Room For Further Enhancement Research Articles

The Influence of Shared Leadership on the Innovative Behavior of New Generation Faculty

This quantitative study examined the influence of shared leadership on the innovative behavior of the new generation faculty within the context of Chinese higher education institutions. Conducted across various universities in Guangdong Province, China, the research engaged a sample of 340 new generation faculty members. Utilizing a survey method, the study evaluated the faculty’s perceptions of shared leadership, encompassing dimensions such as team progress, authority and responsibility sharing, team expectations, and team contract. Additionally, the survey captured data on the innovative behaviors of faculty, including innovation discovery, innovation proclivity, innovation reinforcement, and innovation cooperation. The findings indicated that the new generation faculty placed significant value on shared leadership aspects, particularly team progress and team expectations. The study revealed that the faculty demonstrated a commendable level of innovative behavior, yet there was room for further enhancement in both shared leadership practices and innovative actions. The research established that a combination of career values related to shared leadership significantly predicts the professional development of faculty in terms of innovation. The study’s implications are vital for educational administrators and policymakers, emphasizing the necessity to foster shared leadership environments conducive to nurturing faculty innovation. By recognizing the predictive role of shared leadership in shaping innovative behaviors, educational institutions can strategize more effectively to promote academic excellence and drive educational reforms.

Simulation and Experimental Study on Heat Transfer Performance of Bionic Structure-Based Battery Liquid Cooling Plate

This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery’s heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate’s structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study’s outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities.

Aircraft Skin Damage Visual Testing System Using Lightweight Devices with YOLO: An Automated Real-Time Material Evaluation System

Inspection and material evaluation are some of the critical factors to ensure the structural integrity and safety of an aircraft in the aviation industry. These inspections are carried out by trained personnel, and while effective, they are prone to human error, where even a minute error could result in a large-scale negative impact. Automated detection devices designed to improve the reliability of inspections could help the industry reduce the potential effects caused by human error. This study aims to develop a system that can automatically detect and identify defects on aircraft skin using relatively lightweight devices, including mobile phones and unmanned aerial vehicles (UAVs). The study combines an internet of things (IoT) network, allowing the results to be reviewed in real time, regardless of distance. The experimental results confirmed the effective recognition of defects with the mean average precision (mAP@0.5) at 0.853 for YOLOv9c for all classes. However, despite the effective detection, the test device (mobile phone) was prone to overheating, significantly reducing its performance. While there is still room for further enhancements, this study demonstrates the potential of introducing automated image detection technology to assist the inspection process in the aviation industry.

Assessing the Transparency of Methods in Scientific Reporting

Enhancing transparency in scientific reports is crucial to foster trust, facilitate reproducibility, and ensure the integrity of research, ultimately advancing the progress of knowledge and innovation. To devise strategies for enhancing transparency in scientific reports, the initial step is to assess the current state: to objectively measure the current level of transparency, identifying its shortcomings and associated factors, and to gauge improvements, for instance, following interventions. Here we present a new tool and a proof of concept to this endeavor. Using a checklist, we evaluated the methods transparency of a corpus of 180 papers published in 2011 and 2021 in five top-tier psychology journals. We specifically focused on the materials, procedures, and characteristics of population and sample. We summarized the level of transparency in the methods of each paper with the Transparency Of Methods (TOM) score. This score consists of the proportion of relevant information regarding the method that is available to the reader of a scientific report, either in the main text of the paper, or the appendixes, supplementary materials, and online open repository. It ranges from 0 (i.e., no transparency in the relevant aspects of the methods and materials) to 1 (i.e., the scientific report is fully transparent in all the relevant materials and methods). The results affirm TOM’s potential for assessing the transparency of scientific reporting and offer two snapshots of transparency in the methods of published papers, a decade apart. While they indicate progress has been made, there remains room for further enhancements and highlight specific areas that require attention. In conclusion, this work underscores the ongoing need for improvement in methods’ transparency and introduces a valuable tool, demonstrating its applicability as a means to evaluate the transparency of scientific reports.

Selective Cellular Uptake and Druggability Efficacy through Functionalized Chitosan-Conjugated Polyamidoamine (PAMAM) Dendrimers.

Nanotechnology has ushered in significant advancements in drug design, revolutionizing the prevention, diagnosis, and treatment of various diseases. The strategic utilization of nanotechnology to enhance drug loading, delivery, and release has garnered increasing attention, leveraging the enhanced physical and chemical properties offered by these systems. Polyamidoamine (PAMAM) dendrimers have been pivotal in drug delivery, yet there is room for further enhancement. In this study, we conjugated PAMAM dendrimers with chitosan (CS) to augment cellular internalization in tumor cells. Specifically, doxorubicin (DOX) was initially loaded into PAMAM dendrimers to form DOX-loaded PAMAM (DOX@PAMAM) complexes via intermolecular forces. Subsequently, CS was linked onto the DOX-loaded PAMAM dendrimers to yield CS-conjugated PAMAM loaded with DOX (DOX@CS@PAMAM) through glutaraldehyde crosslinking via the Schiff base reaction. The resultant DOX@CS@PAMAM complexes were comprehensively characterized using Fourier-transform infrared (FTIR) spectroscopy, transmission electron microscopy (TEM), and dynamic light scattering (DLS). Notably, while the drug release profile of DOX@CS@PAMAM in acidic environments was inferior to that of DOX@PAMAM, DOX@CS@PAMAM demonstrated effective acid-responsive drug release, with a cumulative release of 70% within 25 h attributed to the imine linkage. Most importantly, DOX@CS@PAMAM exhibited significant selective cellular internalization rates and antitumor efficacy compared to DOX@PAMAM, as validated through cell viability assays, fluorescence imaging, and flow cytometry analysis. In summary, DOX@CS@PAMAM demonstrated superior antitumor effects compared to unconjugated PAMAM dendrimers, thereby broadening the scope of dendrimer-based nanomedicines with enhanced therapeutic efficacy and promising applications in cancer therapy.

Revolutionizing Alzheimer’s detection: an advanced telemedicine system integrating Internet-of-Things and convolutional neural networks

The advent of Internet-of-Things (IoT)-based telemedicine systems has ushered in a new era of technology facilitating early diagnosis and prevention for distant patients. This is particularly crucial for severe illnesses such as Alzheimer's disease, encompassing memory loss and cognitive dysfunction that significantly impairs daily life, necessitating immediate medical attention. The surge in data from intelligent systems, sourced from diverse locations, has heightened complexity and diminished diagnostic accuracy. In response, this study proposes an innovative distributed learning-based classification model, leveraging a deep convolutional neural network (CNN) classifier. This model proficiently manages clinical data images from disparate sources, ensuring disease classification with high accuracy. The research introduces a novel system designed for automated Alzheimer's disease detection and healthcare delivery. Comprising two subsystems, one dedicated to Alzheimer's diagnosis with an impressive 94.91% accuracy using CNN, and another for healthcare treatment, delivering excellent results. Notably, the system is adaptable to various diseases post-training. The study emphasizes the model's robust performance, achieving an outstanding 94.91% accuracy after 200 training epochs, with a loss of 0.1158, and a validation accuracy of 96.60% with a loss of 0.0922 at training without noise and loss: 0.2938 - Accuracy: 0.8713 - val_loss: 0.2387 - val_accuracy: 0.9069 at CNN with noise. Precision, recall, and F1 scores are comprehensively presented in a classification report, underscoring the system's effectiveness in categorizing Mild Demented and Non-Demented cases. While acknowledging room for further enhancements, this study introduces a promising avenue for telemedicine systems. It significantly impacts the early diagnosis and treatment of Alzheimer's disease and related medical conditions, thereby advancing the healthcare sector and improving patients' quality of life. The inclusion of these quantitative results enhances the abstract's appeal to readers, providing a clearer understanding of the study's outcomes.

The Transformative Power of Artificial Intelligence in Banking Client Service

The financial industry is experiencing a paradigm shift driven by Artificial Intelligence (AI). This manuscript delves into the profound impact of AI on banking client services, exploring how it revolutionizes the industry. As banks strive to elevate customer experiences, reduce operational costs, and maintain their competitive edge, AI emerges as a critical enabler. This study employs a mixed-method research approach to comprehensively investigate the impact of Artificial Intelligence (AI) on client service in the banking sector. To test the hypothesis, we conducted a regression analysis with customer satisfaction as the dependent variable and AI integration and operational efficiency as independent variables. The results demonstrate a statistically significant relationship between AI integration and customer satisfaction (β = 0.632, p < 0.001). The research reveals that clients perceive a moderate level of AI integration into banking operations, with room for further enhancement. Notably, clients express high satisfaction with AI-enhanced services, underscoring its positive influence, as corroborated by existing literature. Recommendations emphasize strategic actions to maximize AI's potential: augmenting AI integration beyond chatbots to encompass predictive analytics and fraud detection, initiating customer education programs to familiarize clients with AI-powered services, prioritizing data privacy and security measures, and providing comprehensive staff training in AI ethics and customer interaction. In conclusion, "The Transformative Power of Artificial Intelligence in Banking Client Service" serves as a timely guide for banking professionals, policymakers, and researchers eager to harness AI's potential for the betterment of the financial industry and its clients. This research paints a compelling picture of how AI is reshaping the future of banking, offering insights into its transformative capacity. As AI continues to evolve, this manuscript provides a roadmap for financial institutions, highlighting the need to adapt, collaborate, and strategize effectively to thrive in the rapidly changing landscape.

Evolution of web tracking protection in Chrome

In our society, protecting users’ privacy is of utmost importance, especially when users access websites. Increased awareness of privacy concerns has led web browsers to implement new mechanisms to improve privacy while browsing the Internet. In each new version of web browsers, it is claimed that they provide better improvements to protect our privacy. However, there is no analysis of these improvements. To cope with this issue, in this paper, we present an analysis of the privacy of different versions of the Chrome web browser. This analysis is based on the PrivacyScanner tool, which we have improved with the detection of additional tracking techniques. Our findings reveal that tracking protection has seen modest enhancements (namely, between Chrome version 83 and 90, we observed a 7.55% reduction in trackers and 4.76% decrease in Google Analytics elements). Therefore, despite these improvements, there is still ample room for further enhancement.

Sharia boards, managerial strategies and governance practices in Islamic banks: a Goffmanesque discourse

PurposeThe study applies Erving Goffman's (1974) “frame analysis” principles to examine how Sharia governance is practiced in Islamic banks and explores the interaction and strategies adopted by bank managers to influence the decisions of Sharia scholars. The study also aims to identify inherent flaws in the Sharia compliance review system.Design/methodology/approachThe study employs the principles of Goffman as a lens to critically analyse a rich dataset obtained through interviews undertaken with 46 key players operating in the governance framework of the Malaysian Islamic banking industry due to its progressive Islamic governance framework.FindingsThe study demonstrates that managers of Islamic banks may engage in “passing” and “covering” strategies while interacting within the governance structure. Concurrently, Sharia boards (SBs) implement “protective practices” during their interactions, adding complexity to their responsibilities within the banks. Consequently, SBs cannot merely be viewed as instruments for legitimising banking operations. This raises questions about the “impression management,” “concealment” and “competence” strategies employed by managers and SB members, as suggested by Goffman's framework. These findings indicate that there is room for further enhancement in the governance practices of Islamic banks.Research limitations/implicationsFuture research could explore aspects related to the governance of Islamic banks, such as investigating the independence and effectiveness of internal Sharia officers. Examining the strategies employed during their interactions with external Sharia boards and other stakeholders could provide further valuable insights.Practical implicationsBy highlighting shortcomings in the governance and compliance review process, the findings could serve as a valuable resource for policymakers. The insights derived could inform the development of regulations aimed at reducing opportunistic behaviour and promoting accountability in the Islamic banking sector.Originality/valueThis study uniquely employs Goffman's concepts of “frontstage” and “backstage” strategies to offer insights into the interactions between Islamic bank managers and SBs and the impact of these interactions on Sharia compliance. The study contributes to the understanding of the dynamics between key players in the governance of Islamic banks and the factors influencing their adherence to Sharia principles.

Smile Aesthetics Assessment in Patients Undergoing the Finishing Phase of Orthodontic Treatment: An Observational Cross-Sectional Study.

Objective To describe the smile characteristics of patients entering the finishing phase of orthodontic treatment. Methods This observational study involved a non-probabilistic sample of 48 patients. Clinical records served as the basis for determining the type of treatment (with or without extractions). Photographs were analyzed to obtain smile variables. Dental casts and panoramic radiographs were evaluated to ascertain the cast-radiograph evaluation (CRE) index. Univariate and bivariate analyses were conducted at a significance level of 0.05. Results The study evaluated 24 men and 24 women, with an average age of 20.10 ± 6.78 years. Fifty percent of the patients did not undergo extractions, and the average CRE index for the sample was 34.83 ± 9.01. Regarding the smile, a medium smile line was prevalent in 66.7% of cases, and a non-consonant smile arc was observed in 58.3%. Significant differences in the smile arc were found between patients with and without extractions (p=0.019). Right and left buccal corridors measured 2.52 mm ± 1.52 and 2.43 mm ± 1.37, respectively. The upper dental midline deviated by 0.80 ± 0.91 mm and had an angulation of 1.65 ± 2.05º. Both variables showed significant differences between Class I and Class II patients (p=0.020; p=0.027). Symmetrical smiles were also observed (1.05 ± 0.17). Conclusions Based on our findings, clinicians should focus on the smile arc in patients who have not undergone extractions and on the midline inclination in Class II patients. These appear to be the most common areas for improvement in patients who are in the finishing phase of treatment. Additionally, considerable variability exists in the smile characteristics of patients still undergoing orthodontic treatment, leaving room for further enhancement of results.

Finite element skin models as additional data for dynamic infrared thermography on skin lesions

ABSTRACT Skin cancer is a significant global health concern, with increasing incidence rates and a high number of deaths each year. Early detection plays a crucial role in improving survival rates, but current screening methods, such as total body skin examination, often lead to unnecessary invasive excisions. This research aims to explore the use of dynamic infrared thermography (DIRT) in combination with other technologies to potentially eliminate the need for biopsies in the future and gather information about the stage or depth of malignant skin lesions. The study involves data acquisition using a thermal camera and a finite element skin model. The FEM skin model employed in this research follows the commonly used five-layer model and is constructed in Siemens Simcenter 3D to be able to simulate the cryogenic cooling on the skin. It is possible to improve the thermal images by choosing an appropriate cooling method, cooling sequence and optimised measurement setup. While the FEM skin model shares certain similarities with the measurement data, there is room for further enhancements to optimise its performance. The acquired data is analysed to assess the effectiveness of the combined technique compared to existing clinical and diagnostic methods.

An effective fusion positioning methodology for land vehicles in GPS-denied environments using low-cost sensors

It is still a main challenge to achieve accurate and reliable vehicle positioning in Global Position System (GPS)-denied environments using low-cost sensors. Although existing methods have obtained a certain performance improvement, there is still room for further enhancement in positioning accuracy due to the lack of effective observation information during GPS outages. To address this challenge, this paper proposes an effective fusion positioning methodology based on enhanced observation information. Firstly, a data denoising algorithm based on convolutional denoising autoencoder is developed to effectively filter the noises in inertial sensors, so as to provide more clean data for subsequent modeling. Then, a differential fusion strategy is designed to selectively fuse multiple observation information from monocular camera, low-cost GPS and wheel speed sensor with extended Kalman filter, which can further improve the positioning accuracy. Finally, a long short-term memory-based error prediction model is constructed to learn the mapping relationship between the result of data denoising and differential fusion. In the case of GPS outages, the error model provides accurate corrections to compensate for position errors of inertial navigation system. The performance of the proposed methodology was evaluated on real-world data collected in complex urban environments. Experimental results indicate that the proposed positioning methodology can obtain significant accuracy enhancement.

Spiro-OMeTAD-Based Hole Transport Layer Engineering toward Stable Perovskite Solar Cells.

Perovskite solar cells (PSCs) have undergone unprecedented growth in the past decade as an emerging photovoltaic technology. Up till now, the power conversion efficiency of PSCs has exceeded 25% that rivals silicon solar cells and there is still room for further enhancement. However, the development in long-term stability lags far behind, which remains a great concern for the commercial application in the future. The device instability mainly arises from the functional components, including perovskite film, charge transport layers, and electrodes along with the involved interfaces. As the most widely studied hole transport layer at the current stage, 2,2',7,7'-tetrakis(N,N-di(4-methoxyphenyl)amino)-9,9-spirobifluorene (Spiro-OMeTAD) helps contribute to the achievement of record efficiency but it weakens the device stability due to the doping-induced side effects such as hygroscopicity and ion migration. Great efforts are devoted to boosting the stability of Spiro-OMeTAD while maintaining excellent photovoltaic performance. In this review, the fundamental properties of Spiro-OMeTAD have been summarized and the recent advances in engineering Spiro-OMeTAD-based hole transport layer for the sake of highly efficient PSCs with enhanced longevity are highlighted. In the end, an outlook for the further optimization of Spiro-OMeTAD is provided and the issues related to large-scale production are discussed.

Da li se obraćamo zidovima ili prenosimo znanje studentima - prednosti i nedostaci online učenja

In the era of digitalization, boosted by Covid-19 pandemic, there is a constant need to measure the success of digital education, compare and reappraise its positive and negative outcomes, which are mainly dependent on the technical conditions available, as well as on the extent to which the previous knowledge of teachers has been adapted to practical and technical challenges. The aim of this paper is to present an analysis of the advantages and disadvantages of digitized education as perceived by students in a qualitative survey, conducted in 2021/22. The starting point were two hypotheses related to positive aspects and two hypotheses related to negative aspects of online learning. An in-depth interview was conducted among 20 high school and 20 university students. By analyzing the results obtained, and juxtaposing them with the data taken from a quantitative survey administered prior to the qualitative one, in 2020/21, it is possible to compare the changes that took place and gain a better insight into the entire process, which has undergone certain improvements, but which also leaves room for further enhancement, particularly in terms of students' and teachers' motivation to actively engage and interact. This would help students overcome their distrust of online education.

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

Ultrahigh emissivity of grating-patterned PDMS film from 8 to 13 μ m wavelength regime

Polydimethylsiloxane (PDMS) is a prominent material for radiative cooling due to its promising optical properties in the mid-infrared spectral region as well as its fabrication easiness. Even though several works have reported that the mid-infrared emissivity of a PDMS film can be increased by surface modification, there is still room for further enhancement through global optimization. Here, we designed and fabricated the thin PDMS film patterned with two-dimensional gratings to obtain the highest emissivity in the wavelength range from 8 to 13 μm. A surrogate-model-based optimization was performed, and the optimum structure exhibited the averaged emissivity value of 0.99 in the wavelength of 8–13 μm, which is the highest value reported to date among polymer-based radiative coolers. For real-world applications, we also developed the fabrication method that is repeatable and applicable for various surfaces using a flexible master mold.

Constrained Multiobjective Optimization: Test Problem Construction and Performance Evaluations

Constrained multiobjective optimization abounds in practical applications and is gaining growing attention in the evolutionary computation community. Artificial test problems are critical to the progress in this research area. Nevertheless, many of them lack important characteristics, such as scalability and variable dependencies, which may be essential in benchmarking modern evolutionary algorithms. This article first proposes a new framework for constrained test problem construction. This framework splits a decision vector into position and distance variables and forces their optimal values to lie on a nonlinear hypersurface such that the interdependencies can be introduced among the position ones and among the distance ones individually. In this framework, two kinds of constraints are designed to introduce convergence-hardness and diversity-hardness, respectively. The first kind introduces infeasible barriers in approaching the optima, and at the same time, makes the position and distance variables interrelate with each other. The second kind restricts the feasible optimal regions such that different shapes of Pareto fronts can be obtained. Based on this framework, we construct 16 scalable and constrained test problems covering a variety of difficulties. Then, in the second part of this article, we evaluate the performance of some state of the art on the proposed test problems, showing that they are quite challenging and there is room for further enhancement of the existing algorithms. Finally, we discuss in detail the source of difficulties presented in these new problems.

Techno-economic analysis of combined inverted Brayton – Organic Rankine cycle for high-temperature waste heat recovery

Many practical cases with waste heat recovery potential such as exhaust gases of reciprocating engines, cement kilns or heat-treating furnaces, are nowadays often integrated with organic Rankine cycle to convert waste heat to the mechanical power. However, when dealing with high-temperature waste heat, organic Rankine cycle faces efficiency limit due to the physical properties of the working and thermal fluids. That gives room for further enhancement of the waste heat recovery technologies via the investigation of different non-conventional schemes as one of the possible ways. In the present work, a system introducing the combined inverted Brayton plus organic Rankine cycle is under investigation. Aspen Hysys models of both conventional organic Rankine cycle and combined cycle were designed, orienting on waste heat recovery from the heavy-load gas-fueled reciprocating engine exhaust. In this way, the performance of the combined scheme was benchmarked versus the conventional organic Rankine cycle. An assessment of the organic Rankine cycle working fluids was provided, and pentane has shown the best thermodynamic performance. The study on inverted Brayton cycle defined the remarkable effect of the water condensation in the gas duct on the inverted Brayton cycle performance. Finally, both thermodynamic and economic optimizations of the models were conducted, setting the stage for the comparison of solutions. Results have shown the 10% advantage of the combined scheme over organic Rankine cycle in generated power and system efficiency. The levelized-cost-of-energy-based optimization for variable capacity factors has highlighted above 6% advantage of the investigated solution. The analysis of the sensitivity from machines’ efficiencies and heat exchangers’ pinches has shown that with some sets of parameters, the studied scheme may concede to the organic Rankine cycle.

Advanced X-ray Imaging Technology.

Since their discovery by Wilhelm Conrad Röntgen in 1895, X-rays have become the most widely available, typically fastest, and usually most cost-effective medical imaging modality today. From the early radiographic approaches using X-ray films as detectors, the portfolio of medical X-ray imaging devices developed into a large range of dedicated instrumentation for various applications. While X-ray imaging has come a long way, there are some physical properties of X-rays, which have not yet been fully exploited, and which may offer quite some room for further enhancements of current X-ray imaging equipment. Firstly, X-ray imaging today is mainly black and white, despite the fact that X-ray generators actually create a full spectrum of X-ray energies, and that the interactions of X-rays that occur within the human body are not the same for all energies and every material. Exploiting these spectral dependencies allows to not only obtain a black and white CT image, but also to obtain more molecularly specific information, which is relevant particularly in oncological precision radiology. The second aspect of X-rays, and so far in radiology mainly neglected and unused, is the physical fact that X-rays can also be interpreted in the wave picture, and not only as presently been done in the particle picture. If interpreted as waves, X-rays-just like visible light-experience a phase shift in matter, and this-if exploited correctly-can produce a new class of X-ray images, which then depict the wave interactions of X-rays with matter, rather than only the attenuating properties, as done until now.

WITHDRAWN: Techno-economic analysis of combined inverted Brayton - Organic Rankine cycle for high-temperature waste heat recovery

Abstract Many practical cases with waste heat recovery potential such as exhaust gases of reciprocating engines, cement kilns or heat-treating furnaces, are nowadays often integrated with organic Rankine cycle to convert waste heat to the mechanical power. However, when dealing with high-temperature waste heat, organic Rankine cycle faces efficiency limit due to the physical properties of the working and thermal fluids. That gives room for further enhancement of the waste heat recovery technologies via the investigation of different non-conventional schemes as one of the possible ways. In the present work, a system introducing the combined inverted Brayton plus organic Rankine cycle is under investigation. Aspen Hysys models of both conventional organic Rankine cycle and combined cycle were designed, orienting on waste heat recovery from the heavy-load gas-fueled reciprocating engine exhaust. In this way, the performance of the combined scheme was benchmarked versus the conventional organic Rankine cycle. An assessment of the organic Rankine cycle working fluids was provided, and pentane has shown the best thermodynamic performance. The study on inverted Brayton cycle defined the remarkable effect of the water condensation in the gas duct on the inverted Brayton cycle performance. Finally, both thermodynamic and economic optimizations of the models were conducted, setting the stage for the comparison of solutions. Results have shown the 10% advantage of the combined scheme over organic Rankine cycle in generated power and system efficiency. The levelized-cost-of-energy-based optimization for variable capacity factors has highlighted above 6% advantage of the investigated solution. The analysis of the sensitivity from machines’ efficiencies and heat exchangers’ pinches has shown that with some sets of parameters, the studied scheme may concede to the organic Rankine cycle.