Experimental Phase Research Articles

Engineering Behaviour of Sustainable Concrete with Steel Mill Scale and the Effect of Admixture on Workability Improvement

Abstract: The integration of chemical admixtures and alternative materials in concrete production has become a focal point in advancing construction technologies. This study investigates the combined effects of superplasticizers and mill scale on the workability and compressive strength of M30 grade concrete. Three different superplasticizers—Auramix 200, SikaPlast®-3069 NS, and Master Polyheed 8126—are evaluated for their performance enhancements in concrete. Additionally, mill scale, an industrial by-product, is utilized to partially replace sand in varying proportions (0%, 20%, 40%, 60%, 80%, and 100%) to assess its viability as a sustainable aggregate alternative. The experimental phase is divided into three stages: initially assessing the workability and compressive strength of M30 concrete without admixtures; subsequently incorporating mill scale to observe its effect on concrete properties; and finally combining mill scale with each superplasticizer to maintain workability while evaluating the compressive strength. Slump cone tests are conducted to measure workability, and compressive strength tests are performed at 7, 14, and 28 days. Results indicate that the inclusion of superplasticizers significantly enhances workability, with Auramix 200 showing the highest improvement. The addition of mill scales up to 60% replacement level increases compressive strength, with a notable peak at 60% replacement for both plain and superplasticizer mixes. However, higher replacement levels (80% and 100%) result in reduced strength and workability. Among the superplasticizers, Auramix 200 exhibits the best overall performance in conjunction with mill scale. This study concludes that the strategic use of superplasticizers and mill scale can produce high-performance, sustainable concrete. Future research should explore long-term durability aspects and optimize mix designs for broader applications in construction.

TARGET IDENTIFICATION AND TRACKING IN COMPLEX ENVIRONMENT

This study focuses on developing advanced methods for the identification and classification of objects in complex environments. Over the past two years, there has been an increase in the use of advanced technologies in various challenging scenarios. This research is centered on accurately identifying targets and tracking them. The study addresses challenges related to object detection in multi-dimensional and intricate settings, taking into account natural conditions like rain and fog, as well as technical limitations such as camera capabilities. Special emphasis is placed on data collection for training the identification model, followed by extensive data preprocessing, including cleaning, labeling, and augmentation. The research employs YOLO and Deep Sport machine learning algorithms, focusing on improving the accuracy and reliability of target recognition and increasing data processing speed to minimize misidentification risks. The integration of YOLO, known for its quick real-time object detection, with Deep Sport, which excels in detailed feature extraction and classification, forms the basis of our methodology. This fusion is a complex combination of both models' strengths, with YOLO quickly identifying relevant objects and Deep Sport performing an in-depth analysis. The experimental phase involves extensive testing of the models in varied weather conditions and settings to evaluate performance under challenging circumstances. This work aims to enhance object identification techniques in complex environments, a critical aspect for the effectiveness of various advanced operations. The findings are expected to significantly contribute to the field by enabling quicker and more accurate target identification.

Efficiency of 7-dehydrocholesterol vitamin-D3 complex cream for xerosis and pruritus in elderly women

Background: Xerosis and pruritus (XP) are the most common skin conditions in the elderly, manifesting clinically as roughness, scales, fissures, and mild-to-severe itching. Vitamin D improves skin hydration, transepidermal water loss (TEWL), and regulates the immune system. We aimed to determine the efficiency of topical 7-dehydrocholesterol (DHC)-vitamin D3 complex cream in elderly patients with XP. Methods: An experimental phase III study was performed on elderly women with XP from December 2021 to March 2022 at the Geriatric Dermatology Clinic of Dr. Mohammad Hoesin General Hospital, Palembang. Inclusion criteria were age older than 55 years, healthy or with comorbidity. The exclusion criteria include the use of moisturizer within two weeks before the study. DHC-vitamin D3 cream was applied on subject’s volar arms once a day. Overall dry skin score (ODSS), visual analog scale (VAS) for pruritus and adverse effects, skin hydration, TEWL, sebum level, serum vitamin D level, and dermatology life quality index (DLQI) were assessed at baseline and after 4 weeks. Statistical analysis was done with Wilcoxon and paired T-test. Results:Eighteen elderly women were included. Most subjects were 60 to 74 years old (83,3%). Half the subjects (50%) had comorbidity. There was a significant difference in skin hydration (p Conclusion: Topical 7-DHC vitamin D3 complex cream is proven to improve skin barriers, increase serum vitamin D levels, well-tolerated, and provided satisfaction and comfort in elderly women with XP.

Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy

Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively

Revolutionizing cultural heritage preservation: an innovative IoT-based framework for protecting historical buildings

Italy offers a cultural heritage of considerable value that needs to be protected. Indeed, natural deterioration linked to the passage of time affects ancient artifacts and buildings. Sometimes, the deterioration compromises the functionality of cultural assets, pushing them toward decay. In this scenario, effective intervention seems impossible on the various critical points because of the wide variability of factors involved and the wide range of possible treatments. However, the spread of low-cost technologies has led to the possibility of having different devices and sensors able to communicate and interact with each other and humans: the Internet of Things (IoT). In this scenario, the IoT paradigm makes it possible to map reality by defining a coherent virtual representation (Digital Twin), which could help preserve Cultural Heritage. This work introduces an IoT-based system combining monitoring, predictive maintenance, and decision-making regarding the implementable interventions for protecting cultural heritage buildings. For this purpose, deep and machine learning techniques allow for the detection and classification of damages on specific materials. The experimental phase consists of two phases: the first aims to evaluate the accuracy of the proposed architecture, and the second exploits a prototype capable of interacting with expert users. The results of the experimental campaign are promising.

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

Experimental and numerical study of combustion in pipe cutting operations with hydrogen-blending gas

This study investigated the flame combustion of gas-containing pipe cutting operations by combining experimental and numerical simulation techniques. Based on the empirical flame length formula and experimental and numerical simulation flame length data, a flame length formula for gas outlet with large aspect ratio was proposed. In the experimental phase, cutting experiments were conducted with cutting slit lengths of 1–11 cm for pipe pressures of 10–550 Pa and hydrogen doping ratios of 0%, 10%, and 20%. The flame lengths were then measured. A hydrogen-doped natural gas combustion model based on the vortex dissipation conceptual model and Grimech-3.0 mechanism was established to simulate the experimental conditions. The flame lengths derived from the numerical simulation were in good agreement with the experimentally measured flame lengths, with an average error of 7.02%. This indicates that the reliability of the numerical model can be verified. The effects of hydrogen doping ratio, tube pressure, and cutting slit length on flame length and thermal radiation range were investigated using the control variable method. The results demonstrated that hydrogen doping reduced flame length and flame length away from the flame. Furthermore, an increase in pipe pressure and single cut length also resulted in an increase in flame length. It was observed that under normal operating conditions, a safe intensity of thermal radiation could be maintained within the operating distance when the operator wore protective clothing.

Investigating the impact of cross-sectional area on the crushing characteristics of axially-loaded hemispherical composite shells

The research focus has shifted towards lightweight structures with high energy absorption capabilities due to advancements in automotive safety technology. This study specifically investigates the impact of cross-sectional area on the energy absorption characteristics of hemispherical composite shells. The experimental phase involves characterizing a glass fiber epoxy composite, followed by the manufacture of hemispherical composite shell specimens with varying cross-sectional areas. These specimens undergo quasi-static axial compressive loading, and the energy absorption parameters are analyzed. The results indicate a significant influence of the composite cross-sectional area on the crushing behavior of hemispherical shells, with a observed decrease in specific energy absorption as the cross-sectional area increases. Additionally, a 3D Finite Element (FE) model is created using ABAQUS FE code to numerically simulate the crushing process. The model’s predictions are compared and validated against experimentally measured values, demonstrating a satisfactory correlation.

Mild alkaline conditions affect digester performance and community dynamics during long-term exposure

This paper examines the adaptive responses of microbial communities to gradual shifts in pH toward the mild alkaline range in anaerobic digestion (AD) systems. The results indicate that a pH of 8.0 serves as a critical upper limit for stable AD operation, beyond which microbial efficiency declines, underscoring the importance of microbial resilience against elevated pH stress. Specifically, hydrolysis genera, e.g. Eubacterium and Anaerobacterium, and syntrophic bacteria were crucial for reactor stability. Fibrobacter had also been shown to play a key role in the accumulation of propionate, thus leading to its dominance in the volatile fatty acid profile throughout the experimental phases. Overall, this investigation revealed the potential adaptability of microbial communities in AD systems to mild alkaline pH shifts, emphasizing the hydrolysis bacteria and syntrophic bacteria as key factors for maintaining metabolic function in elevated pH conditions.

A modified phase-retrieval algorithm to facilitate automatic de novo macromolecular structure determination in single-wavelength anomalous diffraction.

The success of experimental phasing in macromolecular crystallography relies primarily on the accurate locations of heavy atoms bound to the target crystal. To improve the process of substructure determination, a modified phase-retrieval algorithm built on the framework of the relaxed alternating averaged reflection (RAAR) algorithm has been developed. Importantly, the proposed algorithm features a combination of the π-half phase perturbation for weak reflections and enforces the direct-method-based tangent formula for strong reflections in reciprocal space. The proposed algorithm is extensively demonstrated on a total of 100 single-wavelength anomalous diffraction (SAD) experimental datasets, comprising both protein and nucleic acid structures of different qualities. Compared with the standard RAAR algorithm, the modified phase-retrieval algorithm exhibits significantly improved effectiveness and accuracy in SAD substructure determination, highlighting the importance of additional constraints for algorithmic performance. Furthermore, the proposed algorithm can be performed without human intervention under most conditions owing to the self-adaptive property of the input parameters, thus making it convenient to be integrated into the structural determination pipeline. In conjunction with the IPCAS software suite, we demonstrated experimentally that automatic de novo structure determination is possible on the basis of our proposed algorithm.

First-principles prediction of the Co–Al phase diagram including configurational, vibrational and magnetic contributions

Co-based superalloys have attracted attention to replace Ni-based superalloys in high temperature structural applications because of their higher melting temperature and corrosion resistance. Strengthening is provided by Co3(Al, X) (X = W, Cr, Mo, Ni) phases and optimization of their properties requires detailed information about the free energies of the different phases and of the phase diagram of the Co–Al system. This is achieved in this paper through first principles calculations in combination with statistical mechanics. Configurational entropic contributions to the free energy were included through Monte Carlo simulations using the cluster expansion formalism. The vibrational entropy of each phase was determined through the length-stiffness relationship while the magnetic entropy was also included through the Heisenberg Hamiltonian. The computed phase diagram is compared with the currently accepted experimental phase diagram and the different contributions to the stability of each phase are analyzed independently. The potential of this strategy to predict phase diagrams of magnetic systems is clearly established.

Effect of oxidant quantity and humidification temperature on performance of PEMFC with twin inlet and twin outlet flow field

The paper presents an analysis of the performance of a Proton Exchange Membrane (PEM) fuel cell, which is equipped with a flow field design featuring dual inlets and outlets, while operating under conditions of excess stoichiometry. These experiments were conducted using a fuel cell system connected to a station that allowed for the precise adjustment of gas flow rates. During the initial phase of experimentation, various proportions of excess oxygen were systematically applied, while maintaining constant hydrogen flow rates of 80 mL/min and 100 mL/min. Particularly noteworthy, for the case of a 100 mL/min hydrogen gas flow rate and the optimized excess oxygen proportion of 150%, further experiments were undertaken to ascertain the ideal humidification conditions. The outcomes of these experiments revealed that a hydrogen gas flow rate of 100 mL/min consistently outperformed the 80 mL/min flow rate in terms of fuel cell performance. Moreover, it was observed that the introduction of excess oxygen significantly improved performance, up to a 50% oxygen proportion for the 80 mL/min hydrogen flow rate and up to a 150% proportion for the 100 mL/min hydrogen flow rate. One intriguing observation pertained to the influence of humidification. Specifically, it was found that the utilization of a humidification temperature of 100°C, or the absence of humidification altogether, resulted in notably diminished power output. In contrast, intermediate humidification temperatures of 60°C, 70°C, 80°C, and 90°C consistently yielded identical maximum power points (MPP) when combined with a 150% excess oxygen supply and a hydrogen flow rate of 100 mL/min. The twin inlet-twin outlet flow field provides a slight advantage over the conventional serpentine flow field in the overall analysis.

The Role of Short-Chain Fatty Acids, Particularly Butyrate, in Oncological Immunotherapy with Checkpoint Inhibitors: The Effectiveness of Complementary Treatment with Clostridium butyricum 588.

The discovery of immune checkpoints (CTLA-4, PD-1, and PD-L1) and their impact on the prognosis of oncological diseases have paved the way for the development of revolutionary oncological treatments. These treatments do not combat tumors with drugs "against" cancer cells but rather support and enhance the ability of the immune system to respond directly to tumor growth by attacking the cancer cells with lymphocytes. It has now been widely demonstrated that the presence of an adequate immune response, essentially represented by the number of TILs (tumor-infiltrating lymphocytes) present in the tumor mass decisively influences the response to treatments and the prognosis of the disease. Therefore, immunotherapy is based on and cannot be carried out without the ability to increase the presence of lymphocytic cells at the tumor site, thereby limiting and nullifying certain tumor evasion mechanisms, particularly those expressed by the activity (under positive physiological conditions) of checkpoints that restrain the response against transformed cells. Immunotherapy has been in the experimental phase for decades, and its excellent results have made it a cornerstone of treatments for many oncological pathologies, especially when combined with chemotherapy and radiotherapy. Despite these successes, a significant number of patients (approximately 50%) do not respond to treatment or develop resistance early on. The microbiota, its composition, and our ability to modulate it can have a positive impact on oncological treatments, reducing side effects and increasing sensitivity and effectiveness. Numerous studies published in high-ranking journals confirm that a certain microbial balance, particularly the presence of bacteria capable of producing short-chain fatty acids (SCFAs), especially butyrate, is essential not only for reducing the side effects of chemoradiotherapy treatments but also for a better response to immune treatments and, therefore, a better prognosis. This opens up the possibility that favorable modulation of the microbiota could become an essential complementary treatment to standard oncological therapies. This brief review aims to highlight the key aspects of using precision probiotics, such as Clostridium butyricum, that produce butyrate to improve the response to immune checkpoint treatments and, thus, the prognosis of oncological diseases.

RECYCLING OF PHOTOVOLTAIC CELLS – A REVIEW

The growth of solar energy has been remarkable between 2013 and 2022, with a doubling of capacity from 80 GW to 197 GW. To recover valuable materials and mitigate environmental impact, it is imperative to have effective waste management practices in place. While recycling technologies are still in the experimental phase, the EU has put in place legal frameworks for manufacturer liability. There are continuous efforts being made to develop commercially viable technologies for the management of end-of-life PV module waste. An important part of the system of renewable sources is the establishment of a high-quality and efficient waste management system, which is contained within the concept of the circular economy. PV modules will at some point reach the end of their life cycle, which is estimated to be around 25 years, and become waste, therefore a lot of effort is currently being put into research and development of different waste treatment technologies of PV modules. They are still in the experimental phase, and few technologies become commercially available. Also, the management of waste generated at the end of the life cycle of photovoltaic technology is not yet fully regulated. However, at the level of the European Union, there is a legal framework for the liability of PV module manufacturers according to Directive on electric waste and electronic equipment. This paper is a detailed review of recycling technologies based on existing literature.

Electrocatalytic Oxygen Reduction Using Metastable Zirconium Suboxide.

Strategies for discovery of high-performance electrocatalysts are important to advance clean energy technologies. Metastable phases such as low temperature or interfacial structures that are difficult to access in bulk may offer such catalytically active surfaces. We report here that the suboxide Zr3O, which is formed at Zr-ZrO2 interfaces but does not appear in the experimental Zr-O phase diagram exhibits outstanding oxygen reduction reaction (ORR) performance surpassing that of benchmark Pt/C and most transition metal-based catalysts. Addition of Fe3C nanoparticles to give a Zr-Zr3O-Fe3C/NC catalyst (NC=nitrogen-doped carbon) gives a half-wave potential (E1/2) of 0.914 V, outperforming Pt/C and showing only a 3 mV decrease after 20,000 electrochemical cycles. A zinc-air battery (ZAB) using this cathode material has a high power density of 241.1 mW cm-2 and remains stable for over 50 days of continuous cycling, demonstrating potential for practical applications. Zr3O demonstrates that interfacial or other phases that are difficult to stabilize may offer new directions for the discovery of high-performance electrocatalysts.

Thermodynamic re-optimization of the CaO–SiO2 system integrated with experimental phase equilibria studies

CaO–SiO2 is the key system in ferrous and non-ferrous metallurgy, ceramic manufacture, cement production, and other applications. A number of discrepancies and gaps in knowledge has been identified and resolved since the last thermodynamic optimization of this system. This study reports new experimental phase equilibria data measured using recent advances in equilibration, sample quenching, and Electron Probe X-ray Microanalysis (EPMA) technique. An updated self-consistent set of thermodynamic model parameters is provided. Most importantly, these updates are integrated with a complete re-assessment of the 20-component Cu–Pb–Zn–Fe–Ca–Si–O–S–Al–Mg–Cr–Na–As–Sn–Sb–Bi–Ag–Au–Ni–Co system directly relevant to applications in the ferrous and non-ferrous metallurgical industries. To obtain a large single set of model parameters, significant advances were made in the computational approach, with a focus on simultaneous parallel optimization of binary and ternary model parameters using an experimental dataset combining binary, ternary, and multicomponent information. The Modified Quasichemical Formalism is used for the liquid slag phase. Thermodynamic properties of solid phases and liquid endmembers are completely revised following the recommendations for the third generation of CALPHAD databases. Other notable changes include the revision of the melting temperature of CaO, justified by extrapolations from the multicomponent systems, as well as a physically realistic heat capacity function for metastable SiO2 below the glass transition temperature. These updated properties expand the areas of application of the database to amorphous and partially crystallized slags at ambient temperatures, such as thermodynamics and kinetics of leaching of toxic elements in an aqueous environment. The fundamental limitations of the existing Modified Quasichemical Formalism are also discussed, along with strategies for addressing them.

The precision of signals encoding active self-movement.

Everyday actions like moving the head, walking around, and grasping objects are typically self-controlled. This presents a problem when studying the signals encoding such actions because active self-movement is difficult to control experimentally. Available techniques demand repeatable trials, but each action is unique, making it difficult to measure fundamental properties like psychophysical thresholds. We present a novel paradigm that recovers both precision and bias of self-movement signals with minimal constraint on the participant. The paradigm relies on linking image motion to previous self-movement, and two experimental phases to extract the signal encoding the latter. The paradigm takes care of a hidden source of external noise not previously accounted for in techniques that link display motion to self-movement in real time (e.g., virtual reality). We use head rotations as an example of self-movement, and show that the precision of the signals encoding head movement depends on whether they are being used to judge visual motion or auditory motion. We find that perceived motion is slowed during head movement in both cases. The "nonimage" signals encoding active head rotation (motor commands, proprioception, and vestibular cues) are therefore biased toward lower speeds and/or displacements. In a second experiment, we trained participants to rotate their heads at different rates and found that the imprecision of the head rotation signal rises proportionally with head speed (Weber's law). We discuss the findings in terms of the different motion cues used by vision and hearing, and the implications they have for Bayesian models of motion perception.NEW & NOTEWORTHY We present a psychophysical technique for measuring the precision of signals encoding active self-movements. Using head movements, we show that 1) precision is greater when active head rotation is performed using visual comparison stimuli versus auditory; 2) precision decreases with head speed (Weber's law); 3) perceived speed is lower during head rotation. The findings may reflect the steps needed to convert different cues into common units, and challenge standard Bayesian models of motion perception.

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing.

This article presents a computer vision-based approach to switching electric locomotive power supplies as the vehicle approaches a railway neutral section. Neutral sections are defined as a phase break in which the objective is to separate two single-phase traction supplies on an overhead railway supply line. This separation prevents flashovers due to high voltages caused by the locomotives shorting both electrical phases. The typical system of switching traction supplies automatically employs the use of electro-mechanical relays and induction magnets. In this paper, an image classification approach is proposed to replace the conventional electro-mechanical system with two unique visual markers that represent the 'Open' and 'Close' signals to initiate the transition. When the computer vision model detects either marker, the vacuum circuit breakers inside the electrical locomotive will be triggered to their respective positions depending on the identified image. A Histogram of Oriented Gradient technique was implemented for feature extraction during the training phase and a Linear Support Vector Machine algorithm was trained for the target image classification. For the task of image segmentation, the Circular Hough Transform shape detection algorithm was employed to locate the markers in the captured images and provided cartesian plane coordinates for segmenting the Object of Interest. A signal marker classification accuracy of 94% with 75 objects per second was achieved using a Linear Support Vector Machine during the experimental testing phase.

Squiggle Game, from a Psychotherapy to an Educational Creativity Method. Drawing and Designing Together

AbstractThe paper presents an experiment with a squiggle game in a design class: a joint drawing activity. The squiggle game, which comes from psychoanalysis, has been adapted to a teaching‐learning situation in order to observe how graphic design practices and skills develop through creativity. The hypothesis is that the game impacts the training of participants, including teachers, by developing their creativity through the co‐construction that occurs between them. A qualitative methodology based on activity theory has been set up, consisting of two phases of experimentation in a vocational training class in France. The results are based on a semiotic and cognitive analysis of the drawing activity through its components and the participants' discourse about their drawings during self‐confrontations. The discussion creates a dialogue between several research paradigms in educational sciences, psychology, and psychoanalysis within artistic disciplines. It leads to the identification of four creativity methods that impact the teaching‐learning processes generated by the squiggle game: the game of abstract random drawing in pairs; the figurative patterns sought through combinations of strokes (complementation); opposition between very strong former and current routines of drawing or behaviours; and rebounding from the other's prompts towards new dynamics.

Observation of multiple attractors and diffusive transport in a periodically driven Klein-Gordon chain.

We consider a Klein-Gordon chain that is periodically driven at one end and has dissipation at one or both boundaries. An interesting numerical observation in a recent study [Prem et al., Phys. Rev. B 107, 104304 (2023)2469-995010.1103/PhysRevB.107.104304] was that for driving frequency in the phonon band, there is a range of values of the driving amplitude F_{d}∈(F_{1},F_{2}) over which the energy current remains constant. In this range the system exhibits a traveling wave solution termed a "resonant nonlinear wave" (RNW). It was noted that the RNW mode occurs over a range (F_{1},F_{2}) and shrinks with increasing system size, N. Remarkably, we find that the RNW mode is in fact a stable solution even for F_{d}>F_{2}, and that in this regime there exist two attractors, both with finite basins of attraction. We improve the perturbative treatment for the RNW mode, presented in the earlier work, by including the contributions of third harmonics. We also consider the effect of thermal noise at the boundaries and find that the RNW mode is stable for small temperatures. Corresponding to the two attractors for large F_{d} at zero temperature, the system can now be in two nonequilibrium steady states. Finally, we present results for a different driving protocol [Komorowski et al., Commun. Math. Phys. 400, 2181 (2023)10.1007/s00220-023-04654-4] where F_{d} is taken to scale with system size as N^{-1/2} and dissipation is only at the nondriven end. We find that the steady state for this case can be characterized by Fourier's law. We point out interesting differences that occur because of our dynamics being nonlinear and Hamiltonian. Our results suggest the intriguing possibility of observing the high-current-carrying RNW phase in experiments by careful preparation of initial conditions.