show promise as a viable method for the treatment of e-cig induced damage due to the innate properties of bixin along with the advantages of using nanoparticles compared to conventional medicinal interventions.

Infrastructure financing in least-developed countries (LDCs) faces various obstacles, including fragile economic conditions, inadequate regulatory frameworks, and concerns over transparency and investor security. This deters private sector involvement, impeding vital infrastructure development and long-term economic progress. Blockchain technology, on the other hand, has demonstrated its ability to enhance transparency and improve operational efficiency. To bridge this gap, this study presents a novel blockchain-based framework for private financing governance in such projects. The proposed framework was built on three components: a foundational blockchain layer for secure transactions, a DAO layer for governance, and a node layer for user interaction. Moreover, a decentralized decision-making structure was outlined, which allocates voting power according to roles and reputations. The framework also introduces well-defined entry and exit criteria to ensure commitment and transparency. Further, it incorporates a member-driven proposal process and a reputation-based compensation system to encourage active involvement. Finally, a real-world case study was used to assess the framework's practicality and efficiency. According to the findings, the proposed framework has the potential to address and improve the identified gaps. The results of this study provide conceptual insights for LDC infrastructure project stakeholders to enhance the efficiency of infrastructure development in these nations.

A seroepidemiological study conducted from 2020 to 2022 in Guangzhou, China, aimed to evaluate varicella antibody levels. We measured varicella antibody concentrations using an enzyme-linked immunosorbent assay (ELISA) in a cohort of 3,300 subjects. The mean varicella IgG-specific antibody level was 171.2 mIU/mL (95% CI: 158.9, 184.4), with an overall positivity rate of 67.00% (95% CI: 65.37, 68.60). Varicella antibody levels were found to be influenced by age, vaccination dosage, and history of varicella infection. Notably, antibody concentrations increased with age and the number of vaccinations. Subjects with a vaccination history maintained positive antibody levels six years post-vaccination. We recommend two doses of the varicella vaccine for both children and adults. Before incorporating the varicella vaccine into Guangzhou's routine immunization programs, a comprehensive assessment of disease burden and the economic viability of vaccination strategies is essential.

This study aimed to develop a novel nanotechnological slow-release drug delivery platform based on hyaluronic acid Microsponge (MSP) for the subcutaneous administration of methotrexate (MTX) in the treatment of rheumatoid arthritis (RA). RA is a chronic autoimmune disease characterized by joint inflammation and damage, while MTX is a common disease-modifying antirheumatic drug (DMARD), the conventional use of which is limited by adverse effects and the lack of release control. In this work, MSP were synthesized as freeze-dried powder to increase their stability and allow for a facile reconstitution prior to administration and precise MTX dosing. These MSP exhibited stable, rounded shapes, with a signature open porosity structure, and achieved a high MTX loading efficiency, with a slow release after injection. Our drug release studies demonstrated indeed an initial burst, followed by sustained release over a month. Remarkably, by means of a (collagen induced - CIA) rat model of RA, the MTX-loaded MSP almost doubled the therapeutic efficacy, by significantly reducing arthritic scores compared to controls. The pre-clinical study was replicated twice to confirm this marked improvement in performance and the safety profile of MSP. The results show the potential of MSP mediated therapy to enhance MTX efficacy in RA treatment by enabling sustained slow-release while minimizing side effects. This study therefore suggests that the MSP platform may be considered in clinical practice for improving the MTX delivery in RA therapy.

The title compound, trans-11-(3,4-dimethoxyphenyl)-2,3,8,9-tetramethoxy-6-oxo-11,12-dihydro-6H-dibenzo[c,h]chromene-12-carboxylic acid (4), was synthesized for the first time via a two-step protocol from 3,4-dimethoxyhomophthalic anhydride (1) and 3,4-dimethoxybenzaldehyde (DMBA). In the first step, 1 reacts with DMBA to give trans-3-(3,4-dimethoxyphenyl)-6,7-dimethoxy-1-oxo-3,4-dihydro-1H-2-benzopyran-4-carboxylic acid (2), which further reacts with additional two equivalents of 1 to give 4. Compound 4 was characterized by means of spectral methods - 1H-, 13C-, DEPT-135-NMR, and HRMS.

Before 1985 growth hormone (GH) was extracted from human pituitaries, and its therapeutic use was limited to children with severe GH deficiency (GHD). The availability of unlimited amount of recombinant GH (rhGH) allowed to investigate the efficacy of rhGH therapy in a number of con-ditions other than GHD. Nowadays, patients with Turner syndrome, SHOX deficiency, Noonan syndrome, Prader-Willi syndrome, idiopathic short stature, chronic kidney disease, and children born small for gestational age can be treated with rhGH in order to improve adult height. In pa-tients with Prader-Willi syndrome rhGH therapy also improves body composition and cognitive function. Large post-marketing multinational studies in a huge number of pediatric patients demonstrated a good safety profile for rhGH. Recently, long-acting formulations of rhGH have been approved and licensed for GHD, and clinical trials are ongoing for other conditions. In this paper, we will review the use of rhGH for the treatment of children with conditions other than GHD.

In order to improve the accuracy of wind tunnel test, relying on the high pressure gas source of China Aerodynamic Research and Development Center, a secondary flow standard facility based on sonic nozzle array is developed, and its pressure range is (1~6)MPa, and flow range is (0.12~5.55)kg/s. At present, most facilities use the average temperature measured by the temperature array to represent the upstream temperature of the sonic nozzle array. However, the small flow calibration test results show that, the maximum temperature difference upstream of the standard sonic nozzle array is 1.97 K, the temperature field upstream of the sonic nozzle array shows non-uniformity, so the above method cannot accurately obtain the upstream temperature. To solve this problem, each nozzle used in the standard sonic nozzle array was accurately measured by temperature sensors. And the uncertainty of the facility and the discharge coefficient of the calibrated nozzle between the two methods were compared. Results show that, compared with the discharge coefficient obtained by the tempearture sensor array of 0.9902, the accurate measurement of 0.9904 is closer to National Institute of Metrology, China(NIM) traceable result of 0.9907, and the relative uncertainty of the facility is reduced from 0.124% (k=2) to 0.120% (k=2).

Research background: The globalization processes are not only of an economic and cultural nature but also unify trends in the organization and management of administration, including at the local level. The idea of economizing administration was promoted within the broad stream of reforms described as New Public Management (NPM). This idea was often expressed as a postulate for the territorial reorganization of local governments. These reforms, motivated by the economies of scale, aimed to eliminate the allegedly overly fragmented nature of local government structures. Critics of these reforms advocate for glocalization, which takes local institutional conditions into account. They argue that the NPM concept should emphasize a long-term balance between economics, environment, and society, which are the paradigms of sustainable development (SD). According to them, this approach provides a more holistic and effective method for public management.

Multi-stage hydraulic fracturing is a key technology adopted in the energy industry to make shale gas and shale oil fields profitable. Post-frac fluid soaking before putting wells into production has been found essential for enhancing well productivity. Finding the optimum time to terminate the fluid-soaking process is an open problem to solve. Post-frac shut-in pressure data from six wells in two shale gas fields were investigated in this study based on Pressure Transient Analysis (PTA) to reveal fluid-soaking performance. It was found that pressure derivative data become scattering after 1 day of well shut in. The overall trend of pressure derivative data after the first day of well shut-in should reflect the effectiveness of fluid-soaking. Two wells exhibited flat (zero-slope) pressure derivatives within one week of fluid-soaking, indicating adequate time of fluid-soaking. Four wells exhibited increasing pressure derivatives within one week of fluid-soaking, indicating inadequate time of fluid-soaking. This observation is consistent with reported well’s Estimated Ultimate Recovery (EUR). This study presents a new approach to assessment of post-frac fluid-soaking performance with real-time shut-in pressure data.

An eco-friendly plant-mediated approach was employed for the synthesis of green zinc oxide nanoparticles (ZnO NPs) and silver-doped zinc oxide nanoparticles (Ag-doped ZnO NPs), using Artemisia Absinthium leaf extract as a reducing and capping agent. Pure ZnO NPs were also synthesized without the leaf extract, and a comparative study between them was conducted. The nanoparticles (NPs) were characterized using Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), morphological scanning electron microscopy/energy dispersive spectrum (SEM/EDX), and surface area (BET) analysis, and antioxidant and antibacterial activity measurements were conducted. The FTIR analysis showed the absorption peak of the Zn–O bond between 400 and 450 cm−1. The XRD analysis revealed that the ZnO NPs had a hexagonal wurtzite structure with a decrease in particle size from 36.32 to 27.40 to 25.13 nm, and an increase in the BET from 4.003 to 6.032 to 12.151 m2/g for the pure, green, and Ag-doped ZnO NPs, respectively. The field emission scanning electron microscopy (FESEM) imaging revealed the presence of the ZnO NPs. The EDX results showed the zinc and oxygen composition exhibited strong energy signals of 71.28% and 18.12% for zinc and oxygen, respectively. The various characterization techniques used confirmed the formation of the ZnO NPs. The free radical scavenging activities (RSِA%) measured using the 1,1-diphenyl-2-picrylhydrazyl free radical (DPPH) assay at different concentrations and times for the pure, green, and Ag-doped ZnO NPs, and ascorbic acid were 25.50, 29.27, 28.56, and 56.7, respectively. The antibacterial activity of the synthesized samples was tested against three types of bacteria, Staphylococcus aureus ATCC 6538, Bacillus subtilis ATCC 6633, and Pseudomonas aeruginosa ATCC 9027, at different levels (5, 10, 20, and 30 µL), using the hole plate diffusion method. The Ag/ZnO NPs showed more enhanced antibacterial activity than the green ZnO NPs, whereas the pure ZnO NPs showed no antibacterial activity under the same conditions. Comparatively, the antibacterial activity of the Ag-doped ZnO NPs against the test bacteria was found to be higher than that of a commercial gentamycin antibiotic. Ultimately, the present investigation has clearly shown that the differences in the ZnO NPs’ sizes and surface-area-to-volume ratios are responsible for their stronger antibacterial activity.

The emergence of antibiotic-resistant Acinetobacter baumannii (A. baumannii) is a pressing threat in clinical settings. As the last line of defense, colistin is currently widely used to treat multidrug-resistant A. baumannii. Unfortunately colistin-resistant phenotypes in A.baumannii isolates have also been reported, highlighting the desperate need to develop alternative medications to address these serious bugs. To resist colistin, A. baumannii has developed several mechanisms. These include the loss of outer membrane lipopolysaccharide (LPS) due to mutation of LPS biosynthetic genes, modification of lipid A (a constituent of LPS) structure through the addition of phosphoethanolamine (PEtN) moieties to the lipid A component by overexpression of chromosomal pmrCAB operon genes and eptA gene, or acquisition plasmid-encoded mcr genes through horizontal gene transfer. Other resistance mechanisms involve alterations of outer membrane permeability through porins, expulsion of colistin by efflux pumps and heteroresistance. In response to the rising threat of colistin-resistant A. baumannii, researchers have developed various treatment strategies, including antibiotic combination therapy, adjuvants to potentiate antibiotic activity, repurposing existing drugs, antimicrobial peptides, nanotechnology, photodynamic therapy, CRISPR/Cas and phage therapy. While many of these strategies have shown promise in vitro and in vivo, further clinical trials are necessary to ensure their efficacy and widen their clinical applications. Ongoing research is crucial to identify optimal therapeutic strategies for managing colistin-resistant A. baumannii. This review explores the genetic mechanisms underlying colistin resistance and assesses potential treatment options for this challenging pathogen.

Dominant mutations in COL3A1 are known to cause vascular Ehlers-Danlos syndrome (vEDS) by impairing extracellular matrix (ECM) homeostasis. This disruption leads to the fragility of soft connective tissues and a significantly increased risk of life-threatening arterial and organ ruptures. Currently, treatments for vEDS are primarily symptomatic, largely due to a limited understanding of its underlying pathobiology and molecular mechanisms. In this study, we conducted a com-prehensive analysis of the intracellular proteome of vEDS fibroblasts, integrating these findings with our previous transcriptome results to identify key molecular pathways that drive the disease. Additionally, we explored the therapeutic potential of inhibiting miR-29b-3p as a proof of concept. Our integrative multi-omics analysis revealed complex pathological networks, emphasizing the critical role of miRNAs, particularly miR-29b-3p, in impairing ECM organization, autophagy, and cellular stress responses, all of which contribute to the pathogenesis of vEDS. Notably. the inhi-bition of miR-29b-3p in vEDS fibroblasts resulted in the upregulation of several differentially expressed target genes involved in these critical processes, as well as increased protein expression of essential ECM components, such as collagen types V and I. These changes suggest potential therapeutic benefits aimed at improving ECM integrity and restoring intracellular homeostasis. Overall, these insights not only enhance our understanding of the complex biological mechanisms underlying vEDS but also provide a solid foundation for future research focused on developing targeted treatment strategies to address this life-threatening disorder effectively.

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, which have significantly impacted performance gains and are critical to commercializing SSB technology. The findings provide actionable insights for researchers and industry stakeholders, specifically in the areas of interfacial engineering and manufacturability, where gaps in long-term stability and scalable production continue to hinder widespread adoption of SSBs. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity, providing a roadmap for targeted research efforts and strategic investments to advance SSB technology for critical applications in transportation and storage. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency.

In Norway, 78% of publicly funded general practitioners (GPs) are self-employed, remunerated through a mix of capitation and fee-for-service, while 22 % are salaried. GPs register consultations and procedures using nationally standardized fees for health services, which allows for variations in earnings. Our study aims to examine whether high and low earning GPs also differ in their earnings per list patient and, if so, identify the specific fees that contribute to this difference. Additionally, we aim to compare practice characteristics between high and low earning GPs. We analyse national registry data from 2021 for all GPs with at least 100 working days (N=2546) and calculate annual earnings from capitation and fees. High earning GPs (upper quartile) have 55% more listed patients than low earning GPs (lower quartile), yet they earn 40% more from fees per patient (average €178 versus €127). Nearly half of the earnings difference per patient can be attributed to high earning GPs more frequently conducting consultations with patients. Still, patients of high earning GPs receive less minutes in consultations yearly (average 35 versus 38 min), due to high earners’ shorter consultation duration (average 15 versus 20 min). The remaining earning difference comes from higher utilization of procedure fees among high earners (average 5.6 versus 3.9 procedures per patient). Prolonged consultation, medication review, and talking therapy are some of the fees that contribute most to this difference. The findings highlight that considerable earning variations are linked to fee utilization and practice styles among GPs.

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we construct a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Lévy process to S\&P 500 option prices, providing a more comprehensive measure of volatility that reflects the extreme movements and heavy tails observed in financial data. Using an axiomatic approach, we introduce a general family of risk-reward ratios, computed with our revised VIX and fitted over a fractional time series to more accurately identify uncertainty shocks in financial markets.

Objective: To evaluate the affective responses to running with imposed velocity or self-selected time trials in runners, as well as the effects on volume load (VL) and training impulse (TRIMP). Anxiety was also determined. We establish the level of association between the dependent variables. Methods: Three visits were carried out. 1st visit consisted of a maximum running effort test (VO₂Max). 2nd and 3rd visits, participants were divided between the time limit (T_Lim) or time trial 1000m running at self-selected intensity (T₁₀₀₀). Participant responded to felt arousal, feeling and anxiety SUDS scale, before, during and after T_Lim and T₁₀₀₀. Results: T_Lim vs. T₁₀₀₀ (p<0.001), V_Peak x V₁₀₀₀ (p=0.013), showed differences, but did not influence VL_TLim vs. VL₁₀₀₀ (3181.34 ± 872.22 vs. 3570.60 ± 323.3; p=0.062). TRIMP showed no differences (p=0.068). Arousal did not differ between the pre-exercise (p=0.772) and post-exercise (p=0.083) groups, but was different during (p=0.035). There were differences between groups in the pre-exercise (p=0.012), during (p<0.001) and post-exercise (p<0.001) for feeling and anxiety scores. The correlation between TRIMP and affective scores showed association for T_Lim (r=0.46; p=0.043). Conclusion: The self-selected exercise generated positive affective responses, but the same did not occur for the imposed T_Lim. VL and TRIMP presented equality. There was association TRIMP and T_Lim feeling scale. T_Lim significantly increased anxiety scores.

Tracheotomy as an emergency surgical procedure appeared and developed in ancient times1. The surgical techniques for its performance in our time have reached a level of perfection, and in adults it is one of the most frequent surgical procedures performed in emergency and intensive care units worldwide. Children's tracheotomy, on the other hand, presents far more serious challenges to the medical teams involved in its performance. A number of issues related to it - indications and contraindications, time for performance and protocols for decannulation and others are still subject to clarification. In the past years, the development of medical science, the introduction of vaccination programs, the use of many improved medical materials and technologies, as well as the achievements of neonatal and pediatric intensive care practice, have shifted the emphasis of pediatric tracheotomy from its emergency performance to solve an acute asphyxiation problem, to its implementation in children, representing a complex group of patients, with permanent dependence on tracheostomy and related medical technologies for long-term survival2. There are a growing number of children who have experienced complex therapy, for whom tracheostomy care and mechanical ventilation2, including at home environment, are part of their care. This, in turn, puts new questions on the agenda: who will take care for the child in the early postoperative period and during the days of hospitalization afterwards, who will train the people who will care for the tracheotomized child at home, what precautions should be applied in these children during their daily activities – eating, bathing, playing inside and outside the home and others.

Advanced triple-negative breast cancer (TNBC) has poorer outcomes due to its aggressive behaviour and limited treatment options. While therapies like checkpoint inhibitors and PARP inhibitors (PARPi) offer some benefit, chemotherapy remains ineffective beyond the first line of treatment. Antibody-drug conjugates (ADCs) like sacituzumab govitecan-hziy (SG) represent a significant advancement. SG combines SN-38, an irinotecan derivative, with a TROP-2-targeting antibody via a pH-sensitive linking moiety, achieving an good drug:antibody ratio. Infused on Days 1 and 8 over 21 days, SG utilizes a moderately stable linker to ensure effective tumor targeting while minimizing side effects like diarrhea. By delivering SN-38 into the tumor niche, it induces double-stranded DNA damage, promoting cancer apoptosis. In a phase I-II study involving 108 mTNBC patients, SG achieved an overall response rate (ORR) of 33.3% and a median response period of 7.7 months. Common side effects included neutropenia, nausea, and fatigue. This review highlights clinical potential, pharmacokinetics, safety profile, resistance mechanisms of SG along with key ongoing clinical trials, emphasizing its role in managing refractory mTNBC, especially in third-line therapy.

The paper presents a theoretical study on the dynamic behavior of a novel single-rotor system with planetary speed increaser and counter-rotating direct current (DC) generator, patented by authors, during transient stage from rest. The analytical dynamic model of the mechanical subsystem is based on the decomposition of the wind system into the component rigid bodies, followed by the description of their dynamic equations by using the Newton-Euler method. To these dynamic equations are added the linear mechanical characteristics of the DC generator and the wind rotor. The system of equations allows establishing the analytical equation of motion of the wind system and implicitly the time variation of the mechanical power parameters. Numerical simulations of the obtained analytical dynamic model were performed in MATLAB-Simulink in start-up mode from rest for the case study of a 100 kW wind turbine. These results allowed highlighting the time variation of the kinematic parameters (angular velocities and accelerations), torques and powers for all system shafts, both in transient regime and steady-state. The implementation in this case of the counter-rotating generator indicates a 6.4% contribution of the mobile stator to the generator total power. The analytical and numerical results obtained are useful to researchers, designers and developers of wind turbines aiming to optimize their construction and functionality through virtual prototyping.

(1) Background: Notio calculates coefficient of drag area (CDA) considering dynamic pressure data calculated by an integrated Pitot-static tube. Therefore, this study aimed to evaluate the validity and reliability of Pitot-static tube calculations through wind speed (WS) data, against a hot-wire anemometer (HWA). (2) A total of 60 recordings were made, lasting 30 seconds each, in a closed-circuit wind tunnel at 4 different WS (≈30 to ≈60km/h), and at five different yaw angles (0º to 20º). (3) Results: Notio showed WS 6.44% higher than HWA. The calibration process recommended by the Notio manufacturer reduced the differences to a non-significant 0.76%. Comparison of the WS of Notio calibrated and HWA only showed significant differences in the WS group of ≈60 km/h. There were no significant differences in the comparison of yaw angles groups. The reliability of Notio was worse than that of the HWA. (4) Conclusions: Notio calibrated at a speed close to its use, allows reliable and accurate calculation of WS over a wide range of yaw angles under controlled wind tunnel conditions without the presence of a cyclist and bike. However, due to the influence of WS on aerodynamic drag, small errors in WS could translate into considerable values of CDA for cycling performance.

In today's rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This study undertakes a comparative analysis between traditional natural language processing (NLP) models, like BERT, and advanced large language models (LLMs), specifically GPT-4 Omni and GPT-4o Mini, both pre- and post-fine-tuning with few-shot learning. By leveraging an extensive dataset of hotel reviews, we evaluate the effectiveness of these models in predicting star ratings based on review content. The findings demonstrate that the GPT-4 Omni family significantly outperforms the BERT model, achieving an accuracy of 67%, compared to BERT's 60.6%. GPT-4o, in particular, excelled in accuracy and contextual understanding, showcasing the superiority of advanced LLMs over traditional NLP methods. This research underscores the potential of using sophisticated review evaluation systems in the hospitality industry and positions GPT-4o as a transformative tool for sentiment analysis. It marks a new era in automating and interpreting customer feedback with unprecedented precision.

An essential role in proper homeostasis of glucose is played by kidneys where glucose tranport is carried out across cell membranes by two families of glucose tansporters - facilitated diffusion glucose transporters (GLUTs) and Na (+) -dependent glucose co-transporters (SGLT family). Among transporters, sodium-dependent glucose co-transporters have a major role in the kidney ‘s ability to reabsorb glucose. Although localization of glucose transporters has been extensively studied in mammals there are still gaps in knowledge of localization of SGLTs in birds. The aim of the reasarch was to study comparatively immunolocalization of sodium-dependent glucose co-transporters SGLT1 and SGLT2 in kidneys of healthy and T-2 mycotoxicated chickens. Immunohistochemical staining was carried out using polyclonal primary antibodies SGLT1 and SGLT2 (Abcam, UK) on kidney tissue derived from three healthy and three T-2 mycotoxicated 7 days old female layer-type Ross chickens (Gallus gallus domesticus). Sections were stained using Immunohistochemistry kit (Abcam, UK). In kidneys of healthy birds strong staining of SGLT1 and SGLT2 was noted in cytoplasm of epithelial cells of the proximal straight and convoluted tubules. In T-2 toxin group birds' kidneys’ weak expression of SGLT1 and SGLT2 with morphological changes occurred, indicating to reduced glucose transport in urinary system during T-2 mycotoxicosis.

Background: On 1 January 2024, the Noto Peninsula was struck by a 7.6 magnitude earthquake. In the previous article, The food provided in the evacuee shelters proved insufficient in energy and protein for the older adults, 65% of whom visited our emergency clinic. Methods: (1) analyze the amino acid and fatty acid composition of 11 evacuation meals using the "Duplicated Combination" Model, (2) assess the adequacy of the amino acid and fatty acid composition, the digestible essential amino acid score (DIAAS) was calculated and analyzed in relation to the half-life determined by the N-terminal amino acid of the proteins.. Linoleic acid (LA) and alpha-linolenic acid (ALA) levels were compared with EFSA recommendations, (3) The national survey of emergency food stocks in hospitals and social care institutions conducted in Jan, 2024 was analyzed. Results: (1) DIAAS was less than 1.00 for all 11 foods provided and was considered inadequate, (2) the half-life of the protein, whose N-terminal valine has a half-life of 100 hours, must be considered a possible deficiency when living in a shelter for more than a week, (3) LA and ALA levels were less than 40% of EFSA recommended, (4) the nationwide survey found that 80% of people have a 3-day supply and are likely to be energy and protein deficient. Conclusion: Analysis of foods in evacuation shelters after the Noto Peninsula earthquake revealed that the quality of amino acids and fatty acids seem inadequate according to DIAAS and EFSA recommendations. The “Duplicated Combination” Model might be useful to investigate the quality of foods in the shelters.

Rationalization of irrigation water, as global warming, has negatively affected the availability of irrigation water. The field experiment was conducted for the purpose of studying the effect of the type of dripper and the salinity of irrigation water rotation on the bulk density and maize growth and yield using two types of drippers (turbo and spiral) and two levels of irrigation water with different salinity ratios (low, symbolized by L) and (high and symbolized by H). Irrigation water was added in three rotations (L, H), (H, L, H), and (L, H, L). The results showed an increase in bulk density values, giving the highest value for the spiral type compared to the turbo type. Our results indicated that water quality had a significant effect on soil bulk density values, with the highest bulk density values reaching for the (L, H), (H, L, H), (L, H, L), respectively. The results showed an increase in plant height, leaf area, and yield when using the (L, H, L), compared to (L, H) and (H, L, H), respectively. This study demonstrated the possibility of using high-salinity water, which is available in southern Iraq in rotation with fresh water to irrigate the maize crop.

Natural Language Understanding (NLU) includes temporal text understanding, which can be complex and encompasses temporal common sense understanding. There are many challenges in comprehending common sense within a text. Currently, there are a limited number of datasets containing temporal common sense in English, and there is an absence of such datasets specifically for the Arabic language. In this study, an Arabic dataset was constructed based on an available English dataset. This dataset is considered a valuable resource for the Arabic community. Consequently, different multilingual pretrained language models (PLMs) were applied to both the English and new Arabic datasets. Based on this, the effectiveness of these models in Arabic and English is compared and discussed. After analyzing the errors, a new categorization of errors was proposed. Finally, the ability of the PLMs to understand the input text and predict temporal features was evaluated. Through this detailed categorization of errors and classification of temporal elements, this study establishes a comprehensive framework aimed at clarifying the specific challenges encountered by PLMs in temporal common sense understanding (TCU). This methodology underscores the urgent need for further research on PLMs’ capabilities for TCU tasks.

Background/Objectives Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition in their best hearing aid condition, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) based noise reduction on cochlear implant evaluation. Methods Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratio (SNR) through one loudspeaker. Sentence recognition scores were measured for 10 (5-bilateral, 5-unilateral) hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. Results The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. This improvement might make it difficult for some patients to achieve the

The human microbiota constitutes a very complex ecosystem of microorganisms inhabiting both the inside and outside of our bodies, in which health maintenance and disease modification are the main regulatory features. The recent explosion of microbiome research has begun to detail its important role in neurological health, particularly concerning cerebral small vessel disease (CSVD), a disorder associated with cognitive decline and vascular dementia. This narrative review represents state-of-the-art knowledge of the intimate, complex interplay between microbiota and brain health through the gut-brain axis (GBA) and the emerging role of the glymphatic system dysfunction (glymphopathy) and circulating cell-derived microparticles (MPs) as mediators of these interactions. We discuss how microbial dysbiosis promotes neuroinflammation, vascular dysfunction, and impaired waste clearance in the brain which are critical factors in the pathogenesis of CSVD. Further, we discuss lifestyle factors that shape the microbiota composition and functionality, focusing on sleep as a modifiable risk factor in neurological disorders. This narrative review places recent microbiome research into a neuroscientific and vascular perspective to establish future therapeutic avenues in targeting the microbiota to improve brain health and reduce the burden of CSVD.

Calcium (Ca) and phosphorus (P) are critical nutrients and essential elements for the growth and development of humans. Since proper diagnosis and treatment are indispensable parts of personalized and precision medicine in patients with chronic diseases, in addition to unknown about the role of Ca/P ratios, the main aim of this cross-sectional study was to assess serum and urine Ca and P levels, the Ca/P ratios and their association with health and nutritional biomarkers in children and adolescents with chronic diseases. We carried out anthropometric, body composition, clinical evaluation, biochemical analysis, dietary survey, and bone densitometry methods. In 39 patients (2-19 years, 61.5% women, 56.4% adolescents), results showed that 20% of subjects had hypercalciuria, 23% had high Ca/Cr rate, and 44% exhibited hyperphosphaturia. No one patient was hypocalcemic or hyperphosphatemic. Serum and urinary Ca/P ratios were 2.12 and 0.13, respectively. 36% and 5% of subjects had hypophosphatemia and hypercalcemia, respectively. Serum and urine Ca and P levels were associated with several nutritional parameters studied. This study confirms the need to investigate Ca, P, and their relationship in urine in childhood.

Haug and Tatum [1, 2] have recently outlined a possible path to solving the Hubbletension within Rh = ct cosmology models using a trial-and-error algorithm for redshift scaling, 1 specifically z = (R_h/R_t) − 1 and z = (R_h/R_t)^{1/2}-1 . Their algorithm demonstrates that one canstart with the measured CMB temperature and a rough estimate of H_0. Based on this approach, they nearly perfectly match the entire distance ladder of observed supernovae by identifying a single value for H_0. However, their solution is based on a simple numerical search procedure, which, although it can be completed in a fraction of a second on a standard computer, is not a formal mathematical proof for resolving the Hubble tension.Here, we will demonstrate that the trial-and-error numerical method is not necessary and that the Hubble tension can be resolved using the same Haug and Tatum type R_h = ct model through a closed-form mathematical solution. Furthermore, we will prove that this solution is valid for a much more general case of any cosmological redshift scaling consistent with: z = (R)h/R)t)^x − 1. Haug and Tatum have only considered the most common assumptions of x = 1 and x = 1/2 .Our solution involves simply solving an equation to determine the correct value of H0. This is possible because an exact mathematical relation between H_0 and the CMB temperature has recently been established, in combination with the linearity in an R_h = ct model. We also demonstrate that a thermodynamic form of the Friedmann equation is consistent with a wide range of redshift scalings, namely: z = (R_h/R_t)^x − 1.

The paper presents a study on optimizing leather defect detection using the YOLOv11 model. Traditional leather processing has faced challenges in quality control, where human inspection accuracy ranges between 70% to 85%, affecting leather utilization rates and resulting in significant material waste. This research introduces an automated solution to improve defect classification and detection accuracy. The study focuses on defects commonly found in leather, such as insect larvae damage and removal cuts, using a specialized light chamber to control environmental variables. By analyzing both grain and flesh sides of the leather, the researchers demonstrated a significant increase in defect detection accuracy, with the flesh side achieving a higher classification rate. YOLOv11's dual-side analysis allowed for clearer identification of subtle defects, leading to a more efficient classification process. The results suggest that integrating advanced AI models like YOLOv11 with controlled digitization environments could drastically reduce human error and improve leather utilization, providing a scalable solution for the leather industry.

The design and control of lower limb rehabilitation robots for patients after a stroke has gained significant attention. This paper presents the dynamic analysis and control of a 3-degree-of-freedom lower limb rehabilitation robot using combined position-force control based on the force feed-forward and compensate gravity proportional derivative method. In the lower limb rehabilitation robot, the interaction force between the patient with the joints and links of the robot is uncertain and nonlinear due to the disturbance effect of Coriolis force, centrifugal force, gravitational force, and friction force. During recovery stages, forces exerted by the patient's lower limb are considered disturbances. Therefore, to meet the quality requirements in using the rehabilitation robot with different recovery stages of patient training, combining position control and force control is essential. In this paper, we proposed a combinations of proportional-derivative gravity compensation motion control and force feed-forward control to form an advanced combined controller (Position-Force Feedforward Control - PFFC) for a 3 DOF lower limb functional rehabilitation robot. The forces can be sensed using a 3-axis force sensor. In addition, the robot's position parameters are also measured by encoders. The control algorithm is implemented on the STM32F4 Discovery board. Verified test of the proposed control method is shown in the experiments and shown good performance of the system.

Glaucoma, the second leading cause of irreversible blindness globally, encompasses a heterogeneous group of ocular disorders characterized by the progressive degeneration of retinal ganglion cells. Pharmacotherapy remains the cornerstone of treatment, primarily aimed at reducing intraocular pressure (IOP) by decreasing aqueous humor production or enhancing its outflow. The therapeutic classes employed include carbonic anhydrase inhibitors, β-blockers, α-adrenergic agonists, prostaglandin analogs, parasympathomimetics, Rho kinase inhibitors, and hyperosmotic agents. Despite their efficacy, these medications are associated with a range of ocular and systemic side effects, influenced by their mechanisms of action, formulation, and dosage. Ocular adverse effects, such as irritation, dry eye, allergic reactions, and infections, are common, while systemic absorption may lead to more severe outcomes, including organ dysfunction, exacerbation of comorbid conditions, or life-threatening cardiovascular events. Given these potential risks, it is critical for clinicians to understand and monitor these adverse effects, as they significantly affect patient adherence, quality of life, and treatment outcomes. Ongoing research is essential to develop novel therapeutic regimens, agents, or delivery methods that minimize side effects and improve compliance. Incorporating patient-reported outcomes in clinical practice may further enhance the assessment of treatment impact, facilitating more tailored and effective management of glaucoma.

Kinship verification using facial images has become an important area of research within computer vision. This paper presents a robust kinship verification system that leverages Histograms of a Two-Dimensional Discrete Wavelet Transform (Hist-2D-DWT) to improve recognition accuracy. The system extracts planar features by decomposing images into four coefficients—approximation, horizontal, vertical, and diagonal—via discrete wavelet transform. Comprehensive experiments conducted on the TSKinFace and Cornell KinFace datasets validate the method’s effectiveness, achieving accuracies of 93.46% and 89.61%, respectively.

Skin cancer is one of the most common and potentially deadly forms of cancer, making early and accurate detection crucial for effective treatment. In this context, leveraging advanced machine learning techniques can significantly enhance diagnostic accuracy and efficiency. This paper presents an intelligent system for monitoring skin cancer, leveraging a deep feature extraction approach based on Convolutional Neural Networks (CNNs) combined with a Random Forest (RF) classifier. The system processes skin cancer images to extract features using three pre-trained CNN models: ResNet18, Darknet19, and MobileNetv2, which are then used to train an RF classifier for accurate skin cancer detection. The proposed model is designed for edge deployment, making it suitable for real-time applications. Experiments were conducted using the "Skin Cancer: Malignant vs. Benign" dataset, specifically curated for skin cancer classification. The results demonstrate the effectiveness of the proposed system, achieving an accuracy of 82.85%, with a precision of 82.69%, a sensitivity of 82.86%, a specificity of 82.86%, and an F1-Score of 82.75%.

Based on the fact that the solar system will eventually be destroyed and that humans cannot travel to the nearest habitable exoplanet within a lifetime, this article suggests sending synthetic biology- and AI- created astronauts with some plant characteristics to the closest habitable exoplanet. The paper assesses the potential impact of this future technology on various aspects of human society, including scientific, ethical, and cultural dimensions. As long as these astronauts with some plant characteristics possess human consciousness, it is irrelevant whether they are hybrids of carbon- and silicon- based life forms. They will carry on the human heritage on exoplanets. Humanity has two survival options. The first option is the Martian migration program led by Elon Reeve Musk, which is both visible and realistic. It could provide an immediate option for human continuation and expansion, although it may not help us escape the future burning of the Sun during its red giant phase. The second option involves directly sending astronauts with some plant characteristics beyond the solar system. Escaping to Mars still does not free humans from the risk of extinction. We must strive with all our might; otherwise, humanity will be forever trapped within the solar system, awaiting the inevitable helium flash to come.

Classic formulae for entropy and cross entropy contain operations 0x and log2 x that are 1 not defined on all inputs. This can lead to calculations with problematic subexpressions such as 2 0 log2 0 and uncertainties in large scale calculations; partiality also introduces complications in logical 3 analysis. Instead of adding conventions, or splitting formulae into cases, we create a new algebra 4 of real numbers with two symbols ±∞, for signed infinite values, and a symbol named ⊥ for the 5 undefined. In this resulting arithmetic, entropy, cross-entropy, Kullback-Leibler divergence, and 6 Shannon divergence can be expressed without any further conventions concerning. The algebra may 7 form a basis for probability theory more generally.

Background: The prevalence of multidrug-resistant Acinetobacter baumannii is gradually increasing globally, posing a serious burden on patients and society, which is a significant public health issue.Testing the homology of multidrug-resistant Acinetobacter baumannii clones in hospitals in Xinjiang autonomous region can enable the hospital infection control department to better monitor the source and prevent the spread of drug-resistant characteristics. Method: Multidrug resistant Acinetobacter baumannii detected in intensive care units of 9 urban hospitals in Xinjiang autonomous region were collected. After drug sensitivity testing, DNA was extracted, and homology analysis was performed. Conclusion: 72 strains of carbapenem resistant Acinetobacter baumannii were multi drug resistant; The resistance rate of this bacterium to polymyxin B (1.3%) and tigecycline (1.7%) was low. The genotype of type 3 was unique to Urumqi; Type 4 is mainly distributed in Changji and Hotan, showing a unique significant band; Type 2 appeared in Urumqi and Hotan, and type 1 was prevalent throughout Xinjiang. It is distributed in all regions, and the similarity coefficient is 99%.

The interactive knowledge platform, FaST, can be used by general public, various government and non-government agencies, and businesses for the advancement of the local economy in the community, creating jobs for community members, increasing income potentials, and thus alleviating poverty; and provide enhanced educational opportunities, and can also indirectly benefit to improve health and other prospects.

Old growth forests are increasingly rare but important carbon sinks which harbour rich biodiversity. Chronic browsing by the white-tailed deer (Odocoileus virginianus) is a major problem in northern temperate forests where deer numbers have increased in recent decades driven by stricter hunting rules and reduced predation necessitating local monitoring of vegetation responses. The objective of this study was to determine the effects of deer exclusion on tree regeneration dynamics and soil nutrient cycling in an old growth Carolinian forest. This was done using exclusion fencing and tip-up mounds at McMaster Forest Nature Preserve and the Sheelah Dunn Dooley Nature Sanctuary in Hamilton Ontario. Tree regeneration was surveyed from thirty 1m x 1m quadrats within exclusion plots and another thirty quadrats from deer browsed areas adjacent to the exclusion plots. Soil samples were taken from each quadrat to analyze browsing impacts on nitrate, phosphate and soil organic matter. Red oak (Quercus rubra) was planted at the top and base of tip-up mounds of varying heights and widths and monitored for deer access and browsing activity. Results show a significantly higher density of woody plants within exclosures compared to non-exclosures (p=0.0089) and twice more abundance of highly palatable species within the exclosures. However, species richness (p > 0.05) and diversity (p > 0.05) were minimally impacted by deer browsing showing a resilient old growth forest. Soil nitrate was consistently higher in the non-exclosures while phosphate was consistently higher within deer exclosures. Finally, more seedlings survived at the top of mounds than the bases showing the potential of tip-up mounds to be a natural method of deer exclusion and a critical avenue for restoring over-browsed forests.

Background: Treatment outcomes are vital measures of the success of TB treatment programs, especially in areas with limited re-sources, as tuberculosis continues to be a major public health issue. The study aimed to evaluate clinical data from TB-infected pa-tients' clinic files in rural areas of the Olivier Reginald Tambo District Municipality, Eastern Cape, South Africa, to understand the treatment outcomes of tuberculosis in patients with drug-resistant tuberculosis, human immunodeficiency virus, and nutritional status. Methods: The study analyzed 360 patient files to investigate the treatment outcomes of patients with drug-resistant tuberculosis (DR-TB) and HIV, as well as a body mass index (BMI) indicating moderate to severe classification. The data included patient de-mographics, clinical characteristics, and social history. The study also detailed and analyzed culture conversion, time in treatment, and treatment outcomes. Additionally, BMI data were presented using a scatter plot to display the distribution of BMI across different age groups and genders to identify patterns and trends in obesity prevalence. Results: The study demonstrated the high rates of both overweight and underweight individuals among middle-aged and elderly people, emphasizing the need for personalized nutritional interventions. It found that Type 1 (RR-TB) and Type 2 (MDR-TB) were the most common forms of DR-TB, with a higher prevalence of HIV-positive individuals in Type 1 (RR-TB). The study also showed the distribution of different BMI categories among DR-TB types and HIV statuses. Overall, it suggests limited connections between DR-TB type, BMI category, and HIV status in the research. Conclusion: It is imperative to prioritize the management of co-morbid conditions in DR-TB patients, as these conditions signifi-cantly influence treatment outcomes. Targeted interventions are essential for patients with severe or complex co-morbidities.

In its broadest meaning, salutogenesis denotes an orientation toward the origins and assets for positive health, as opposed to the origins and risk factors associated with disease (which is termed pathogenesis). While salutogenesis continues to inspire health promotion, it has been noted that qualitative studies can further its understanding and broaden its use in research, clinical practice, and policies. The present study is based on an analysis of more than 7.5 hours of structured and unstructured recorded interviews with six refugee musicians. Applying interpretive phenomenological analysis, we comparatively explored the factors contributing to persistence in music between participants. Five factors were found: a) long-term participation and identity as musicians; b) desirable effects of music performance; c) competence, talent, and social recognition; d) management of social and cultural differences; and e) locally organized refugee events. The links between music performance and salutogenesis include: 1) inclusion of salutogenic orientation (in addition to pathogenic orientation) toward health; 2) that music invigorated generalized resistance resources so that their expression was augmented, steady, and enduring; and 3) that a high sense of coherence was demonstrated. Music had a major role in fostering sense of coherence and promoting health and well-being.

Accurate information on ecotourism site suitability is crucial for effective planning, aiding in ecological conservation and local economic uplift. This study addresses the lack of documented ecotourism site suitability in the Lake Tana watershed of Ethiopia by evaluating factors such as elevation, slope, vegetation density, and proximity to water and infrastructure using Geographical Information System (GIS) and multi-criteria evaluation methods. The Analytical Hierarchy Process (AHP) combined with GIS was used to create a resource inventory and establish sustainable tourism criteria. A suitability map was developed based on weights from pair-wise comparisons of the selected criteria. Results show that 20% of the area, particularly near Lake Tana and water bodies, is highly suitable for ecotourism. Additionally, 50% is moderately suitable, 3.1% is marginally suitable, and 26% is unsuitable for development. These findings can guide resilient policies for ecotourism initiatives and help assess sustainable tourism in other regions. Regional tourism offices, Non-governmental Organizations, and policymakers can use this data to evaluate natural resource suitability for ecotourism, providing a foundation for further research in the field.

Artificial Intelligence (AI) faces several mathematical challenges, such as optimization, generalization, interpretability of models, and phase transitions. These problems significantly limit the application of AI in critical areas such as medicine, autonomous systems, and finance. This paper examines the main mathematical problems of AI and proposes solutions based on the use of the universality of the Riemann zeta function.

This study introduces a new approach to monitoring avian diversity and vocal behavior, as well as assessing their responses to environmental factors using IoT-based sensors. We utilized BirdNet, a deep learning model for identifying bird species by their vocalizations, to data from our MINTS-AI environmental sensors deployed across Dallas, Texas. The study investigates how ambient temperature, humidity, light intensity, and particulate matter concentrations affect bird behavior. The results show a positive correlation between bird diversity and temperature during winter, with a Pearson coefficients of 0.65 (2023) and 0.80 (2024), and R2 values of 0.43 and 0.64, respectively. In contrast, during summer, a negative correlation was found with Pearson coefficients of -0.63 (2023) and -0.34 (2024), with corresponding R2 values of 0.40 and 0.11. Machine learning models further highlighted species such as the Northern Mockingbird and Mississippi Kite as particularly sensitive to temperature changes. Humidity analysis revealed significant correlations of vocal activity for species like the Canada Goose and House Finch, indicating that vocal activity may depend on moisture levels. Light intensity also showed strong influences on species such as the Northern Mockingbird and Scissor-tailed Flycatcher, linking variations in light to vocal behavior. However, no significant relationship was found between particulate matter (PM2.5) concentrations and bird behavior. This methodology offers a new way to explore the effects of climate change on bird behavior over time, given adequate long-term data. These findings emphasize the important role of environmental changes in shaping bird populations and their ecological interactions.

Combining genomics with digital healthcare information is set to transform per- sonalized medicine. However, this integration is challenging due to the differing nature of the data modalities. The large size of the genome makes it impossible to store it as part of the standard electronic health record (EHR) system. Rep- resenting the genome as a condensed representation containing biomarkers and usable features is required to make the genome interoperable with EHR data. This systematic review examines both conventional and state-of-the-art methods for genome language modeling (GLM), which involves representing and extract- ing features from genomic sequences. Feature extraction is an essential step for applying machine learning (ML) models to large genomic datasets, especially within integrated workflows. We first provide a step-by-step guide to various genomic sequence pre-processing and representation techniques. Then we explore feature extraction methods including tokenization, and transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, prediction, and language processing algorithms. Additionally, we explore the role of GLM in func- tional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers (BERT), enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to me-taheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy op-timization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops op-timizer (AGTO), were introduced. Based on the OCP extracted from simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluated the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods.

The benefits of engagement in sport are widespread, and include not only improved health and wellness, but also engagement in social participation and the realization of rights. Research on the use of assistive products in sport participation has previously focused largely on the use of specialized products in elite sport and has not addressed the importance of every-day assistive products for facilitating sport participation. This research aims to highlight the use of the 50 products on the World Health Organization’s Priority Assistive Product List for sport participation. We found that all 50 products are relevant to sport participation, and support participants to engage directly in sport, but also in social engagement with other sport participants, and as observers and fans of sport.

This review explores methodological considerations for maximizing the precision and accuracy of observational cohort studies assessing the risk profiles of tobacco and nicotine products. These considerations, informed by the ROBINS framework for minimizing statistical bias, are anchored in a comprehensive characterization of exposure to all tobacco products currently or formerly used, with corroboration of dose-response relationships. Best-practice considerations for non-randomized observational studies of exposure are enumerated, to most precisely and accurately inform future waves of databases, studies and meta-analyses. These principles are then considered within the context of a recent comprehensive meta-analysis, representing 107 observational studies, which evaluated the health effects of using electronic cigarettes (EC) compared to non-use, use of combusted cigarettes (CC) and dual-use of both products (DU). The meta-analysis had previously reported the relative risk from all-sources, including tobacco use and non-tobacco use. We now report the magnitude of risk reduction associated with displacing CC use with EC use indicated from the primary references, along with considerations regarding the precision of characterization of dose, duration, and timing of CC and EC use in the cited studies.

During the post-harvest of coffee and plantain, organic residues with high potential for utilization are generated. This work aimed to measure the effect of extrusion on the nutritional, physicochemical, and functional properties of mixtures of coffee pulp (CP), rejected plantain (RP), and plantain rachis (PR) flours. The residues were dehydrated, milled, and mixed according to the simplex reticular experimental design. Subsequently, the mixtures were extruded. The properties before and after extrusion were determined. It was found that the effect of extrusion reduced the crude fiber and lipid content composition, but protein and ash content were not changed. A positive relation was found between coffee pulp flour and rachis plantain flour in response to total phenolic content (TPC) and antioxidant activity (AA). Some blends increased the TPC and AA, but others reduced it. At the same time, water activity and water and oil absorption capacity showed a significant extrusion effect, while the pH did not. It was determined that the optimum mixture extruded was 0,364:0,333:0,303 of CP, RP, and PR, respectively. Extrusion reduced all pasting properties of the optimized blend. The flours studied presented a relevant nutritional and functional contribution, which favors their viability for use in the food industry.

Obesity management is a big challenge for health care providers. Obesity-related metabolic diseases are the most common chronic conditions and risk for shorter lifespan. Primary care is the appropriate level, not only for the management but for the prevention and early recognition as well. Obesity develops gradually and needs attention in the early phase of weight gain. The main results of four Hungarian and international studies in primary care settings were summarized, seeking relationship between weight gain in younger life and development of metabolic diseases. Data of primary care patients were collected about the changes of their weight gain from 20y to the present. Source: medical files and self-reports. Early weight-gain between 20y and 30y means a serious risk for developing diabetes, between 30y and 40y for hypertension and even faster weight-gain could be a risk factor for both metabolic diseases. In females, significant weight gain around pregnancies and the menopause could increase the risk of these morbidities as well. Primary care service providers/family physicians/general practitioners ought to be not only an inactive observers, they have to give more focus on those of their patients who show conspicuous weigh gain in their younger decades, to explore the individual reasons and to initiate the appropriate intervention as early as possible.

The instantaneous nature of quantum entanglement remains one of the most intriguing aspects of quantum mechanics. While the no-signaling theorem prohibits faster-than-light communication, the concept of a finite speed for the collapse of the quantum wavefunction has been a subject of philosophical debate. In this paper, we propose an experimental setup designed to test the speed of wavefunction collapse using entangled photon pairs over a distance of 10 km. By systematically varying the time delay between measurements and analyzing the resulting correlations, we aim to outline a method that could place bounds on the collapse speed without contradicting the no-signaling theorem. We argue that detecting a finite, possibly superluminal, speed of collapse could allow for maintaining realism and locality through high-speed remote synchronization, eliminating the need for hidden variables. We provide a detailed experimental design, including an algorithm and a Python implementation using the Cirq library to simulate the proposed experiment. Our goal is to motivate experimentalists to undertake this test, which could have profound implications for our understanding of quantum mechanics and the nature of reality.

Global population projected to reach 10 billion by 2050 emphasizing the need for sustainable food production. Traditional protein sources present environmental and scalability challenges, demanding the diversification of protein sources. Edible insects, such as house crickets (Acheta domesticus), emerged as a promising solution due to their high nutritional value, efficient feed conversion rates, and lower environmental impact compared to conventional protein sources. Incorporating insect powders into new food products can improve consumer acceptance, but often lead to poor functionality and/or undesirable organoleptic characteristics. However, protein isolates were revealed to be effective in enhancing functionality and consumer acceptance. This study aimed to develop and compare the yield of three different protein extraction methods, using sodium hydroxide, ascorbic acid or alcalase, from house crickets (Acheta domesticus) for food applications. Protein extraction was performed on cricket powder with a mean protein content of 57.95 g/100 g, and the results were evaluated. The enzymatic method showed the highest protein extraction rate of 85.97% with a mean protein content of 74.03 g/100 g, while extraction with NaOH or ascorbic acid resulted in rates of 74.32 and 56.99%, respectively. Further studies on the functionality and sensorial evaluation of products developed with this protein extract are recommended.

(1) Background: Glenohumeral internal and external rotational range of movement (GIRD and GERD) has been considered one of the primary outcomes to determine risk of injury in overhead athletes, as tennis players. Nevertheless, current testing position doesn’t consider than most tennis actions are repeated at 45of abduction, and actively. The aim of this study is establishing normative values of pathological GIRD and GERD in tennis players, to estimate the passive and active range of motion of the glenohumeral joint at 90° and 45º of abduction.; (2) Methods: 43 tennis players voluntarily participated in the study (19.1±2.75 years). Dominant and non-dominant shoulder range of motion (ROM), external rotation (ER), internal rotation (IR) at 90 and 45 in active and passive conditions were evaluated. GIRD and GERD were calculated in both positions and conditions; (3) Results: There were significant differences for all passive measurements between 45 and 90 testing position. ER and TROM at 90 and 45 showed significant differences in both passive and active conditions and in the dominant and non-dominant side. There were no significant differences in IR and TROM actively in both the dominant and the non-dominant at 90 or 45; (4) Conclusions: It is necessary to evaluate the ER under the same conditions at 90 or 45. Practitioners should consider assessing ER at the angle at which most actions are repeated in tennis (45°) as a method to monitor GERD. Evaluating GERD in asymptomatic tennis players could help avoid future biomechanical and GIRD problems. Both GIRD and GERD should be considered as a percentage of the athlete's own deficit in IR or ER, instead of having as a reference specific degree that have been observed in baseball pitchers.

This study investigates the use of two nanoclays, Laponite (Lap) and Montmorillonite (Mt), as eugenol slow release carriers for enhancing the functionality of low-density polyethylene (LDPE) active packaging films. Utilizing a vacuum-assisted adsorption method, eugenol was successfully intercalated into Lap and Mt nanoclays, forming EG@Lap and EG@Mt nanohybrids. The nanohybrids were subsequently incorporated into LDPE films at varying concentrations (5, 10, and 15 wt.%). Extensive characterization by a number of analytical techniques confirmed effective integration and dispersion of the nanohybrids within the LDPE matrix. EG@Lap and EG@Mt nanohybrids enhanced the films’ antioxidant and barrier properties, with EG@Lap demonstrating higher eugenol release rates, antioxidant activity, and barrier effectiveness. Packaging tests on minced pork revealed that the LDPE/15EG@Lap films extended shelf life by approximately two days more compared to the LDPE/15EG@Mt films, due to the higher eugenol release. These findings suggest that EG@Lap-based films offer improved active packaging capabilities, with potential applications in a number of areas where extending the freshness of perishable foods is needed.

In the bar of our Milky Way, there should be another supermassive black hole B which is with the mass almost 10−2~10−5 the Sgr A* and with a distance almost 10,000 lightyears from the Sgr A*. The bar is formed by the stars that are orbiting around the two black holes Sgr A* and B. And, in the bar, a lots of stars orbit around B while another lots of stars around Sgr A*. The two orbits form the peanut shape with “x-shaped structure” in the bar, while the boxy core is formed with all of the three orbits. And, the orbits of the stars out of the bar could be repulsed to a distant place by the black hole B to form the galactic spiral arms.The ultrafast bar should indicate that the conjectured black hole has been observed.

This study explores the impact and significance of implementing a comprehensive education system for children with special needs. The research aims to understand how inclusive practices influence the academic, social, and emotional development of these students. Using a mixed-methods approach that includes case study analysis, reviews of global education models, and theoretical frameworks, the study identifies key strategies for fostering inclusivity. The findings reveal that inclusive education not only enhances academic performance but also promotes social integration and emotional well-being. The analysis compares inclusive practices with traditional special education models and highlights the advantages of equity-driven, accessible learning environments. The study also addresses the challenges faced, such as inadequate resources and the need for continuous teacher training. Recommendations include policy reforms, increased funding, and the adoption of assistive technologies to support diverse learning needs. The research underscores the importance of a collaborative approach involving educators, parents, policymakers, and the community in creating an education system where every child has the opportunity to thrive.

Predicting the thermophysical properties of designed materials at the nanoscale is crucial. However, this remains a significant challenge despite numerous efforts by researchers and scientists. This paper presents a unified thermodynamic model to study the size and shape-dependent cohesive energy, as well as the melting temperature of semiconductor nanosolids, including InSb, ZnSe, CdS, and CdSe. The model provides insight into the thermal stability of nanosolid materials by considering the combined impact of size, shape, and packing density. The finding indicates that smaller nanoparticles exhibit a lower cohesive energy compared to larger ones. Our unified thermodynamic model yields results consistent with experimental values and demonstrates a closer agreement than those from the universal liquid drop model of size-dependent cohesive energy, which includes a shape factor of α = 2/3 for nanowires. According to the results, the melting temperature of nanoparticles drops as their size decreases. Below a diameter of 5 nm, this trend becomes more noticeable. In the nano-scale domain, the nanoparticle's shape also matters greatly. In particular, compared to tetrahedral nanoparticles, nanowires show a lesser reduction in melting temperature. Also, up to a diameter of roughly 10 nm, the melting temperatures of these nanoparticles rise with particle size. The search results show that, especially in the < 5 nm size range, the size and shape of nanoparticles significantly affect their melting point. The models accurately predict the relationship between particle size and melting point for various semiconductor nanomaterials, aligning with experimental findings. Interestingly, the information points to a potential preference for these nanomaterials to form nanowire structures in the < 5 nm size range. Our research offers helpful insights into the thermophysical properties of these semiconducting materials, which may have significant implications for their use in a diversity of technological applications.

The nature of multi-energy systems requires the integration of interdisciplinary methodologies and complementary tools in a coherent and systematic manner. Therefore, it is imperative to develop an efficient experimental environment to effectively implement the interaction between different tools for structural and parametric optimization (synthesis) of multi-energy systems and analysis of their operation. Due to the inherent complexity of the energy system optimization model, solving the synthesis problem often leads to significant challenges in terms of required computational resources and runtime. Known synthesis approaches do not fully address these challenges. In this context, we propose a subject-oriented environment for the multi-energy system synthesis in a reasonable time considering the available computational resources. The basis of the environment is a service-oriented application. The modeling and optimization of the studied systems is performed by means of scientific workflows. The execution of the specialized tools for modeling and optimization is done through web services. The pre-selection of these tools, depending on the level of model detail of the systems under study, and the data validation are performed on the specially developed testbeds. Such testbeds are constructed as system workflows. This is one of the distinguishing features of our approach. The application of the proposed environment is demonstrated through a case study on the synthesis of a microgrid (a special case of a multi-energy system). As a result of the study, an optimal microgrid configuration was proposed. It is based on the cogeneration of heat and electricity generation using natural gas.

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.

Background/Objectives: This study investigates the effectiveness of an herbal formulation, STRESSLESS (ST-65), which combines ashwagandha (Withania somnifera) and bacopa (Bacopa monnieri), on SH-SY5Y human neuroblastoma cells. Given the rising interest in natural compounds for neuroprotection and stress alleviation, we aimed to explore the cellular and molecular effects of this formulation. Methods: Utilizing a nuclear magnetic resonance (NMR) metabolomic approach and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS), we identified key bioactive compounds in ST-65, including withanolides from ashwagandha and bacosides from bacopa. Results: Our findings indicate that ST-65 treatment significantly alters the metabolic profile of SH-SY5Y cells. Key changes included increased levels of metabolites linked to neuroprotection, energy metabolism, and antioxidant defense. Notable enhancements were observed in specific amino acids and neuroprotective compounds, suggesting activation of neuroprotective mechanisms and mitigation of stress-induced damage. Conclusions: The study reveals a complex phyto-chemical profile of ST-65 and underscores its potential as a natural active agent for addressing stress-related neurodegenerative conditions. These insights into neuronal mechanisms provide a foundation for further exploration of herbal formulations in neuroprotection.

The escalating issue of phenol dye-containing wastewater necessitates the development of efficient and sustainable treatment methods. In this context, we present a novel composite photocatalyst comprising ZnFe2O4 (ZFO) nanoparticles supported on nanocellulose(NC), aimed at addressing this environmental challenge. The synthesis involved a facile hydrothermal method followed by impregnation of ZFO nanoparticles onto the NC matrix. ZFO, as a weakly magnetic semiconductor catalytic material, was utilized in photocatalytic experiments under magnetic field conditions. By controlling the electron spin states through the magnetic field, electron-hole recombination was suppressed, resulting in improved photocatalytic performance.The resultant composite exhibited remarkable photocatalytic activity under visible light irradiation, owing to the synergistic effect between ZFO nanoparticles and NC. Through systematic evaluation, we demonstrated the high efficiency and stability of the composite photocatalyst in degrading phenol dye pollutants in aqueous solutions. Furthermore, the composite material exhibited promising recyclability, retaining its activity even after multiple usage cycles. This study underscores the potential of the ZFO/NC composite for sustainable wastewater treatment, offering a promising avenue for environmental remediation.

Background: In the UK pharmacy professionals (pharmacists and pharmacy technicians) contribute to the delivery of local and national public or population health interventions. Existing literature predominantly focuses on micro-level activities, primarily in community pharmacies typically delivering public health interventions to individuals. Despite their sizeable daily interaction, there is little-known evidence on pharmacy professionals’ (PPs) involvement at meso and macro levels nor to what extent pharmacy professionals have specialist/advanced roles within public health practice. This mixed method study presents recommendations for future action. The study explored pharmacy professionals’ specialist/advanced roles within public health as well as their opportunities and barriers to career development. Method: The study included two surveys (for pharmacy professionals and public health professionals (PhPs)), a call for evidence and two workshops to develop recommendations. Pharmacy professionals (n=128) and public health professionals (n= 54) across the UK participated in the survey. Results: The majority of PP respondents were females (70%); pharmacists (85%), working in primary (33%) or secondary (25%) care settings, mainly based in England (75%), most (63%) lacked formal public health qualifications, although they were involved in a diverse range of public health interventions. The PhPs were mostly females (67%), practicing in England (58%). Both professional groups identified opportunities and barriers to pharmacy professionals' involvement in public health. Almost half or the PhP respondents (44%) stated that they had a pharmacy professional working as part of their current public health teams. Eighty-seven per cent of PhP respondents (45/52) agreed that having pharmacists or pharmacy technicians specialising in public health would be beneficial or very beneficial. Most documents, reports, case histories provided through the call for evidence were unpublished. The workshops generated 94 recommendations, highlighting collaboration and the need to acknowledge pharmacy professionals' contributions to public health. Conclusion: The recommendations for action had three main themes: national strategic approach and commissioning, workforce development and further pharmaceutical public health evidence reviews research.

Background: Barrett’s Esophagus (BE) is the only known precursor for esophageal adenocarcinoma (EAC). Patients with multiple risk factors for BE/EAC are recommended for screening, however few eligible patients undergo evaluation by endoscopy. EsoGuard® (EG) is a commercially available biomarker assay used to analyze esophageal cells collected non-endoscopically with EsoCheck® (EC), for qualitative detection of BE/EAC. This study evaluates real-world clinical utility of EG on cells collected with EC in patients defined by U.S. gastroenterology societies to be at-risk for BE and EAC. Methods: This multi-center, observational CLinical Utility of EsoGuard (CLUE) study enrolled screening-eligible patients as defined by the American College of Gastroenterology (ACG) and the American Gastroenterological Association (AGA). Clinical utility was evaluated by the provider decision impact of EG, and additionally by assessing patient compliance outcomes with recommended follow-up testing. Results: 551 patients were enrolled, with a mean age of 62.0 ± 12.4 years and 56.1% (309/551) meeting ACG guideline criteria for BE screening. EC cell collection was successful in 97.1% (535/551), among which EG positivity rate was 27.3% (n = 146). The provider decision impact was high with 100% of EG positive patients being referred for esophagogastroduodenoscopy (EGD), while 98% of EG negative patients were not referred. Among EG positive patients, the overall compliance with follow-up EGD was 85.4%. Conclusions: Combining EC non-endoscopic esophageal cell collection with the EG biomarker assay is effective in guiding provider decision-making for detection of BE and EAC. Patients with positive EG results demonstrate high compliance with recommended follow-up EGD.

The positive relationship between species richness and area is a fundamental principle in ecology. However, this pattern deviates on small islands, where species richness either changes independently of area or increases at a slower rate—a phenomenon known as the Small-Island Effect (SIE). While the SIE has been well-documented in natural ecosystem, its presence in highly fragmented and disturbed urban ecosystem remains unexplored, posing challenges for urban vegetation conservation. Urban remnant vegetation, isolated by surrounding infrastructures, preserves intact zonal vegetation characteristics, serves as a benchmark for restoring near-natural habitats and offers ideal conditions to test the existence of SIE in human-altered landscapes. In this study, we surveyed 17 remnant vegetation patches in Qingdao City, China. In total of 331 plants attributed to 255 genus in 81 families been recorded. Firstly, by using six species-area relationship regression models tested SIE for remnant vegetation with different life form (i.e. annual herb, perennial herb, shrub and tree), we found SIE detected in only woody plants, with area threshold ranging from 6.38 ha (tree) to 11.91 ha (shrub). According to stepwise and generalized linear regression mode analysis we found SIE detected in only woody plants, with area threshold 6.38 ha for tree and 11.91 ha for shrub. Further analysis revealed that the SIE in shrub was driven by landscape shape index, perimeter-area ratio, and the proportion of sealed surface within patch. For trees, the SIE was influenced by the distance to the source of species, GDP, night light intensity and perimeter-area ratio. This finding justifies that conservation in urban planning, construction and development should focus not only on protecting large areas, but also on maintaining and promoting diverse habitats within these areas. At the same time, reducing anthropogenic disturbance and enhancing the connectivity of green spaces are important for the persistence of metacommunities and can contribute to the local species pool, thus potentially improving the ecological resilience of urban environments.

Cervical cancer remains a significant global public health challenge, affecting over half a million women annually, with a mortality rate of approximately 60%, especially in resource-limited regions. This study presents an advanced methodology for cervical cancer diagnosis through deep learning techniques. Utilizing a publicly available cervical cancer image dataset, the research introduces a novel classification framework that integrates a Neural Feature Extractor (NFE) based on a pre-trained VGG16 architecture and an AutoInt model for automatic feature interaction learning. The extracted features are processed through machine learning classifiers such as KNN, LGBM, Extra Trees, and others for classification tasks. Among these classifiers, KNN achieved the highest accuracy of 99.96%, followed closely by LGBM at 99.92%. The study also assesses the computational complexity of various models, demonstrating that simpler models like LDA exhibit faster prediction times, while more complex models, such as KNN and LGBM, provide higher accuracy. These findings highlight the potential of deep learning frameworks in improving cervical cancer classification accuracy, especially in resource-limited environments.

Ischemic stroke (IS) remains a leading cause of mortality and long-term disability worldwide, with limited therapeutic options available. Despite the success of early interventions such as tissue-type plasminogen activator administration and mechanical thrombectomy, many patients continue to experience persistent neurological deficits. The pathophysiology of IS is multifaceted, encompassing excitotoxicity, oxidative and nitrosative stress, inflammation, and blood-brain barrier disruption, all of which contribute to neural cell death, further complicating the treatment of IS. Recently, extracellular vesicles (EVs) secreted naturally by various cell types have emerged as promising therapeutic agents because of their ability to facilitate selective cell-to-cell communication, neuroprotection, and tissue regeneration. Furthermore, engineered EVs, designed to enhance targeted delivery and therapeutic cargo, hold potential to improve their therapeutic benefits by mitigating neuronal damage and promoting neurogenesis and angiogenesis. This review summarizes the characteristics of EVs, the molecular mechanisms underlying IS pathophysiology, and the emerging role of EVs in IS treatment at the molecular level. This review also explores the recent advancements in EV engineering, including the incorporation of specific proteins, RNAs, or pharmacological agents into EVs to enhance their therapeutic efficacy.