Room For Enhancement Research Articles

Performance and trend of core measures among oral cancer patients: A population-based cohort study

Abstract Objectives This study explores the performance and trend of core measures, which were established as evidence-based indicators to assess and improve the quality of care for oral cancer patients. Methods This retrospective cohort study uses the National Health Insurance Research Database (NHIRD), Taiwan Cancer Registry, and Cause of Death Statistics, provided by the Health and Welfare Data Science Center (HWDC), Ministry of Health and Welfare (MOHW). The study population was adult patients hospitalized for oral cancer surgery with stage I-IV between 2014 and 2021. The trend tests and the generalized linear mixed model (GLMM) were applied. Results A total of 23392 oral cancer patients were enrolled. The core measure set used in this study included five indicators (two process-related and three outcome-related indicators). Process indicators’ mean achievement rates significantly increased from 82.6% to 85.7% (p = 0.018, trend test). On the other hand, most patients demonstrated good achievement rates to the outcome indicators (98.9% to 99.3%) (p = 0.218, trend test). Overall indicators’ 100% achievement rate increased over time from 62.9% in 2014 to 66.2% in 2021 (p < 0.001, trend test). Subgroup analyses were performed by age, stage, and comorbidity score. The analysis of the GLMM revealed that the 100% achievement to overall indicators was positively associated with age<45, not-low socioeconomic status, body mass index ≥ 24, squamous cell carcinoma, recent treatment year, treated with surgery only, negative surgical margins, and receiving care in a high-volume hospital (all p < 0.01). Conclusions The results highlight a significant measurable improvement in the quality of oral cancer care. However, there is still room for enhancement in process indicator performance. Key messages • The results highlight a significant measurable improvement in the quality of oral cancer care. • There is still room for enhancement in process indicator performance.

Urban Planning Perspective on Food Resilience Assessment and Practice in the Zhengzhou Metropolitan Area, China

This study aims to assess and analyze the urban food resilience of the Zhengzhou metropolitan area, proposing innovative assessment frameworks and methodologies. Utilizing a dual-level analysis approach that combines long-term planning impact analysis (2000–2020) with short-term resilience assessment (2018–2022), the study integrates public government data and Geographic Information System (GIS) data, employing spatial analysis, Analytic Hierarchy Process (AHP), and fuzzy comprehensive evaluation techniques. Findings from 2000 to 2020 indicate that urban planning within the metropolitan area has significantly impacted the food system. Urbanization has led to reduced agricultural land, but improvements in infrastructure have enhanced the efficiency of the food supply chain. Woodland and grassland areas have remained relatively stable, providing an ecological buffer for the food system. Building on this, the short-term assessment from 2018 to 2022 reveals significant dynamic changes and a continuous improvement trend in food resilience, though there is still room for enhancement. Food supply chain management and emergency preparedness and management contributed the most to overall resilience. Notably, extreme events such as the COVID-19 pandemic and the “7.20 Flood Disaster” prompted the adoption of innovative measures to enhance food resilience. The study develops a multidimensional theoretical framework and assessment system for urban food resilience, offering new perspectives and methods for understanding and enhancing urban food resilience. The results highlight the critical role of urban planning in enhancing food resilience, recommending the integration of the food system into comprehensive urban planning, strengthening regional collaboration, and enhancing public engagement. These findings provide an important basis for policymaking and practice aimed at improving the long-term adaptability and short-term recovery capabilities of urban food systems.

Price-Predicting Model: Predictions for All Sailboats

Sailboat pricing is a nuanced field, significantly influenced by vessel age, geographical location, and brand reputation. To address this complexity, we leveraged the XGBoost algorithm, a powerful machine learning tool, and refined it through Bayesian optimization to create a highly accurate model for predicting sailboat prices. Our analysis revealed significant regional disparities, with sailboats from Europe, the USA, and the Caribbean commanding varying market valuations. When applying this optimized model to the Hong Kong used sailboat market, we were able to generate forecasts with improved accuracy, though still with room for enhancement. Notably, the Lagoon brand stood out as a consistent high performer, underscoring the importance of brand reputation in sailboat pricing. To provide actionable intelligence, we have synthesized a comprehensive analysis that integrates our research findings and predictive strategies. This analysis presents clear visualizations alongside concise interpretations, thereby constituting a pivotal resource for informed decision-making within the local market context.

Corporate Social Responsibility, Ethical Leadership, Organizational Climate and Employer-Employee Relations in a Trading Company

Workers are essential for organizational vitality, playing a crucial role in sustainable expansion. This study, anchored in social exchange theory, investigates corporate social responsibility, ethical leadership, organizational climate, and employer-employee relations in a Trading Company. Using quantitative, descriptive, comparative, and correlational design with a sample size of 162 out of 279 employees, the study reveals significant correlations among key factors. Despite the company's excellence, the researcher suggests there's room for enhancement.

Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

Investigating the knowledge and attitude of nurses regarding hypoglycemia of diabetic patients hospitalized in Marjan Teaching Hospital in 2022

Introduction and purpose: Diabetes is the most common disease caused by carbohydrate metabolism disorder that causes damage to many organs including kidney, eyes and heart. Increasing the knowledge and attitude of nurses can reduce the chronic complications of the disease. Therefore, the present research was conducted with the aim of determining the knowledge and attitude of nurses regarding hypoglycemia of diabetic patients admitted to Marjan Teaching Hospital in the year. Research method: In this cross-sectional descriptive study, 172 nurses working in Marjan teaching hospital in Babylon city were selected by available purposive sampling method. Information was collected using three parts of questionnaire, including first part of demographic data, and tow part about knowledge assessment, and last part of questionnaire to assess nurses' attitudes regarding hypoglycemia in diabetic patients. Findings: results of the study showed that among 172 participating nurses, the majority were women (55.8%), with the largest number falling into the age group of 20-29 years. Regarding knowledge assessment, nurses scored an average of 57.47 out of a total possible score ranging from 41 to 70, indicating a moderate level of knowledge about hypoglycemia in diabetic patients. Specifically, nurses in this department scored an average of 59.86% of knowledge points related to hypoglycemia. The average score among nurses regarding hypoglycemia in diabetic patients was 17.41 out of a possible range of 9 to 24, suggesting a generally positive attitude towards managing hypoglycemic episodes in diabetic patients. Conclusion: The study highlights the importance of improving nurses' knowledge and attitudes regarding hypoglycemia in diabetic patients. While most nurses showed moderate knowledge levels, there's room for enhancement, especially in managing hypoglycemic episodes effectively. Although nurses generally displayed a positive attitude, continuous education could bolster their confidence and skills in diabetic care, potentially reducing chronic complications.

Rearrangement Distance Problems: An updated survey

One of the challenges in the Comparative Genomics field is to infer how close two organisms are based on the similarities and differences between their genetic materials. Recent advances in DNA sequencing have made complete genomes increasingly available. That said, several new algorithms trying to infer the distance between two organisms based on genome rearrangements have been proposed in the literature. However, given the diversity of approaches, the diversity of genome rearrangement events, or even how each work models the genomes and what assumptions are made by each of them, finding the ideal algorithm for each situation or simply knowing the range of applicable approaches can be challenging. In this work, we review these approaches having the algorithmic and combinatorial advances since 2010 as our main focus. This survey aims to organize the recently published papers using a concise notation and to indicate the gaps filled by each of them in the literature. This makes it easier to understand what still needs to be done and what has room for enhancement.

HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation.

Both Convolutional Neural Networks (CNNs) and Transformers have shown great success in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer models to capture both local and global context interactions. However, there is still room for enhancement, particularly when considering constraints on computational resources. In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges. Specifically, we design a Hierarchy-Aware Pixel-Excitation (HAPE) module for adaptive multi-scale local feature extraction. During the global perception modeling, we devise an Efficient Transformer (ET) module streamlining the quadratic calculations associated with traditional Transformers. Moreover, a correlation-weighted Fusion (cwF) module selectively merges diverse feature representations, significantly enhancing predictive accuracy. HAFormer achieves high performance with minimal computational overhead and compact model size, achieving 74.2% mIoU on Cityscapes and 71.1% mIoU on CamVid test datasets, with frame rates of 105FPS and 118FPS on a single 2080Ti GPU. The source codes are available at https://github.com/XU-GITHUB-curry/HAFormer.

Community-Based Tourism Development in Kerangan Ecotourism Village, South Tangerang City

Community-based tourism development represents a facet of sustainable tourism. Keranggan Tourist Village's diverse potential attractions, facilities, and supportive communities are a prime example of an actively developing tourist village. While Keranggan applies community-based development principles, there is room for enhancement through more effective strategies. This research seeks to analyze the economic, social, and cultural dimensions of Community-Based Tourism (CBT) in the development of Keranggan Tourist Village. Drawing from reputable journals, the literature review method serves as the research approach. Economic indicators focus on implementing green entrepreneurship, while social indicators emphasize enhancing the performance of Pokdarwis. Cultural indicators encompass initiatives such as information exchange between local communities and guests, conducting comparative studies with other tourist villages, and providing tourism-related training for the community.

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column.

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

Research on optimization of forest fire early warning task by neural architecture search

We employed Neural Architecture Search (NAS) technology in this research paper and com-pared it with the classic convolutional neural network structures LeNet-15 and VGG (Visu-al Geometry Group)16. The objective was to optimize the performance of early warning forest fire image classification tasks. We adopted a public dataset "Forest Fire Early Warning 2 Clas-sification" from the Baidu PaddlePaddle platform, which comprises images of fires and no-fires under various environmental conditions. A convolutional neural network (CNN) model was automatically designed through AutoKeras and underwent 20 training epochs. In the ex-perimental results, NAS outperformed with an accuracy rate of 92%, surpassing LeNet-15 (83%) and VGG16 (49%). However, its training time was longer at 33 seconds, and GPU utili-zation was higher, ranging from 28% to 33%. Despite the room for improvement in training time and resource utilization, NAS has proven its superiority in complex image classification tasks due to its high accuracy. Although NAS has room for enhancement in terms of training time and resource utilization, its outstanding performance in the image recognition task of ear-ly forest fire warning shows great potential for future research and application.

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance.

Biochar/Mg-Al spinel carboxymethyl cellulose-La hydrogels with cationic polymeric layers for selective phosphate capture

Recently, biochar-related phosphate sorbents have been extensively investigated and achieved significant progress; however, there is still much room for enhancement on capturing performance and recovery of powdery ones after sorption. Herein, a new kind of adsorbent, in which biochar/Mg-Al spinel encapsulated in carboxymethyl cellulose-La hydrogels with cationic polymeric layers, was fabricated, aiming for integrating multi-advantages of each component for enhanced phosphate capture. Batch static experiments were correlated to the phosphate adsorption performance of the adsorbent. The maximum phosphate adsorption capacity of the adsorbent was 89.65 mg P/g at pH = 3. The Langmuir isotherm model and the pseudo-second-order kinetic model fitted well with the adsorption behavior of the adsorbent. More importantly, this composite adsorbent that integrated with biochar, Mg-Al spinel, cationic polymeric components exhibited favorable selectivity over coexisting anions (Cl-, SO42-, HCO3– and NO3–) and performed good reusability after five consecutive cycles. By virtue of the bead-like feature, fixed-bed column experiments demonstrated that the Thomas model fitted the breakthrough curves well under varied experimental conditions. The adsorption mechanism of phosphate on the designed composite adsorbent with multi-components could be described as the electrostatic attraction, ligand exchange and inner-sphere complexation, which might account for the efficient phosphate capturing performance.

In situ construction of Sn-doped structurally compatible heterojunction with enhanced interfacial electric field for photocatalytic pollutants removal and CO2 reduction

Fabrication of heterojunction photocatalysts is a promising strategy for achieving spatial charge separation, for which the interfacial electric field is the linchpin. However, the current density of interfacial electric field still have room for enhancement. In this study, we first demonstrated the regulation of interfacial electric field between bismuth semiconductors assisted by stannum (Sn) doping, raising the surface charge transfer efficiency from 27.25 % of BiOBr/BiOIO3 to 38.08 % of Sn-BiOBr/BiOIO3 composite. Correspondingly, the visible-light induced degradation rates towards tetracycline, 2, 4-chlorophenol and Rhodamine B exhibited 4.2-, 4.7- and 31.5-fold increase, respectively. Meanwhile, the CO2 photoreduction activity was also improved. The density functional theory calculation results unveiled that the Sn2+/Sn4+ redox couple could cause charge redistribution at interface and a distinctly unidirectional interfacial electric field with the direction from BiOIO3 to Sn-BiOBr was formed. This study provides a universal strategy for the design of bismuth-containing heterojunction with tunable interfacial electric field.

Analyzing the Newest Interchange Connection at Cairo Metro Network Pertaining to Passenger Flow

The French standard governing occupanckes dedicated for gatherings of individuals and institutions receiving the public, including railway stations and tunnels was used in the design of Cairo’s transit network, which consists of three lines connected through three existing interchange connections and one that is currently under construction. There is a lack of studies quantifying passenger evacuation in relation to Cairo transit, especially with regard to the interchange connections. This study assessed the viability of the evacuation methods applied to the design of interchange connections in Egypt. The emergency evacuation procedures for the future interchange connection layout between Lines 1 and 3 were assessed using the French standard and NFPA 130 Standard for Fixed Guideway Transit and Passenger Rail Systems analytical methods. The analysis accounts for the traffic forecast as presented in the Egyptian company for operation and maintenance report for Line 1 and the feasibility study of Line 3. The results demonstrated that the connection was compliant with the French standard, but it did not fully meet NFPA requirements. It was concluded that there is still room for enhancement and for gaps to be considered. Recommendations include undertaking investigations into passenger characteristics and behavior in the context of passenger flow in emergency situations using approaches such as virtual research and field surveys, and simulation techniques.

Reduced Leakage Synchrophasor Estimation: Hilbert Transform Plus Interpolated DFT

Synchrophasor estimation is typically performed by means of spectral analysis based on the discrete Fourier transform (DFT). Traditional DFT approaches, though, suffer from several uncertainty contributions due to the stationarity assumption, spectral leakage effects, and the finite-grid resolution. This paper addresses these limitations, by proposing a joint application of the Hilbert transform (HT) and the interpolated DFT (IpDFT) technique. Specifically, the HT enables the suppression of the spectral leakage generated by the negative image of the tones under analysis, whereas the IpDFT limits the effects of spectrum granularity. In order to relax the constraint in terms of measurement reporting latency, the proposed estimator can adopt a window length of 40 ms and yet provides a noticeable estimation accuracy with a worst-case total vector error and frequency error equal to 0.02% and 4 mHz, respectively, in steady-state conditions. In this context, this paper discusses the most suitable setting of the algorithm parameters and their effect on spurious component rejection. Moreover, a thorough metrological characterization of the algorithm estimation accuracy and responsiveness with respect to the IEEE Std. C37.118.1 is carried out in order to detect the main uncertainty sources as well as possible room for enhancement. Finally, a comparison with two consolidated IpDFT approaches shows the actual performance enhancement provided by the proposed algorithm.

Hereditary cancer prevalence and communication about family history risks in palliative care.

56 Background: Hereditary cancer (CA) assessment & communication about family history (FH) risks in palliative care are underexplored & could be critical for surviving relatives. FH may not be adequately reviewed & appropriate information conveyed in the palliative care (PC) setting, even though family health is a legacy concern of patients. Cancer patients seeking PC never wish for their relatives to have CA & may be an important resource for communication about their relative’s CA risk & appropriate referrals. Methods: This prospective observational study among cancer patients receiving PC consultations was conducted at a 977-bed academic urban hospital between June 2016 & March 2017. Data were collected through structured interviews and chart review during hospitalizations. Results: 75 adult cancer patients were enrolled. Patients’ average age at diagnosis was 55.3 ± 15.7 years. 41(55%) self-identified as male; 34(45%) as female; 20(27%) as black, 49 (65%) white, and 6(8%) other. 24 patients (32%) were considered to have pedigrees that merited referral as high-risk. 32 patients (43%) thought that their CA had a likely genetic or inherited component. 48(64%) thought the palliative care setting was an appropriate place to discuss the implications. The mean comfort level in addressing these questions was 8.7 on a 10 point scale with 10 indicating extremely comfortable. Conclusions: Review of FH may be an intervention that PC providers & families can discuss & incorporate in legacy activities. This could enhance CA prevention, especially among high-risk families. There is room for enhancement in how FH & patient and family concern are integrated in the palliative & cancer care continuum.

Science Parks Approaches to Address Sustainability

Multiple Helix Model is the theoretical framework where reciprocal university-industry-government relationship constitutes a key element of the innovation process. In the centre of this model arise the Science Parks (SPs) as hybrid agents that, having a close relationship with university, promote technology transfer and innovative entrepreneurship, with a legal framework defined by the public sector. The aim of this article is to analyse the link between SPs and sustainability under the hypothesis that they might become drivers of sustainability. A qualitative analysis of a sample of 12 Spanish SPs has been conducted. It concludes that SPs have high potential as sustainable knowledge facilitators and a proactive attitude in relation to environmental commitment, although there is still room for enhancements in their sustainability approaches. This study highlights the potential of STPs as facilitators of sustainable practices and also presents theoretical implications for future research linking Quintuple Helix Model and sustainable entrepreneurial strategies.

Cross-Connected Source Multilevel Inverter for Active Power Filtering Using Unified Constant-Frequency Integration Control

This paper proposes an Active Power Filter (APF) system which utilizes a five-level nonconventional Cross-Connected Source Multilevel Inverter (CCSMI) with a modified Unified Constant-Frequency Integration (UCI) control. The CCSMI consists of six power switches which can produce five output voltage levels. With the use of the CCSMI, the parts count is reduced with equivalent or better performance compared to the use of conventional multilevel inverters. The capability of the proposed system is tested using three types of nonlinear loads with different characteristics. The APF system is simulated under steady-state condition using MATLAB/Simulink. The results obtained show improvement in the supply current Total Harmonic Distortion (THD) with room for enhancement, using the proposed APF system.

Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network

Handwritten mathematical expressions recognition is yet challenging task due to its intricate spatial structure, tangled semantics and 2-dimensional layout of the characters. There is a still room for enhancement in recognition rate. Artificial neural network is superior to disentangle classification problems. In this paper, feedforward back propagation neural network is implemented to achieve both character recognition and mathematical structure recognition with upgrade in effective performance in addition to accuracy of the experimental results including lessen efforts. System proves its potency by recognizing expressions in analysis of math documents.