Feasibility Of Application Research Articles

A Systematic Review of Techno-Economic, Environmental and Socioeconomic Assessments for Vibration Induced Energy Harvesting

There is a growing need to ensure the resilience of energy and water systems through digitalization, retrofit these systems for cleaner energy systems, and protect public safety in terms of water quality. This resilience requires a reliable power supply that could be provided by harnessing unexploited energy hidden in the current water infrastructure through the deployment of vortex-induced vibration energy harvesters. Therefore, being able to understand the feasibility of deploying these devices across technical, socioeconomic and environmental scales could further enhance successful deployment and integration of these devices. This paper aims to provide a systematic review investigating the development of energy harvester technologies to understand the key methods used to assess their application feasibility. This study used the PRISMA guidelines, and 139 articles were reviewed and synthesized. The trends were visualized, illustrating the current direction in energy harvesting development and application and methods used to assess the feasibility of these devices and technology. The majority of the reviewed studies focused on technical feasibility, design configuration, limitation, and identification of the most optimal application environment. The results revealed a huge opportunity for energy harvesters, especially as a power supply for monitoring sensors. Nevertheless, the results also identified a knowledge gap when it comes to assessing the overall application feasibility of energy harvesting as most studies currently neglect economic feasibility, environmental impacts, social aspects and energy resilience. Assessment tools will help fill this knowledge gap by identifying the key barriers and benefits gained from integrating this technology into existing energy systems and water systems.

The Role of Yeast in the Valorisation of Food Waste

The implementation of the circular bioeconomy is now widely accepted as a critical step towards reducing the environmental burden of industrial waste and reducing the impact of this waste on climate change. The valorisation of waste using microorganisms is an attractive and fast-developing strategy capable of achieving meaningful improvements in the sustainability of the biotechnology industry. Yeasts are a powerful chassis for developing valorisation strategies and key opportunities. Thus, this study examines how waste from the food sector can be effectively targeted for valorisation by yeast. Yeasts themselves are critically important elements in the production of food and brewing, and thus, the valorisation of waste from these processes is further reviewed. Policy and regulatory challenges that may impact the feasibility of industrial applications of yeast systems in the valorisation of food waste streams are also discussed.

Enhancing stability and explainability in reinforcement learning with machine learning

Abstract. In the field of reinforcement learning, training agents using machine learning algorithms to learn and perform tasks in complex environments has become a prevalent approach. However, reinforcement learning faces challenges such as training instability and decision opacity, which limit its feasibility in real-world applications. To solve the problems of stability and transparency in reinforcement learning, this project will use advanced algorithms like Proximal Policy Optimization (PPO), Q-DAGGER, and Gradient Boosting Decision Trees to set up reinforcement learning agents in the OpenAI Gymnasium environment. Specifically, the study selected the Atari game Breakout as the testbed, enhancing training efficiency and game performance by refining reward structures and decision-making processes, and integrating interpretable models to provide explanations for agent decisions. This study has successfully developed robust reinforcement learning agents that excel in complex environments. By employing advanced algorithms like PPO, Q-DAGGER, and Gradient Boosting Decision Trees, the study has addressed issues of training instability, and improved game performance through optimized reward structures and decision processes. Additionally, by integrating interpretable models, the study has provided insights into the learned strategies of the agents, thereby enhancing decision transparency. These findings provide crucial support for the broader application of reinforcement learning in real-world scenarios and offer valuable insights for tackling other complex tasks.

Virtual dissection applications in learning human anatomy: international medical students' perspectives.

Understanding human anatomy is fundamental to building medical competencies. This study explored the diverse perceptions of medical students in Saudi Arabia and Egypt concerning virtual dissection applications. Students from different Saudi and Egyptian governmental universities were invited. The online questionnaire covered the sociodemographic characteristics, students' perception of the advantages of virtual anatomy applications, satisfaction with their efficiency, and their recommendation to use them. Comparisons were performed in country-wise and then university-wise patterns. Significance was considered with a P < 0.05. The mean age of 401 participants was 20 ± 1.6. Females were 63% and the males were 37%. Saudi universities participated by 47% and Egyptian universities by 53% with various academic levels. The traditional program covers 25% of the participants and 75% are in integrated programs. Virtual dissection applications were used by 84% of the sample while the remaining 16% did not use them. No significant differences between Saudi participants and those from Egypt in the adopted teaching methods and the usual use of virtual dissection applications. However, significant differences in considering the ease of virtual applications (P = 0.016), their perfectness in lectures (P = 0.04), and their perception (P = 0.034). Also, there was no significant difference in satisfaction while their recommendation differed significantly (P = 0.034). The university-wise comparisons elaborated significant differences in the feasibility, reality, usefulness, preference, and perfectness of virtual dissection applications in lecturing. Technology can be an efficient complementary, not a replacement resource to support the educational process. Also, students' perceptions are valuable in maximizing the educational benefits of curricula.

Walnut shell/phenolic resin carbon electrodes via laser sintering and KOH activation

Currently, the application of 3D-printed biomass-based carbon electrodes is hindered by their suboptimal performance. This study utilizes walnut shell powder as a carbon source and employs Selective Laser Sintering (SLS) technology for the efficient 3D printing of a double-helix carbon precursor. High-performance biomass-based carbon electrodes are prepared through carbonization and KOH activation. Comparative studies on the microstructure and electrochemical performance of the carbonized and activated samples were conducted. Experimental results demonstrate that KOH activation significantly enhances the microstructure of the carbon electrodes, leading to marked improvements in their electrochemical performance. During desalination experiments, the carbon electrodes exhibited excellent ion removal efficiency, with a maximum conductivity reduction rate of 0.2 μS/cm·min, confirming their feasibility in seawater desalination applications. This research validates the potential of walnut shell biomass for the preparation of 3D-printed carbon electrodes, addressing bottlenecks in traditional carbon electrode manufacturing processes while promoting efficient biomass utilization and environmental protection.

Enhancing domain-specific text generation for power grid maintenance with P2FT.

The digitization of operation and maintenance in the intelligent power grid equipment relies on a diverse array of information for smart decision-making. In the domain of intelligent decision generation, proficiency is contingent upon extensive learning from copious amounts of text. This necessitates not only robust processing capabilities but also a high level of specialization. In addressing situations where authorization is lacking, pre-trained language models (PLMs) have already provided ideas when confronted with specialized domains or tasks. In consideration of the complexity of textual content in the field of the power grid, which encompasses a multitude of specialized knowledge and involves an abundance of proprietary terminology, we have undertaken an exploration of pre-trained model specialization using the power grid domain as an example, specifically for the task of generating maintenance strategies. A two-stage fine-tuning approach (P2FT) is employed, utilizing a large-scale pre-training model specifically designed for natural language processing. The efficacy and practical value of this method were evaluated through multiple metrics, juxtaposed with other advanced approaches involving low-parameter or parameter-free fine-tuning methods. Through a meticulous analysis and validation of experimental outcomes, we have corroborated the feasibility and practical application value of employing this approach for pre-trained model specialization. Additionally, it has furnished valuable guidance for text generation within both the Chinese language domain and the power grid domain.

Development of a Data-driven Decision Support System for the Efficient Operation of the Research Reactor Secondary System

The High-flux Advanced Neutron Application Reactor (HANARO) is a research reactor in the Republic of Korea. The secondary cooling system of HANARO releases heat into the atmosphere via cooling towers and fans. Because a part of the system is exposed to the external atmosphere, the behavior of the secondary cooling system is coupled with atmospheric conditions. However, no procedure exists to reflect such coupling, and the cooling fans are empirically controlled by the operators. This empirical decision-making may increase the workload and reduce operational efficiency; therefore, the cooling fan operational schemes must be improved. In this study, a decision support system is developed based on two artificial intelligence (AI) models to aid operators’ decision-making in controlling the cooling fans. During model development, data augmentation was applied to alleviate data imbalances. Additionally, a graphical user interface-based decision support system prototype was developed based on the models to provide operators with more comprehensive summaries of the information related to cooling-fan operations and outputs from the AI models. The models demonstrated acceptable performance on the testing data. The prototype system was installed in the main control room of HANARO and evaluated for its usability improvements and feasibility in practical applications.

Decorin-armed oncolytic adenovirus promotes natural killers (NKs) activation and infiltration to enhance NK therapy in CRC model

Colorectal cancer (CRC) is a prevalent malignant tumor of the gastrointestinal system, with the third and second highest incidence and mortality rates globally in 2020, respectively. Immunotherapy has developed rapidly in recent years. Natural killer (NK) cells have received increasing attention in the field of tumor immunotherapy due to their recognition and killing tumor cells without the limitations of major histocompatibility complexes. However, constraints within the tumor microenvironment that impede the infiltration and proliferation of NK cells result in poor efficacy of NK cell therapy for solid tumors. Oncolytic viral therapy is an immunogenic treatment with the potential to enhance anti-tumour immune responses and promote immune cell infiltration. In this study, we synergistically combine NK cells with an oncolytic adenovirus carrying Decorin (rAd.DCN) for the treatment of colorectal cancer (CRC) in a xenograft mouse model. By using Flow cytometry, real-time quantitative PCR and Calcein-AM release assay, we found that rAd.DCN could effectively promote proliferation, activation and degranulation of NK cells, up-regulate expression and secretion of NK cell killing activity-related factors, and enhance their killing activity. The efficacy is better than that of the blank control oncolytic virus rAd.Null. Combined treatment significantly inhibited tumor growth, increased the number of NK cells in peripheral blood, promoted the killing function of NK cells, and increased the expression levels of perforin and IFN-γ. At the same time, more NK cells were recruited to infiltrate tumor tissue. Our study established the feasibility of combination NK cells and oncolytic adenovirus application, thus expanding the scope of potentially curative treatments for NK cells in CRC.

Recent Patents on Long Distance Pneumatic Conveying Technology

Background: Pneumatic conveying is the use of air flow energy to transport granular materials in the direction of airflow in a closed pipeline, which involves the disadvantages of low conveying efficiency and easy deposition of particles. Objective: It is necessary to develop pneumatic conveying devices to reduce particle deposition, promote particles to enter the flow field again to accelerate, and achieve the effect of extending the pneumatic conveying distance. Method: The anti-settling device re-accelerates the particles so that the particles return to the flow field, which effectively reduces the probability of settlement blockage of material particles during transportation. The pneumatic conveying pressurized separator uses a pot-shaped housing to generate an internal rotating flow field to screen particles, and the light dust adheres to the filter, reducing the possibility of pipeline dust explosion. Results: The anti-settling device can quickly replace the anti-settlement block according to the parameters of the conveying pipeline, which can effectively reduce the probability of settlement blockage of material particles during transportation. The pressurized separator can use the air compressor to cooperate with the return air pipe to effectively remove the dust in the pneumatic conveying pipe and carry out secondary pressurized transportation of the materials in the pneumatic conveying pipe, which improves the safety of pneumatic conveying. Conclusion: The above technology improves the efficiency of pneumatic conveying and gas utilization, enhances the speed of particle conveying, reduces particle settlement and collision, extends the distance of pneumatic conveying, and ensures the safety of pneumatic conveying and the feasibility of industrial applications.

Capacity planning of storage batteries for remote island microgrids with physical energy storage with CO2 phase changes

Energy storage devices based on the CO2 thermal cycle (CTC) lose competitiveness in cold regions because of the decreased energy volume density and charge–discharge efficiency at low ambient temperatures. The CO2 hybrid thermal cycle (CHS) combines the CTC with the CO2 hydrate thermal cycle (CHT) to maintain a high energy volume density and charge–discharge efficiency regardless of the ambient temperature. The CHS-based energy storage system has been shown to have an energy volume density of 80–140 kWh/m3 and charge–discharge efficiency of 60 %–106 %. However, the economic feasibility of this system has not been considered. In this study, a numerical analysis was performed on the practical application and economic feasibility of CHS-based energy storage for the 100 % renewable energy microgrid of a pair of remote islands. In a case analysis of CHS installation in a microgrid for a remote island with 100 % renewable energy, the construction cost of CHS was 2/3 of that of pumped storage; the equipment cost reduction of CHS strongly depends on the type and amount of renewable energy to be interconnected, and therefore, the overall optimization of the power system is necessary.

Photovoltaic to electrolysis off-grid green hydrogen production with DC–DC conversion

Green hydrogen (H2), being the product of water electrolysis powered by renewable energy sources, is expected to be an energetic vector of major importance toward a more sustainable energy mix. In this context, photovoltaic (PV) -based H2 production is a key element, where power electronics technologies are critical to enable its development. In off-grid applications, designing DC–DC power electronics converters with high efficiency and high power density is really important since it can impact significantly the global performance of the H2 production system. In this work, a two-stage DC–DC power conversion system composed by an unregulated DCX converter and a regulated partial power converter is considered to increase the H2 production system efficiency. The DCX converter works in open-loop at resonant frequency with a high DC–DC conversion ratio. A partial power converter (PPC), which regulates only a fraction of the total power between its input and output terminals, represents a improvement in PV-to-H2 direct conversion, particularly since the PV panels do not require regulation from zero to nominal voltage, and in this work, is introduced for regulation of the DCX-based system, by optimizing the PV produced power through a maximum power point tracking (MPPT) algorithm along with the current control of the electrolyzer. A comparison with state-of-the-art converters show an important improvement of the proposed DCX with PPC regulated system. Compared to the solution with classical interleaved-buck pre-regulator, significant improvements in terms of efficiency for the whole range of operation are obtained, up to 2.5% higher with the proposed system. Experimental evaluation of the proposed PPC and DCX two stage converter for PV-powered electrolysis system is provided, validating its feasibility and interest for off-grid green hydrogen production application.

ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM

Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. As for the contribution of this research, a reliable method for stress identification has been suggested, using a random forest model to categorize stress levels from EEG signals. Data were filtered using a bandpass filter, Independent Component Analysis, and more so using the Z-score to remove outliers and poor signals. Data that has been cleaned from noise and outliers will go through a feature extraction process using Power Spectral Density (PSD). The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. Thus, the model obtained an accuracy level of 0.8571, thereby approving the tool’s efficiency in distinguishing between different degrees of stress. The computational efficiency of the model, with a classification time of 0.2762 seconds, demonstrates its feasibility for practical applications. Based on these findings, it can be concluded that the Random Forest algorithm can be used to integrate wearable technology and for offering suggestions and timely interventions for better mental health.

Fluorescence by incoherently pumped polar quantum dot placed in a low-frequency monochromatic field

Abstract We studied spectral properties of high-frequency fluorescent radiation from a two-level quantum system with broken inversion spatial symmetry, which can be implemented as a model of a one-electron two-level asymmetric polar semiconductor quantum dot whose electric dipole moment operator has permanent unequal diagonal matrix elements. The case of the excitation of this system by incoherent pumping of some sort supplemented with an additional driving monochromatic field, which frequency is much lower than the optical transition frequency of the quantum dot, was considered. An analytical expression for the fluorescence spectrum as a function of the amplitude and frequency of the monochromatic driving field, as well as of the pumping intensity, was derived. We also discussed feasible practical applications of the effects under study in the field of quantum optics and optoelectronics for the generation of high-frequency radiation, possessing stationary equidistant spectrum, and for the control of the radiative properties of such nanodevices as spasers (dipole nanolasers).

Three-Dimensional Printing Technology in Drug Design and Development: Feasibility, Challenges, and Potential Applications

Background/Objectives: The present investigation evaluates the impact of 3D-printing technology on the design of pharmaceutical drugs, considering the feasibility issues and problems concerning technological, pharmaceutical, and clinical matters. This paper aims to review how 3D printing can modify the traditional manufacturing of drugs with personalized medicine-therapy outcomes being individualized and optimized, hence improving patients’ compliance. Methods: The historical development of 3D printing from rapid prototyping to advanced pharmaceutical applications is discussed. A comparison is then made between traditional drug manufacturing approaches and the different techniques of 3D printing, including stereolithography, material extrusion, and binder jetting. Feasibility is assessed based on clinical trials and studies evaluating the efficacy, safety, bioavailability, and cost-effectiveness of 3D-printed drugs. Results: Current evidence indicates that material selection, regulatory barriers, and scalability issues are some of the major challenges to be overcome for wider acceptance. Other matters, such as ethical issues concerning patient data privacy, the misuse of 3D-printing technology, and technical complexities related to pharmaceutical 3D printing, are discussed further. Future applications also include bioprinting and in situ printing together with their implications for personalized drug delivery, which will also be discussed. Conclusions: This review stresses that intersectoral collaboration and the updating of regulatory frameworks are a must to overcome the barriers that confront 3D-printing applications in drug development. can could be an opportunity for innovative licensing and manufacturing techniques in pharmaceutical product development that can change the paradigm of personalized medicine through modern printing techniques.

Asphalt pavement raveling recognition based on deep learning algorithms

Abstract Pavement raveling can lead to the development of issues such as potholes. Monitoring and providing real-time early warnings for road surface developments is crucial for timely maintenance planning and effectively controlling the occurrence and progression of surface issues. To achieve intelligent identification of raveling distress on asphalt pavement surfaces, we utilized a portable laser-based pavement surface scanning device to collect raveling images, followed by histogram equalization to eliminate noise caused by environmental factors. Additionally, data enhancement strategies such as rotation and mirroring were used to enrich training data and expand the sample size. A Convolutional Neural Network (CNN) was employed to perform multi-layer convolution and pooling operations on the images to quickly extract features of distress. Finally, five pre-trained transfer learning models—ResNet-18, DenseNet, VGG16, Inception, and EfficientNet—were introduced to identify the asphalt surface disease data and the normal surface data. The results showed that the accuracy and loss of the training sets of several models exhibited a positive trend, with the EfficientNet model demonstrating stable and superior performance under the same training conditions. The precision matrix, confusion matrix, precision, recall, and F1 score were used as evaluation indexes to assess the effectiveness of the models for identifying pavement raveling. The EfficientNet and Inception models had the best accuracy and comprehensive performance, with accuracy rates of 0.96 and 0.95, respectively. The precision, recall, and F1 score are 0.95, 0.96, and 0.96 for the EfficientNet model respectively. A total of 146 images correctly predicted raveling disease and only 4 normal images were incorrectly predicted as diseased. For example, the EfficientNet model’s identification accuracy for pockmark disease on the PC platform was 0.963, verifying the model’s feasibility in practical applications. The principal contribution of this paper lies in the efficient and precise identification of asphalt pavement raveling, which adversely affects vehicular traveling comfortability. This provides theoretical and technical support for the intelligent identification of asphalt pavement defects and enhancing road asset maintenance management in the pavement engineering industry.

Evaluation of three kinetic energy storage systems

Abstract. This paper investigates recent advances in energy recovery systems (ERS) in automotive vehicles to reduce air pollution and impact on climate change. The three ERS systems: mechanical flywheel, regenerative braking, and regenerative electrically assisted (REAT) turbocharger are evaluated for their potential to reclaim energy wasted by the automobile thereby increasing range and efficiency of the vehicle. The methods of comparison are based on data sourced from current literature relevant to total energy recovery, system configuration, and application feasibility. This will identify regenerative braking as best for new vehicles to decrease fuel consumption and regenerative electrically assisted turbocharges (REAT) as best for retrofitting older vehicles. The paper will use the results to identify the energy recovery system with the greatest potential for immediate application and further research to overcome remaining challenges.

Research on the Feasibility of Machine Learning Methods in Stock Price Prediction

Abstract: Stock price prediction has always been an important topic in financial research. Accurate price prediction can not only help investors make informed investment decisions but also enhance market stability and reduce systemic risk. In recent years, with advancements in computing technology and data science, machine learning methods have been increasingly applied in the financial field. Compared to traditional methods, machine learning methods can better handle high-dimensional, nonlinear, and large datasets, thus demonstrating higher prediction accuracy and applicability in stock price prediction.This paper reviews relevant literature and selects five models for empirical research: Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), LightGBM, a combination of LSTM and LightGBM, and Convolutional Neural Network (CNN). The effectiveness of these models in predicting the stock price of Meituan-W (3690) was analyzed and compared in detail. The experimental results show that the LightGBM model performs best in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), proving its significant advantages in handling large-scale, high-dimensional, and nonlinear data. By comparing the prediction results of different models, this paper explores the strengths and weaknesses of each model and their feasibility and effectiveness in practical applications. Machine learning methods have significant potential in stock price prediction, model selection needs to comprehensively consider data characteristics, computational resources, and practical application scenarios.

Large language models for structured reporting in radiology: past, present, and future.

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.

Critical Review on Carbon Nanomaterial Based Electrochemical Sensing of Dopamine the Vital Neurotransmitter

The clinical diagnosis of dopamine biomarkers plays a crucial role in classifying nervous system-related disorders, which are increasingly prevalent across all age groups worldwide. Accurate and thorough diagnosis is essential for administering appropriate drug therapies. However, it has been observed that there is a scarcity of diagnostic methods available in the market, highlighting a significant demand for such tools, particularly as the healthcare system transitions towards personalized medicine. This growing demand has garnered significant attention from researchers working in diagnostics. It is of great therapeutic and pharmacological significance to design and develop diagnostic instruments for the monitoring of dopamine levels both in vivo and in vitro. Extensive research efforts have been dedicated to devising realistic diagnostic techniques for assessing dopamine levels in bodily fluids, with a particular focus on electrochemical sensing methodologies. While studies related to electrochemical sensing of dopamine have shown promising advancements in terms of simplicity, speed, and sensitivity, there remains a notable gap in their application for clinical studies. Thus, this review aims to provide an overview of the latest progress in non-enzymatic (enzyme-free or direct electrochemical) electrochemical sensing of dopamine, specifically focusing on its integration with carbonaceous nanomaterials in electrodes. Additionally, the review discusses the potential for the commercialization of these laboratory-proven techniques soon, emphasizing their feasibility and practicality in real-world applications.

Exploring the black box of human reproduction: endometrial organoids and assembloids - generation, implantation modeling, and future clinical perspectives.

One of the critical processes in human reproduction that is still poorly understood is implantation. The implantation of an early human embryo is considered a significant limitation of successful pregnancy. Therefore, researchers are trying to develop an ideal model of endometrium in vitro that can mimic the endometrial micro-environment in vivo as much as possible. The ultimate goal of endometrial modeling is to study the molecular interactions at the embryo-maternal interface and to use this model as an in vitro diagnostic tool for infertility. Significant progress has been made over the years in generating such models. The first experiments of endometrial modeling involved animal models, which are undoubtedly valuable, but at the same time, their dissimilarities with human tissue represent a significant obstacle to further research. This fact led researchers to develop basic monolayer coculture systems using uterine cells obtained from biopsies and, later on, complex and multilayer coculture models. With successful tissue engineering methods and various cultivation systems, it is possible to form endometrial two-dimensional (2D) models to three-dimensional (3D) organoids and novel assembloids that can recapitulate many aspects of endometrial tissue architecture and cell composition. These organoids have already helped to provide new insight into the embryo-endometrium interplay. The main aim of this paper is a comprehensive review of past and current approaches to endometrial model generation, their feasibility, and potential clinical application for infertility treatment.