High Cost Research Articles

Road Traffic Crash-Related Musculoskeletal Injuries in Bafoussam, Cameroon: Exploring the Gaps in Physiotherapy Access and Utilization: A pilot study

Background: Road traffic crashes worldwide result in significant morbidity and mortality, with musculoskeletal injuries often neglected in primary management. This study aims to investigate the prevalence and management of Road Traffic Crash-Related Musculoskeletal Injuries (RTCMI) in Bafoussam, Cameroon. Methods: We conducted a prospective cross-sectional study in Bafoussam's hospital emergency units from January to June 2024. Using an exhaustive sampling, we enrolled 120 road traffic traumatic victims who provided informed consent to participate in the study. We excluded patients unable to communicate due to trauma status. Data analysis employed Excel 2016 with a 95% confidence interval. Results: Our study revealed a RTCMI prevalence of 73%, with fractures being the most common injury (80%). Notably, physiotherapy was not prescribed in primary management, and only 11% received Physiotherapy prescriptions in secondary care. A mere 17% of respondents requested physiotherapy, citing prescription failure (100%), lack of knowledge (83.6%), preference for traditional treatment (21.9%), and high costs (2.7%) as barriers. Conclusion: This study highlights the need for further research into RTCMI management and physiotherapy integration in Cameroon. Our findings suggest significant gaps in prescription, awareness, and accessibility, emphasizing the importance of scaling up this study to inform healthcare policy and practice.

An automated phenotyping method for Chinese Cymbidium seedlings based on 3D point cloud

Aiming at the problems of low efficiency and high cost in determining the phenotypic parameters of Cymbidium seedlings by artificial approaches, this study proposed a fully automated measurement scheme for some phenotypic parameters based on point cloud. The key point or difficulty is to design a segmentation method for individual tillers according to the morphology-specific structure. After determining the branch points, two rounds of segmentation schemes were designed. The non-overlapping part of each tiller and the overlapping parts of each ramet are separated in the first round based on the edge point cloud-based segmentation, while in the second round, the overlapping part was sliced along the horizontal direction according to the weight ratio of the tillers above, to obtain the complete point cloud of all tillers. The core superiority of the algorithm is that the segmentation fits the tiller growth direction well, and the extracted skeleton points of tillers are close to the actual growth direction, significantly improving the prediction accuracy of the subsequent phenotypic parameters. Five phenotypic parameters, plant height, leaf number, leaf length, leaf width and leaf area, were automatically calculated. Through experiments, the accuracy of the five parameters reached 98.6%, 100%, 92.2%, 89.1%, and 82.3%, respectively, which reach the needs of various phenotypic applications.

Sustainable Operation Strategy for Wet Flue Gas Desulfurization at a Coal-Fired Power Plant via an Improved Many-Objective Optimization

Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants.

Systemic administration of a potent mouse monoclonal antibody provides multisite protection against HPV16 infection

Although preventive vaccines for Human Papillomaviruses (HPV) are available, a definitive cure for the viral infection itself is currently lacking. There is a sizable population that remains inaccessible to HPV vaccination due to reasons such as high costs or lack of availability of the vaccines. Therefore, there remains a significant population susceptible to HPV infection. Persistent multisite infections with high-risk HPV types can cause cancer at several different anatomic sites.Especially HPV16 is a key etiologic factor for cervical, other ano-genital and oropharyngeal cancers. Therefore, it is imperative to develop pharmaceutical interventions for the treatment of viral infections. In this study, a panel of 9 neutralizing antibodies was screened using the hybridoma technique, with 20F6 being identified as the representative antibody. The purified 20F6 exhibited an IC50 of 0.0011 μg/ml against HPV16, demonstrating potent viral inhibitory activity. Moreover, it displayed cross-neutralizing efficacy towards other Alphapapillom 9 subtypes including HPV31, HPV33, HPV52, and HPV58 with respective IC50 values of 2.0 μg/ml, 7.3 μg/ml, 1.7 μg/ml, and 3.0 μg/ml. 20F6 recognizes the linear epitope MSLW, the first four amino-acids located at the very N-terminus of the HPV16 L1 protein. Administration of 20F6, 24 h prior to and following HPV16 pseudo-virion (PSV) challenge, conferred protection against infection in mice at doses as low as 1 mg/kg. Following intraperitoneal administration of 20F6, neutralizing antibodies were consistently detected at both oral and vaginal sites, indicating that prophylactic systemic administration of 20F6 may confer efficient protection against multiple susceptible mucosal sites.

Development of a questionnaire on high school teachers' perceptions of differentiated learning in schools

Differentiated learning is an approach that adapts to each student's learning needs. Teachers provide facilities that suit students' individual needs, bearing in mind that each student has unique characteristics and cannot be treated uniformly. This article aims to provide a deeper understanding of learning differentiation and explore teachers' perceptions regarding its implementation in the classroom. The results of interview research show that the purpose of differentiated learning is to coordinate learning by paying attention to students' interests, readiness and learning preferences. This aims to help all students achieve learning goals, increase motivation and learning outcomes, and establish a harmonious relationship between teachers and students, so that students are more enthusiastic about learning. In conclusion, differentiated learning gives students the opportunity to learn naturally and efficiently with the help of teachers who are able to combine the required methods and approaches. There are several obstacles that arise, such as limited facilities and infrastructure that support the differentiated learning process, teachers need time to prepare learning instruments, appropriate methods and appropriate media, implementing differentiated learning requires relatively high costs and teachers must have good classroom management skills.

Development and clinical application of bioelectrical impedance analysis method for body composition assessment.

Obesity, which is characterized by excessive body fat, increases the risk of chronic diseases, such as type 2 diabetes, cardiovascular diseases, and certain cancers. Sarcopenia, a decline in muscle mass, is also associated with many chronic disorders and is therefore a major concern in aging populations. Body composition analysis is important in the evaluation of obesity and sarcopenia because it provides information about the distribution of body fat and muscle mass. It is also useful for monitoring nutritional status, disease severity, and the effectiveness of interventions, such as exercise, diet, and drugs, and thus helps assess overall health and longevity. Computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry are commonly used for this purpose. However, they have limitations, such as high cost, long measurement time, and radiation exposure. Instead, bioelectrical impedance analysis (BIA), which was introduced several decades ago and has undergone significant technological advancements, can be used. It is easily accessible, affordable, and importantly, poses no radiation risk, making it suitable for use in hospitals, fitness centers, and even at home. Herein, we review the recent technological developments and clinical applications of BIA to provide an updated understanding of BIA technology and its strengths and limitations.

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN

Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and other available relevant methods have issues like low resolution, uncertainty and high cost, this study proposes a novel method by combining computational fluid dynamics (CFD), fluid-structure interaction (FSI), conditional generative adversarial network (cGAN) and convolutional neural network (CNN) to predict coronary artery occlusion risk using only noninvasive images accurately and rapidly. First, a cGAN model called WSSGAN was developed to predict the WSS contours on the vessel wall by training and testing the model based on the calculated WSS contours using coupling CFD-FSI simulations. Then, an 11-layer CNN was used to classify the WSS contours into three grades of occlusions, i.e. low risk, medium risk and high risk. To verify the proposed method for predicting the coronary artery occlusion risk in a real case, the patient’s Magnetic Resonance Imaging (MRI) images were converted into a 3D geometry for use in the WASSGAN model. Then, the predicted WSS contours by the WSSGAN were entered into the CNN model to classify the occlusion grade.

A Simulation-Based Approach for Evaluating the Effectiveness of Robotic Automation Systems in HMR Product Loading

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.

A system reliability demonstration test design method based on subsystem test data

AbstractReliability demonstration test (RDT) is the test to verify that the reliability of the product meets the reliability requirements during product development and is usually required to be performed to determine whether it can be accepted. It is essential for complex systems with higher costs. However, the traditional test plans such as the plan in the current RDT standard GJB‐899A have the problem of too long test time or too high risk. In addition, the test data of the complex system itself is usually less, but the test data of the subsystem is easier to obtain. In this paper, the research object is the system, and the main research content is how to use the reliability growth data in the development stage of the subsystem to design the system RDT. First, based on the reliability growth model, the test data in the development stage of the subsystems are modeled to obtain the reliability of each subsystem. The Bayesian theory is used to obtain the lifetime distribution of the system. Then, according to the definition of the two types of risks in the RDT, the life distribution of the product is integrated into the two types of risks, and the RDT plans of the system are designed. Finally, a simulation case is used to verify our RDT design method, and the results show that compared with the traditional plan, our RDT plan designed has a shorter test time and lower test risk.

Costing of nature-based vs grey solutions for water management: cases from Nairobi

Climate change adaptation in urban Africa is a critical development issue of this century. Informal and low-income neighbourhoods in African cities are particularly vulnerable as they are often located on flood pathways. Nature-based Solutions (NbS) have been put forward as an approach to enhance flood and climate resilience in urban settings. While social and environmental benefits of urban NbS are increasingly well documented, a key gap in the literature is robust data on the cost of implemented NbS, particularly from African cities. In this paper, we compare lifecycle costs of two significant implemented NbS projects against hypothetical equivalent grey solutions, in two different informal settlements in Nairobi, Kenya. We apply comparative Life Cycle Costing (LCC) for both cases and a Cost Benefit Analysis (CBA) for one case study where historical data is available. In one of the two cases NbS was significantly cheaper, while in the other higher costs were attributed to costs required to create understanding of the new approach. We find that the cost of any solution is highly context dependent, although the NbS approach shows competitive lifecycle costs, potential for cost-optimisation as the concept matures, and highly attractive for investment when the documented co-benefits are considered.

INFLUENCE OF MOISTURE CONDITION AND SILICA SAND ON FRICTION COEFFICIENT OF WIND TURBINE BRAKE SYSTEM

Major drive train parts for the horizontal wind turbines including the gearbox, generator, and brake system have very high repair and replacement costs and take a long time to complete. This paper investigates the impact of environmental air pollution on drivetrain tribology behaviors. Experimental research of the wind turbine's drive train system is simulated and investigated. The mechanical brake system of wind turbines is the subject of one case study chosen for this research paper. This research paper is examined how impurities affect the friction coefficient of the brake on wind turbines. According to the findings, the friction coefficient rises with each increase in wind turbine shaft speed. Also, it has been discovered that the size of the contaminants' particles significantly affects the friction coefficient. Furthermore, the findings may offer helpful insights of the drive train system that as humidity increased from 30 mm3 to 90 mm3, the coefficient of friction significantly decreased.

Nexus between Monetary Indicators and Bitcoin in Selected Sub-Saharan Africa: A Panel ARDL

The rapid adoption and growing prominence of Bitcoin and other cryptocurrencies have sparked significant interest and debate among economists, policymakers, and financial analysts. In Sub-Saharan Africa, where traditional financial systems often face challenges such as limited access to banking services, high transaction costs, and volatile currencies, Bitcoin presents both opportunities and risks. Understanding the interplay between Bitcoin and key monetary indicators such as monetary aggregates, exchange rates, and interest rates can provide valuable insights for policymakers and stakeholders in these economies. This study therefore seeks to investigate the nexus between monetary indicators and Bitcoin in selected Sub-Saharan African countries using a Panel ARDL (Autoregressive Distributed Lag) approach. The analysis focuses on understanding the dynamic relationship between key monetary variables, such as monetary aggregates, exchange rates, interest rates, and Bitcoin prices, from 2010 quarter three to 2022 quarter four. The findings reveal several significant relationships between monetary indicators and Bitcoin across the selected Sub-Saharan African countries. In the short run of the Panel Ardl monetary aggregates exhibit a positive relationship with Bitcoin prices, indicating that changes in the money supply may influence the demand for cryptocurrencies. Conversely, both exchange rates and interest rates show a negative relationship with Bitcoin prices in the short run, suggesting that currency depreciation and higher borrowing costs may reduce demand for Bitcoin. In the long run, the relationship between monetary aggregates and Bitcoin remains positive, emphasizing the potential influence of money supply on cryptocurrency markets over time. However, the significance of exchange rates diminishes, indicating a less pronounced impact in the longer term. Interestingly, interest rates continue to exhibit a significant negative relationship with Bitcoin prices in the long run, highlighting the persistent effect of borrowing costs on cryptocurrency demand. These results have important implications for policymakers, investors, and researchers interested in the intersection of monetary policy and cryptocurrency markets in Sub-Saharan Africa. Policymakers may consider the impact of monetary policy decisions on cryptocurrency adoption and market dynamics, while investors can use these insights to inform their investment strategies.

TRANSPORT SYSTEMS AS ESSENTIAL PART OF GOVERNMENT POLICY

The paper discusses the significance of transport systems as a crucial compone nt of public policy in the majority of countries worldwide. Transport policy aims to achieve various objectives, including promoting economic growth, enhancing accessibility, improving safety, reducing environmental impacts, and promoting social equity. A significant aspect is the substantial funding required to construct and maintain transportation infrastructure, includin g roadways, railway tracks, airports, harbors, and publictransportation systems.Special emphasis is placed on international transport corridors such as the Trans-Caspia n International Transportation Route, which complements the Chinese Belt and Road initiative and presents new opportunities for trade between China and European nations. The paper emphasize s the importance of collaboration between different levels of government and internationa l stakeholders to address cross-border transportation challenges and harmonize regulations. The authors also discuss the role of the Shanghai Cooperation Organization in the development of regional transportation infrastructure and logistics. They highlight key issues such as outdate d infrastructure, high transportation costs, low efficiency, and the need for increased transit capacity. Additionally, they discuss other international initiatives, such as the Eurasian Economic Union and Central Asian Regional Economic Cooperation that aim to improve regional connectivity and facilitate trade.In conclusion, they emphasize the importance of strategic planning and investment in transportation systems to promote sustainable economic growth and enhance transportation links between Central Asian countries and other regions of the world.

EnNet: Enhanced Interactive Information Network with Zero-Order Optimization

Interactive image segmentation extremely accelerates the generation of high-quality annotation image datasets, which are the pillars of the applications of deep learning. However, these methods suffer from the insignificance of interaction information and excessively high optimization costs, resulting in unexpected segmentation outcomes and increased computational burden. To address these issues, this paper focuses on interactive information mining from the network architecture and optimization procedure. In terms of network architecture, the issue mentioned above arises from two perspectives: the less representative feature of interactive regions in each layer and the interactive information weakened by the network hierarchy structure. Therefore, the paper proposes a network called EnNet. The network addresses the two aforementioned issues by employing attention mechanisms to integrate user interaction information across the entire image and incorporating interaction information twice in a design that progresses from coarse to fine. In terms of optimization, this paper proposes a method of using zero-order optimization during the first four iterations of training. This approach can reduce computational overhead with only a minimal reduction in accuracy. The experimental results on GrabCut, Berkeley, DAVIS, and SBD datasets validate the effectiveness of the proposed method, with our approach achieving an average NOC@90 that surpasses RITM by 0.35.

Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

The efficient and sustainable operation of the agricultural sector has become increasingly important in light of the transformations brought about by the third and fourth industrial revolutions. Population growth, increasing food demand, rising input costs, and environmental pressures necessitate innovative approaches not only to ensure food security but also to mitigate the effects of climate change. The European Union (EU) emphasizes the role of digital technologies in supporting agricultural productivity and resilience by promoting a bio-based economy. Strategies such as Farm to Fork (F2F) initiative aim to reduce pesticide and nutrient inputs, thus preserving biodiversity and supporting ecosystem health. Artificial intelligence (AI) and predictive analytics, along with connected sensors, offer opportunities to optimize water and nutrient usage and increase crop yields. By utilizing AI, combining remote sensing technologies, and monitoring changes in land use, it is possible to reduce environmental risks associated with agricultural practices. Although there are challenges such as high investment costs and data control for the integration of digital technologies, ongoing research and development efforts promise to overcome these obstacles. In conclusion, the integration of digital technologies into agriculture presents unique opportunities to address urgent issues and achieve sustainability goals. This review discusses the applicability of fundamental technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture (PA), and Machine Learning (ML) in making agriculture more efficient and sustainable, by enabling the perception, monitoring, collection, analysis, and extraction of meaningful insights from agricultural data.

The Transformation of Social Media in Arabic Language Learning: Opportunities and Risks in the Indonesian Context

This research examines the possibilities and dangers associated with using social media for learning Arabic in Indonesia. This work aims to address research difficulties by utilising library-based research methods, which involve analysing data obtained from various sources such as publications, papers, journals, scientific articles, and related books. Next, we will examine the potential and concerns associated with the evolution of social media in the context of Arabic language acquisition. The findings revealed that educational opportunities encompassed convenient access to information and learning resources, widespread availability of global connectivity and language exchange, innovative learning methods, global reach and interactive teaching, and enhanced ease and effectiveness of student-Arabic-teacher interaction. The risks associated with using social media in education include distractions from irrelevant content, exposure to inappropriate material that can have a negative impact on students, technical issues such as unstable internet connections, and high data costs, particularly in remote areas.

Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical Farming Adoption Among Food Retailers in France

This paper examines the potential of in-store vertical farming technologies in urban food retail as a solution to the increasing demand for fresh, locally sourced food. By enabling a “sell where you produce” model, vertical farming can reduce food miles, enhance traceability, and lower carbon emissions. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to analyze factors influencing the adoption of these technologies among food retailers in Rouen, France, with a particular focus on the role of risk aversion in shaping attitudes and decisions. The findings highlight that performance expectancy and facilitating conditions are the key drivers of adoption intentions among retail store managers. However, perceived risks, such as high investments and operational costs, pose significant barriers, while social influence appears to have little impact on their decisions. The study underscores the importance of conducting economic evaluations and risk assessments of vertical farming practices to ensure their long-term viability. As vertical farming holds the potential to boost fresh food production in urban areas, reduce food miles, and create sustainable supply chains, it is crucial to carefully weigh its benefits and risks to support its successful adoption in urban food retail.

Towards Hydraulic Design Optimization of Shaft Hydropower Plants: A 3D-CFD Application Based on Physical Models

The shaft hydropower plant (SHPP) is a novel hydraulic concept for low-head hydropower sites with several environmental and operational advantages over conventional layouts. However, the first two projects implementing this concept have shown comparatively high construction costs and project risks. Therefore, further optimization is required to increase economic attractiveness and enable broader market adoption. Initial model tests recommend a square-shaped shaft inlet with a three-sided approach flow for low-loss and fish-friendly inflow conditions. Yet, this design requires significant space for structural implementation and may be unsuitable for use with multiple shafts or as an extension of non-powered dams and weirs. This research paper presents the application of a computational fluid dynamics simulation setup to evaluate the hydraulic performance of various design configurations, especially alternative design layouts with a one-sided approach flow without further physical model tests. The simulation setup is calibrated against observations including head loss and velocity measurements from the physical model tests, and its satisfactory performance enables the analysis of alternative design layouts. This study aims to derive the most significant design parameters for achieving the desired hydraulic conditions at the intake. Increasing the flow depth before the intake and enlarging the inlet area have the most significant impact, while increasing the overflow of the front gate has the least significant effect. The chosen CFD application is deemed suitable for hydraulic design optimization and provides guidance on the key parameters to focus on for tailored site-specific design development.

Economic Assessment of Small-Scale Mountain Dairy Farms by Using Accounting Data: Evidence from an Italian Case Study

Dairy mountain farms are economically disadvantaged due to small farm sizes and high production costs. This situation was exacerbated firstly by the opening of the market linked to the abolition of milk quotas; secondly by the rising prices due to the energy crisis due to covid 19 and the war in Ukraine. However, these farms are important for the preservation of traditional landscapes, the economy of these areas and the offered ecosystem services, especially when they are managed extensively. The objective of this research is to understand if mountain dairy farms are economically sustainable and competitive through the analysis of a case study. The economic analysis takes into account the production cost of milk, profitability, and some economic indicators using accounting data. The results show that the farm profitability is decreasing, and the costs are unsustainable. As a consequence, more efficient policy support is needed to overcome this crisis.

Influence characteristic and improvement mechanism of thermal storage performance of Packed-bed thermocline tank based on high-temperature molten salt

Chloride salts and carbonates thermal energy storage (TES) have great application potential in high-temperature concentrated solar power (CSP) plants. However, their thermal storage characteristics, performance improvement mechanism and economic feasibility of application in high-temperature conditions are not clear. In this study, a transient, two-dimensional and axisymmetric model is developed for packed-bed thermocline tanks. The effects of six commonly solid fillers on thermal storage performance of NaCl-KCl-MgCl2 salt and Na2CO3-K2CO3-Li2CO3 salt at an operation temperature of 450–800 °C are simulated. The suggestions for optimizing fillers are provided combined with thermal and economic performance. Results show that high volume heat capacity and low thermal conductivity of fillers play important roles in deferring migration and weakening diffusion of thermocline, thus promoting thermal storage capacity and efficiency of tanks. Concrete and cast iron are suitable fillers for chloride salts and carbonates TES. The concrete tank is suitable for systems with strict cost control and low TES requirements. The iron tank is suitable for systems with high TES and low cost control requirements, which significantly increases effective thermal storage capacity of chloride salts by 222.1 % and considerably reduces TES cost of carbonates by 41.1 %. The results can be beneficial for high-temperature TES system of CSP plants.