Practical Requirements Research Articles

Nonlinear vibration of a loaded string in energy harvesting

Abstract Designing wideband energy harvesters using beam structures typically involves complexities, particularly in low-frequency and low-energy environments where the limitations of beam structures become more evident. To address these challenges, this study proposed a strategy for energy harvesting using a loaded-string system and established a theoretical model to investigate its performance. A parametric study was conducted for the string system, examining the effects of initial tension, mass location, material stiffness and excitation amplitude. The accuracy of the proposed model was verified through experimental validation. Both theoretical and experimental analyses observed a frequency shifting phenomenon, demonstrating the wideband characteristics of the system. Furthermore, the proposed string structure allows for convenient parameter adjustments, enabling the tuning of its natural frequency and operating bandwidth to meet more stringent practical requirements. The string system provides a new direction for designing energy harvesters to harness low-frequency energy from the ambient environment.

Motor imagery EEG signal classification based on deformable convolution v3 and adaptive spatial attention mechanism

This paper is dedicated to solving the issue of insufficient feature extraction and declining model performance in the classification of motor imagery electroencephalogram (EEG) signals. We propose an enhanced ResNet18 neural network model and introduce two pivotal innovations at the model level. Firstly, we present the deformable convolution v3 (DCNv3), an improved convolutional structure that overcomes the limitations of the original deformable convolution. Through unbiased design with center sampling points, DCNv3 achieves more precise feature localization, enhances the model’s representation capability for complex structures, and reduces the risk of overfitting. Simultaneously, it comprehensively captures features in effective information regions, thereby strengthening the model’s generalization ability. Furthermore, this enhancement design also improves computational efficiency, reducing computational and parameter burdens. Secondly, we introduce a novel Adaptive Spatial Attention Mechanism (ASAM) aimed at precisely capturing feature correlations, thereby avoiding information loss. We conducted extensive comparative experiments on publicly available datasets BCI IV dataset 2a, BCI IV dataset 2b, and BCI III dataset 3a, achieving accuracy rates of 91.32%, 92.3%, and 90.47% respectively. Compared to other methods, our approach not only significantly improves the average classification accuracy but also demonstrates remarkable generalization capabilities. These results validate the effectiveness and stability of our method, thereby better meeting the practical application requirements of brain-computer interface technology.

Инструменты поддержки предпринимательства в Арктике: резильентный подход

The article deals with the development of state support for business in the Arctic zone of Russia for the period 2020–2023 from the position of the resilient approach. Achieving national strategic objectives in the region necessitates the formulation of new approaches to implementing state support, taking into account the “updated” priorities and the accelerating changes. The demand for a modern interpretation of the concept of resilience is driven by practical requirements to address the challenges of economic sustainability. The analysis conducted demonstrates that the development of the Russian Arctic zone will depend on the “properly adjusted” mechanisms of state support and systemic measures aimed at creating favorable conditions for entrepreneurship, the livelihoods of the population, and the traditional sector of the ethno-economy in this region. There is an urgent need to establish a comprehensive support system to address national tasks in the Arctic, including the development of key settlements. This involves simplifying permitting procedures, creating infrastructure, refining and coordinating preferential regimes integrated into the strategic planning system, providing comprehensive support for businesses from regional and local authorities, and utilizing tax catalysts to enhance the resilience of the socio-economic systems of settlements in the northeastern Arctic zone of Russia.

Recent progress in solar-driven CO2 reduction to multicarbon products.

Currently, most catalysts used for photoconverting carbon dioxide (CO2) typically produce C1 products. Achieving multicarbon (C2+) products, which are highly desirable due to their greater energy density and economic potential, still remains a significant challenge. This difficulty is primarily due to the kinetic hurdles associated with the C-C coupling step in the process. Given this, devising diverse strategies to accelerate C-C coupling for generating multicarbon products is requisite. Herein, we first give a classification of catalysts involved in the photoconversion of CO2 to C2+ fuels. We summarize metallic oxides, metallic sulfides, MXenes, and metal-organic frameworks as catalysts for CO2 photoreduction to C2+ products, attributing their efficacy to the inherent dual active sites facilitating C-C coupling. In addition, we survey covalent organic frameworks, carbon nitrides, metal phosphides, and graphene as cocatalysts for CO2 photoreduction to C2+ products, owing to the incorporated dual active sites that induce C-C coupling. In the end, we provide a brief conclusion and an outlook on designing new photocatalysts, understanding the catalytic mechanisms, and considering the practical application requirements for photoconverting CO2 into multicarbon products.

Energy-efficient and directional preparation of cellulose nanosheets by one-pot surface modification assisted swelling method

The energy-intensive and time-consuming process required for disassembling natural materials into nanoblocks are the major obstacles for the practical applications of biopolymer nanomaterials. Herein, a one-pot, energy-efficient and directional preparation method for gently exfoliating cellulose into nanosheets was achieved by surface modification assisted swelling process. The resulting cellulose nanosheets (CNSs) exhibited diameters ranging from 100 to 480 nm and thicknesses of approximately 5 nm, with a maximum yield of 97.9 %. This method could be widely applicable to common cellulose raw materials. Contrary to other nanocellulose reported previously, CNSs could be dried and stored in solid state, and re-dispersed in aqueous phase, thereby convenient for storage and transportation. Cellulose nanosheets films (CNSFs) obtained from CNSs showed high transparencies (>90 %) and excellent gas barrier properties, especially for the water vapor permeability of only 0.0072×10−10 cm3 cm cm−2 s−1 Pa−1, which were superior to other cellulose based films. In the simulation experiment of dry food packaging, CNSF10 possessed remarkable water vapor and oxygen blocking capabilities comparable to commercial cling films and met the practical requirements. The preparing process of CNSs reported here had the advantages including easy implementation, energy efficiency, and environmentally friendliness, and expanded possibilities for large-scale and widespread utilization of nanocellulose.

Refining soil nutrient assessment: Incorporating land use boundaries for precision agriculture.

Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km2, 25 km2, and 4.05 km2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management.

Automation of simplified two-step radiolabeling via ditosylate synthon for 18F-labeled radiotracers using AllinOne module

Direct fluorination of a tosylate or mesylate precursor has been a wide-spread and reliable way for radio-fluorination. This approach can be difficult to achieve when the precursor cannot be easily obtained or is chemically unstable. A possible alternative method is to radiolabel ethylene ditosylate or 1,3-propanediol di-p-tosylate to form a radiofluorinated synthon. Here we present the automation of a simplified and reliable approach for the two-step fluorination using [18F]FP-TMP, an analog of antibacterial agent trimethoprim. We demonstrate the feasibility of purifying the fluorinated synthon via filtration, and one final HPLC purification on a commercially available Trasis AllinOne module. The overall reaction time for the two-step reaction is around 90 min andthe decay-corrected yield for more than fifty preparations of [18F]FP-TMP is 22 ± 5 % with high radiochemical purity (≥ 90 %) and specific activities (147 ± 107 GBq/μmol). All batches passed pre-established quality control specifications, demonstrating the utility of using this method in tracer syntheses that meet good manufacturing practice (GMP) requirement. This method can be adopted to the syntheses of other radiotracers, such as [18F]FE-TMP, (+)-[18F]F-PHNO and [18F]FFMZ.

Livestream sales prediction based on an interpretable deep-learning model

Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply–demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction. Our model achieved correct prediction results on our private, real operating data. The mean absolute percentage error (MAPE) of our model’s performance fell as low as 11.47% for a + 1.5-days forecast. Even for a 1-week forecast (+ 6 days), the MAPE was only 19.79%, meeting actual business needs and practical requirements. Notably, our model demonstrated robust interpretability, as evidenced by the feature contribution results which are consistent with prevailing research findings and industry expertise. Our findings provided a theoretical foundation for predicting shopping behavior in live-broadcast e-commerce and offered valuable insights for designing live-broadcast content and optimizing the user experience.

Ultra-Strong Protein-Based Hydrogels via Promoting Intermolecular Entanglement of the Amorphous Region.

Proteins are classified as biopolymers which share similar structural features with semi-crystalline polymers. Although their unique biocompatibility facilitates the universal applications of protein-based hydrogels in the biomedical field, the mechanical performances of protein-based hydrogels fall short of practical requirements. Conventional strategies for enhancing mechanical properties focus on forming regularly folded secondary structures as analogs of crystalline regions. This concept is based on proteins as the analogy of semi-crystalline polymers, in which crystalline regions profoundly contribute to the mechanical performances. Even though the contribution of the amorphous region is equally weighted for semi-crystalline polymers, their capacity to improve the mechanical performances of protein-based structures is still undervalued. Herein, the potential of promoting the mechanical performances is explored by controlling the state of amorphous regions in protein-based hydrogels. A fibril protein is chosen, regenerated silk fibroin (RSF), as a model molecule for its similar viscoelasticity with a semi-crystalline polymer. The amorphous regions in the RSF hydrogels are transformed from extended to entangled states through a double-crosslinking method. The formation of entanglement integrates new physically crosslinked points for remarkable improvement in mechanical performances. A robust hydrogel is not only developed but also intended to provide new insights into the structural-property relationship of protein-based hydrogels.

COVID-19 Vaccination Recommendations: Challenges and Strategies for High-Risk Populations

<p>COVID-19（新冠併發重症）大流行對全球，尤其是有潛在醫療狀況的易感染之高風險族群，造成深遠的影響。為因應全球不斷演變的疫情趨勢以及台灣持續增加病例的疫苗防治需求，台灣家庭醫學醫學會建立了針對易感染之高風險族群的COVID-19疫苗接種建議。本研究進行全面性的文獻回顧並經由專家小組討論後，擬定具證據力的建議以作為防疫策略參考。總計提出25項建議，說明糖尿病、肥胖症、慢性腎臟疾病、心血管疾病和呼吸系統疾病病友感染COVID-19後的重症風險為高，因此強調COVID-19 mRNA（訊息核糖核酸）疫苗在這些易感染之高風險族群中的安全性和有效性以及家庭醫師在COVID-19疫情的關鍵作用，包括常規篩檢、衛教、疫苗接種服務的提供和持續的研究。此建議為醫界與政府相關單位在為易感染高風險族群制定疫苗接種計畫時，提供了兼具客觀實證依據與符合臨床實務需求的具體指引，同時強化家庭醫師在防疫工作中的核心角色，透過持續的監測與積極推動疫苗施打，在不斷變化的COVID-19疫情趨勢中發揮重要作用。</p> <p> </p><p>The COVID-19 pandemic has had a profound global impact, particularly on high-risk populations, such as those with underlying medical conditions. In response to the evolving pandemic landscape and the constant surge in the need to vaccinate among vulnerable cases in Taiwan, the Taiwan Association of Family Medicine (TAFM) has developed recommendations on COVID-19 vaccination for high-risk groups. Based on a comprehensive literature review and expert panel discussions, a total of 25 evidence-based recommendations have been developed to serve as a reference for vaccination strategies, highlighting the increased risk of severe COVID-19 outcomes among individuals with complications such as diabetes, obesity, chronic kidney disease, cardiovascular disease, and respiratory illnesses. The recommendations emphasize the safety and effectiveness of COVID-19 vaccines, particularly mRNA vaccines, for the aforementioned high-risk populations and underscore the critical roles of family physicians in response to severe COVID-19 outcomes, including routine screening, health education, vaccine administration, and continuous research to optimize strategies. The TAFM recommendations provide a robust, evidence-based framework that meets clinical practice requirements and provides effective guidelines for physicians, healthcare institutions, and governments in tailoring vaccination efforts to address the unique needs of high-risk groups in Taiwan. Ongoing monitoring and proactive vaccination drives are essential for ensuring the effectiveness of these recommendations in the evolving COVID-19 landscape, while also accentuating the continuous efforts of family physicians in preventing endemic diseases.</p> <p> </p>

Quantitatively nonblocking supervisory control of discrete-event systems

In this paper, we propose a new property of quantitative nonblockingness of an automaton with respect to a given cover on its set of marker states. This property quantifies the standard nonblocking property by capturing the practical requirement that every subset (i.e. cell) of marker states can be reached within a prescribed number of steps from any reachable state and following any trajectory of the system. Accordingly, we formulate a new problem of quantitatively nonblocking supervisory control, and characterize its solvability in terms of a new concept of quantitative language completability. It is proven that there exists the unique supremal quantitatively completable sublanguage of a given language, and we develop an effective algorithm to compute the supremal sublanguage. Finally, combining with the algorithm of computing the supremal controllable sublanguage, we design an algorithm to compute the maximally permissive solution to the formulated quantitatively nonblocking supervisory control problem.

Clinical Practice Ability and Related Factors Among Senior Undergraduate Nursing Students Post-Clinical Internship: A Latent Profile Analysis

The shortage of nurses and inadequate clinical practice abilities among senior undergraduate nursing students has raised significant concerns globally. The discrepancy between nursing education and practical skill requirements in hospitals is the primary contributor to this issue. Objectives: This study aimed to (1) explore potential classifications for assessing the clinical practice abilities of senior undergraduate nursing students in clinical practice, (2) identify distinct subgroups based on their clinical practice abilities, and (3) analyze the factors influencing these subgroups. Design: This was a cross-sectional study using latent profile analysis. Participants: A total of 336 fourth-year nursing students who had recently completed clinical practice were included in this study. Methods: Data were collected using a structured questionnaire that included sociodemographic factors, the Nursing Professional Attitude Questionnaire (NPAQ), and the Environmental Assessment Scale for Nursing Clinical Practice. The Clinical Ability Scale for Undergraduate Nursing Students (CCSUNS) measures clinical practice abilities. Latent profile analysis was used to identify subgroups of students based on their clinical practice abilities. Multinomial logistic regression analysis was used to analyze the factors influencing these subgroups. Results: Three profiles of clinical practice ability were identified: (1) "low level - guidance dependent" (51.79%), (2) "intermediate - response improvement" (33.85%), and (3) "high level - autonomous decision-making" (16.37%). Key influencing factors included gender, academic performance, professional nursing attitude, and the clinical practice environment. Conclusions: More than half of the senior nursing students demonstrated poor clinical practice abilities. Enhancing the clinical practice environment, increasing practical opportunities, fostering professional attitudes, and addressing gender equality are critical for improving clinical practice abilities. Future multicenter longitudinal studies are recommended to explore changes in clinical practice abilities over time and across different stages of clinical practice.

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

A fuzzy multi-objective programming model for the delivery and distribution of humanitarian relief materials

Disaster relief presents many unique logistics challenges, including damaged transportation infrastructure, multiple conflicting goals, and secondary disasters caused by spontaneous disaster relief. The delivery and distribution of humanitarian relief materials faced by nonprofit organizations (NPOs) poses a critical challenge due to the highly unpredictable nature of disasters, conflicting missions, and the need to navigate government regulations in disaster management. In this paper, we propose a novel multi-objective disaster relief modeling system for NPOs, considering (1) prioritization (providing medical services to seriously injured victims), (2) effectiveness (degree of satisfaction), (3) efficiency (operational costs), and (4) equity (cost of maintaining order). To incorporate the practical constraints of disaster relief operations, we include factors such as road damage, transportation capacity limitations, and route flow saturation as part of the model formulation. We then utilize fuzzy multi-objective programming to optimize the proposed disaster relief model, considering two relief distribution schemes: a common NPOs' spontaneous relief scheme and a newly designed traffic control scheme with government regulation. Based on the real-world scenario of the 2008 Wenchuan earthquake, we demonstrate that the designed traffic control scheme exhibited remarkable improvement among the NPOs' four objectives compared to the spontaneous relief scheme. Moreover, in the process of solving fuzzy multi-objective programming problems, artificial intelligence technology is employed to handle the fuzziness in multi-objective and automatically adjust parameters, making the solution results better and in line with practical situations. And we also validate the proposed model can be solved in polynomial time, meeting the engineering practice requirements, and can be easily extend to most disaster relief distribution scenarios.

Adaptive spatiotemporal neural networks through complementary hybridization

Processing spatiotemporal data sources with both high spatial dimension and rich temporal information is a ubiquitous need in machine intelligence. Recurrent neural networks in the machine learning domain and bio-inspired spiking neural networks in the neuromorphic computing domain are two promising candidate models for dealing with spatiotemporal data via extrinsic dynamics and intrinsic dynamics, respectively. Nevertheless, these networks have disparate modeling paradigms, which leads to different performance results, making it hard for them to cover diverse data sources and performance requirements in practice. Constructing a unified modeling framework that can effectively and adaptively process variable spatiotemporal data in different situations remains quite challenging. In this work, we propose hybrid spatiotemporal neural networks created by combining the recurrent neural networks and spiking neural networks under a unified surrogate gradient learning framework and a Hessian-aware neuron selection method. By flexibly tuning the ratio between two types of neurons, the hybrid model demonstrates better adaptive ability in balancing different performance metrics, including accuracy, robustness, and efficiency on several typical benchmarks, and generally outperforms conventional single-paradigm recurrent neural networks and spiking neural networks. Furthermore, we evidence the great potential of the proposed network with a robotic task in varying environments. With our proof of concept, the proposed hybrid model provides a generic modeling route to process spatiotemporal data sources in the open world.

MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications

Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuitionistic fuzzy c means clustering. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. The clustering algorithm has membership and non membership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. Furthermore, the algorithm is extended for calculating membership and updating prototypes by minimizing the new objective function of weighted LINEX intuitionistic fuzzy c-means. Finally, the developed algorithms are illustrated through conducting experiments on random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.

Technology Integration in Teaching: Applying TPACK Model for Vietnamese Universities

This research aims to synthesize international experiences in applying technology in teaching according to the TPACK model (Technological Pedagogical Content Knowledge), in order to draw lessons and apply them to Vietnamese universities. This article uses a theoretical research method, mainly synthesizing and analyzing texts from research papers published in large databases including Science Direct, Scopus, and Google Scholar, using keywords such as "TPACK" and "application of technology in teaching". To effectively apply technology, educational technologists have developed modern teaching frameworks, including the TPACK model, which combines three elements: (i) Technological knowledge; (ii) Pedagogical knowledge; and (iii) Content knowledge. Teachers need to harmoniously integrate these three elements when applying the TPACK model. In Vietnam, there have been some recent studies on the TPACK model in universities; however, the number of studies is still limited. This research aims to synthesize and analyze relevant studies on the application of technology in teaching according to the TPACK model. International experiences serve as practical lessons for Vietnamese universities to adapt and apply appropriately to the TPACK model, integrating technology with content and pedagogical methods in teaching to meet current practical requirements.

Detection of Abnormal Blood Flow Region Based on Near Infrared Correlation Spectroscopy

Blood flow measurement of microvessels in human tissues is of vital importance for the diagnosis and treatment of many diseases. In this paper, the detection method of abnormal blood flow regions based on near-infrared correlation spectroscopy is studied. We used the NL-Bregman-TV imaging algorithm to realize Blood flow imaging. However, due to the limitation of the number and distribution of detectors, the pixels obtained from images are extremely low, which cannot meet the practical requirements of the visual and the abnormal blood flow range measurement. In this paper, the bicubic interpolation method is used to improve the resolution of low-pixel blood flow images. The parameter index of the normalized similarity was proposed to help judge the effect of the interpolation method on the resolution of this kind of image. Aiming at the extraction of abnormal regions, a threshold segmentation algorithm based on the histogram difference method and a morphological processing algorithm is proposed to extract the contour of abnormal blood flow. The method proposed in this paper can be used to accurately locate and extract the clear and smooth contour of abnormal blood flow.

Perceptions of Nurse Practitioners and Physician Assistants/Associates Toward the Concept of Developing an Advanced Practice Postgraduate Residency/Fellowship Program at a Large Academic Medical Center.

Introduction There have been exponential growth and increased interest in postgraduate residency/fellowship formalized training among nurse practitioners (NPs) and physician assistants/associates (PAs). Although not a requirement for state licensure and entry-level practice, postgraduate NP and PA residency/fellowship programs offer a structured pathway for new graduates and experienced PAs and NPs looking to transition into a new medical or surgical specialty/subspecialty. In this article, we examine the perceptions of employed NPs and PAs toward postgraduate training including the concept of developing a program at our institution. Methodology This was a cross-sectional single-large academic medical center (AMC) study, where an anonymous electronic survey was initially developed by the director of advanced practice providers (APPs). The survey was piloted by members of the Advanced Practice Council (APC) comprising NPs, PAs, and certified registered nurse anesthetists (CRNAs), and their collective feedback was used to finalize the survey prior to distribution. Descriptive statistics were used to describe and summarize the data. In addition, we assessed the association between respondents' characteristics and perceptions regarding NP and PA residency/fellowship programs using chi-squared tests of independence. Results The majority of the respondents (69.1%; 65/94) believed that starting an advanced practice residency/fellowship program at our AMC has many benefits such as increasing specialty-specific knowledge and training (79.8%; 75/94), improving medical decision-making (73.4%; 69/94), promoting professional development and clinical education (73.4%; 69/94), and improving procedural competency (60.6%; 57/94). Moreover, over half of the respondents (53.2%; 50/94) indicated that there is value in an APP residency/fellowship program obtaining optional accreditation. Additionally, over half of the respondents (56.4%; 53/94)indicated that they would be interested in participating as a clinical preceptor if an APP residency/fellowship program was developed at our AMC. Lastly, about one-third of the respondents felt strongly that APP residency/fellowship training programs should offer post-professional doctoral degree options such as Doctor of Nursing Practice (DNP), Doctor of Medical Science (DMSc), and Doctor of Health Science (DHSc). Conclusion Although most respondents have never completed a formalized postgraduate training program and were less familiar with the published literature regarding these programs, our findings suggest that the attitudes ofemployed NPs and PAs aregenerally positivetoward the concept of postgraduate specialty-specific training.

Development of an In-situ Nasal Gel Formulation of Bepotastine Besilate for the Treatment of Allergic Rhinitis

The objective of this research was to formulate and evaluate mucoadhesive in situ nasal gels as a novel delivery system for Bepotastine Besilate. Aiming to provide sustained drug release directly at the site of action, thereby reducing the need for frequent dosing due to the drug's rapid absorption and short half-life. A series of eight formulations were developed using varying ratios of Poloxamer 407 and Poloxamer 188. The formulations exhibited a visually clear sol phase, indicating uniform dispersion of ingredients, with gelling temperatures ranging from 33.3±0.41 to 36.7±0.73°C. The gelling time, an important parameter for user convenience and efficacy, ranged from 15.7±01.52 to 43.3±20.80 s, meeting practical application requirements. Furthermore, all formulations consistently achieved a drug content of over 95%, ensuring dosage uniformity and efficacy. The pH values of the formulations were within the acceptable range of 6.13±0.10 to 6.91±0.02, minimizing the risk of mucosal irritation upon application. Importantly, the mucoadhesive strength ranged from 1056±0.32 to 6456± 045 dyne/cm², indicating robust adhesion to nasal mucosa, which is critical for prolonged drug retention and sustained release. In vitro drug release studies demonstrated sustained release profiles exceeding 4 hours for all formulations, following Higuchi kinetics, suggesting controlled drug release from the gel matrix. Accelerated stability studies corroborated the formulations' stability over the test period, indicating their potential for long-term storage and commercial viability. Additionally, FTIR analysis revealed no evidence of drug-polymer interactions, confirming the compatibility of Bepotastine Besilate with the selected polymer matrix. These comprehensive findings support the potential of in situ nasal gels as an effective and promising strategy for enhancing the therapeutic efficacy of Bepotastine Besilate, thereby improving patient outcomes.