Processing Cost Research Articles

Real-time scheduling of power grid digital twin tasks in cloud via deep reinforcement learning

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates.

Merger of internet of things and machine learning: The internet of everything sector projects, benefits, and future roles

The convergence of the Internet of Things and Machine Learning shapes key developments in information technologies, such as the Internet of Everything environment. The Internet of Things' Machine-to-Machine connectivity advances, real-time Big Data storage, and data-driven Machine Learning synergize the scope of Internet of Everything innovations. As the Internet of Things and Machine Learning merger continues, the Internet of Everything reshapes the world sector map by providing unprecedented possibilities and efficiencies. The propelled Internet of Everything is ushering in a new era of transformative projects in various sectors such as Healthcare and Life Science, Manufacturing, Agriculture, Energy, Transportation, and Construction. The high-tech transformative projects offer many applications in process optimization, cost reduction, increased productivity, and efficiency. Our work examines the prominent joint Internet of Things and Machine Learning initiatives, projects and works to encourage new initiatives and multidisciplinary work worldwide. We also discuss the benefits of transformative projects in different sectors in detail and put forward a forecast for their future roles. Our study is supported by solid academic literature and project examples.

Electrochemical deposition combined with high-temperature molecular sieve adsorption strategy: Efficient removal of key fission element yttrium for purification of radioactive molten salts

In order to realize energy cost savings in waste salt recycling and electrolysis processes during the nuclear fuel cycle, this paper presents a novel approach for the purification of waste molten salt by employing molecular sieves to absorb excess fission products selectively. The method is founded on the principles of electrolytic refining to achieve sustainable development of reprocessing. In this study, a systematic investigation of the electrochemical reduction/oxidation processes of Y(III)/In(III) was performed on electrode, as well as the electrode reaction of Y(III) on an In electrode at 773 K. Constant-current electrolysis produced an extraction rate of 93.11 % for Y(III) on the In electrode. In order to improve the current efficiency, greatly save the processing cost, and realize the sustainable development of dry post-processing. Subsequently, the waste molten salt environment following electrolysis was simulated, and the fission product was effectively adsorbed utilizing 5A molecular sieves, resulting in a yttrium removal rate of up to 99.94 %. This process not only significantly reduces the cost of electrolysis but also enhances the removal efficiency of target nuclides. The study examined the molten salt adsorption mechanism of molecular sieve, determining the primary adsorption mechanism to be chemical one (ion exchange), through the application of pseudo-first-order, pseudo-second-order, and Weber-Morris kinetic models for re-evaluation.

Efficient neural implicit representation for 3D human reconstruction

High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional methods for reconstructing 3D human motion frequently require the use of expensive hardware and have high processing costs. This study presents HumanAvatar, an innovative approach that efficiently reconstructs precise human avatars from monocular video sources. At the core of our methodology, we integrate the pre-trained HuMoR, a model celebrated for its proficiency in human motion estimation. This is adeptly fused with the cutting-edge neural radiance field technology, Instant-NGP, and the state-of-the-art articulated model, Fast-SNARF, to enhance the reconstruction fidelity and speed. By combining these two technologies, a system is created that can render quickly and effectively while also providing estimation of human pose parameters that are unmatched in accuracy. We have enhanced our system with an advanced posture-sensitive space reduction technique, which optimally balances rendering quality with computational efficiency. In our detailed experimental analysis using both artificial and real-world monocular videos, we establish the advanced performance of our approach. HumanAvatar consistently equals or surpasses contemporary leading-edge reconstruction techniques in quality. Furthermore, it achieves these complex reconstructions in minutes, a fraction of the time typically required by existing methods. Our models achieve a training speed that is 110× faster than that of State-of-The-Art (SoTA) NeRF-based models. Our technique performs noticeably better than SoTA dynamic human NeRF methods if given an identical runtime limit. HumanAvatar can provide effective visuals after only 30 s of training. Please visit https://github.com/HZXu-526/Human-Avatar for further demo results and code.

Polarization-controlled metasurface design based on deep ResNet

The accuracy of metasurface inverse design depends on the input dimension of the target light field and the output dimension of the unit structure parameters. When the two light field targets are simultaneously controlled, the designed unit structure often cannot satisfy the simultaneous directional control of the two light field target parameters due to the existence of coupling. This greatly limits the completeness of the function of metasurfaces in applications. In this paper, we apply the designed 13-layer residual network (ResNet) to the inverse design of wavefront control of dielectric metasurfaces. After the neural network training is completed, the correspondence between any amplitude, phase and the element structure parameters within a certain error range can be realized, which greatly saves the time cost in the metasurface design process. As a verification of the inverse design method, we arranged two focusing metasurfaces of different sizes. Their focusing efficiencies are more than 53%. The nanounit structure and its inverse design method proposed in this paper provide a new idea for the design of the metasurface to control the polarized light field. At the same time, it will have important applications in the intelligent design of metasurfaces.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.

A techno-economic assessment of conventional and modified Solvay processes for CO2 capture and reject brine desalination

The ongoing discharge of desalination reject brine and emission of carbon dioxide (CO2) into the environment pose a major threat to the ecosystem. In this context, the Solvay process presents a potential mitigation scheme for reducing reject brine salinity while simultaneously sequestering CO2. This work reports for the first time a systematic techno-economic assessment of the conventional (ammonia-based) and modified (calcium hydroxide-based and Potassium hydroxide-based) Solvay processes. The model evaluates the effect of reactor conversion, inlet CO2 weight%, inlet CO2 temperature, gas/liquid ratio, and brine flow rate on the CO2 and Sodium ions removal. In addition, the process cost associated with each parameter was analyzed, and the impact of implementing carbon tax on process profitability was investigated. Furthermore, the Solvay processes were evaluated using economic metrics including Net Present Value (NPV), economy of scale, and Levelized cost of water. The results suggest that the calcium hydroxide-based modified Solvay process outperforms other processes in terms of CO2 and Sodium ions removal. Moreover, for the studied parameters, the calcium hydroxide-based modified Solvay process demonstrated economic viability, yielding a decent annual profit. However, the conventional Solvay process requires a remarkable amount of expenses, yet after the implementation of 40 dollars per metric tons of CO2 as a carbon tax, the conventional Solvay reaches the break-even point. These findings underscore the importance of implementing environmentally friendly practices in the industry, as the modified Solvay process proves to be a promising solution for mitigating environmental threats and promoting sustainable operations.

Synthesis of P-decorated polysiloxane for good flame retardancy and toughness of epoxy thermosets

Polysiloxanes are modifiers that can enhance the fire safety and toughness of epoxy thermosets. However, the design of high-efficiency polysiloxane modifiers remains a formidable challenge, due to their indefinite structures as well as the complexity and high costs of the preparation process. A phosphorous-containing epoxy-functionalized polydimethylsiloxane (PDMS-DGE) has been prepared by a three-step process using octamethylcyclotetrasiloxane (D4), 2,4,6,8-tetramethylcyclotetrasiloxane (D4H), 1,1,3,3-tetramethyldisiloxane (TMDS), allyl glycidyl ether (AGE), and 9,10-dihydro-9-oxo-10-phosphaphenanthrene (DOPO). Incorporation of 15 wt% PDMS-DGE strongly improved the flame retardancy of epoxy thermosets. There was 38.2 % increase in limiting oxygen index value (LOI), 54.4 % decline in total heat release (THR), and 27.2 % decrease in total smoke production (TSP) as compared to the same values for combustion of unmodified epoxy thermoset. This could be ascribed to the generation of phosphorus/silicon-containing char layers, which effectively reduced the formation of combustible gases, smoke, and heat during burning. Compared to neat EP thermoset, the flexural strength and impact strength of the 15 wt% PDMS-DGE modified epoxy thermoset was increased by 51.1 % and 107.8 %, respectively. This is due to the existence of epoxy groups, rigid phosphaphenanthrene structures, and flexible polydimethylsiloxane chains in PDMS-DGE. Further, the presence of PDMS-DGE provides epoxy thermosets with good moisture resistance. A facile strategy to develop polysiloxanes for epoxy thermosets with both flame retardant properties and toughness, which has vast potential for industrial applications has been proposed.

Remarkable photodegradation breakdown cost, antimicrobial activity, photocatalytic efficiency, and recycling of SnO2 quantum dots throughout industrial hazardous pollutants treatment

In the conducted research, a one-step hydrothermal synthesis of pure and titanium-doped tin dioxide quantum dots is elaborated upon, with a thorough analysis of their structural, optical, morphological, and photocatalytic properties undertaken using advanced analytical techniques. Through X-ray Diffraction XRD, the crystalline nature and phase purity of the tetragonal structures of SnDs were confirmed, with the crystallite sizes measured at 3.0 nm for SnD1 and 7.66 nm for SnD2, following treatments at 240 °C and 300 °C, respectively. The structural integrity of SnO2 was maintained despite titanium doping. FTIR spectroscopy verified the existence of specific vibrational modes indicative of surface hydroxyl groups. HRTEM images revealed the spherical morphology of particles, with diameters of 3.5 nm for SnD1 and 9.1 nm for SnD2. Optical band gaps, determined through UV-DRS, ranged from 3.33 eV in SnD1 to 3.47 eV in SnDTi2. The photocatalytic degradation of Congo Red dye under xenon lamp irradiation was quantitatively assessed; notably, SnD1 exhibited a 23 % higher rate constant compared to SnD2, attributed to its smaller particle size and a 31 % greater surface area. Doping with 4 % Ti in Sn0.96Ti0.04O2 more than doubled the degradation rate compared to a 6 % Ti doping in Sn0.94Ti0.06O2. Furthermore, the generation of hydroxyl radicals was significantly enhanced, showing an increase of approximately 220 % for SnD1 and 80 % for SnD2. The capability of these nanomaterials to reduce the chemical oxygen demand of industrial organic pollutants to within regulatory limits under solar irradiation was documented, with SnD1 maintaining its photocatalytic efficiency over seven cycles of reuse. In the photocatalytic degradation rate of Congo Red dye, which was 23 % higher for SnD1 compared to SnD2, and the threefold increase in the degradation rate for SnDTi1 compared to SnDTi2. An economic assessment, based on electricity tariffs in Saudi Arabia, highlighted the cost-effectiveness of SnD1, which ranged from 26.93 to 30.36 USD per breakdown cost of the photodegradation process, showing it to be less costly than SnD2. Conversely, SnDTi1 was found to be more economical than SnDTi2, with costs ranging from 26.67 to 33.09 USD. Collectively, the results emphasize the outstanding photocatalytic performance and cost-efficiency of SnDs, reinforcing their potential as sustainable solutions for the treatment of industrial wastewater. Additionally, the antibacterial efficacy of these materials against a range of bacteria, yeast, and fungi was investigated and substantiated.

Deep reinforcement learning driven cost minimization for batch order scheduling in robotic mobile fulfillment systems

Robotic Mobile Fulfillment Systems (RMFS) are extensively employed in modern warehouses. In the era of booming e-commerce, this parts-to-picker model significantly reduces warehouse costs and enhances operational efficiency. The primary focus of this article is on the scheduling of batch orders, which involves the simultaneous allocation of mobile robots and picking stations to orders to meet their requirements, with the aim of minimizing processing costs. While the optimization of RMFS has been extensively investigated by scholars in recent years., research on categorized storage remains limited. Furthermore, combinatorial optimization in scenarios with large orders, numerous picking stations, and multiple mobile robots presents an ongoing and significant challenge. To overcome these challenges, we introduce the Enhanced Deep Reinforcement Learning Method for Batch Order Scheduling (EDRL-OBOS), designed to minimize operational costs in RMFS. Our proposed algorithm, EDRL-OBOS, features a uniquely designed state space that encompasses the allocation of all orders, with the variable portion of the state space diminishing as the algorithm evolves. Moreover, we substitute traditional actions with heuristic rules, significantly enhancing the efficiency of the algorithm. Additionally, we establish a reward function based on a greedy algorithm, ensuring that our method surpasses conventional algorithms. As the number of episodes progresses, the EDRL-OBOS algorithm assigns picking stations and sequences of mobile robots to each order until all orders within the state space are fulfilled, subsequently determining the reward. Subsequently, all states are reset to their initial conditions, and the order allocation scheme is optimized through the learning experience from the previous episode, until convergence is reached. Ultimately, we conduct simulation experiments by varying the number of orders, picking stations, and mobile robots. The results indicate that the EDRL-OBOS algorithm achieves a maximum cost reduction of 22.89%, a minimum of 19.66%, and an average of 21.57%, across various scenarios. At the same time, the computation of our algorithms is superior to that of traditional algorithms.

Channel selection strategy with consumer impulse purchase in livestream selling

AbstractConsumer impulsive purchasing is very common, especially in the livestream selling where they are influenced by policies such as low prices, limited time, and limited quantity. This behavior can increase a firm's sales, but it also leads to higher return rates. Considering consumer impulse purchase, this paper conducts an analytical model to investigate the conditions for a firm to introduce a livestream channel and whether the firm should adopt a low or moderate livestream price strategy when introducing a livestream channel. Our results show that, when return processing cost is small enough, the firm benefits from the livestream strategies as long as the streamer's fan effect is larger than a threshold. Specifically, a firm prefers to adopt the low livestream price strategy when cooperating with a large fan effect streamer and a moderate livestream price strategy with a small fan effect streamer. However, when return processing cost is large, the low livestream price strategy cannot be the dominant strategy. Furthermore, we make several extensions (i.e., different impulse utility on different livestream price, impact of positive impulse utility of low impulsive consumers, different online return rates of different types of consumers, the costs of introducing the livestream channel, and consumer return processing cost) to verify the robustness of the model, and the main conclusions remain unchanged. These results provide guidance for firms on the introduction of livestream channel and pricing decisions of different channels.

Bidirectional Reflectance Distribution Function (BRDF) measurements, modeling and applications in space-agile Tx/Rx common aperture system

The Tx/Rx common aperture system not only needs to suppress out-of-field stray light, but also needs to suppress the influence of optical surface backscatter on detectivity. Backscatter generated by optical surfaces is often the dominant source of stray radiation that causes T/R isolation problems, and the black surfaces chosen for baffles also play a significant role in stray light suppression. Four commonly used optomechanical structure surfaces scattering BRDF data are measured, emulated and used. CFRP is selected as the baffle material, for the processing performance, cost performance and thermal control difficulty. A comparison is made between theoretical and experimental results for clean and particle contaminated ultra-smooth optical surface (CL= 800) scattering BRDF data. With the help of Mie theory and measured BRDF, the study improved the fitting accuracy of the Harvey-Shack model for the contaminated micro roughness surface. The Harvey-Shack model modeling coefficients of the particle contaminated micro-roughness surface are b = 0.12, l = 0.08, and s=-2.2. The scattering radiation introduced by particulates reduces the T/R isolation of the space-agile Tx/Rx common aperture system by 3.74 dB. Measurement results of particulates reducing the T/R isolation by 4.05 dB, verified the accuracy of particle contaminated surface BRDF modeling.

Tar Remedies

This two-part essay turns to the landscapes of bitumen mining in the Athabasca tar sands in western Canada. Despite the environmental costs of the tar sands mining process, the Canadian state remains invested in oil extraction in the tar sands. Starting from the premise that the extraction and burning of this bitumen was and is not inevitable, this dialogue locates hazardous hope in the landscapes of the Athabasca region. To do so, the first section is an analysis of Warren Cariou’s photographic practice, situating his work within themes of toxicity and hope. Written by an art historian, it argues that we can read the petrographs through a mode of critical spectatorship that generates questions about how extraction makes our world and how these processes are historically contingent choices based in what society has chosen to value. The second part is a short reflection by Warren Cariou on his practice and how he theorizes hope in the context of pollution.

Exploring the essential features influencing the synthesis of methylenedianiline to support industrial processes

The most important intermediate of methylene diphenyl diisocyanate (MDI), the most widely and quantitatively produced isocyanate in the world, is methylenedianiline (MDA). MDA is industrially produced from the reaction of aniline and formaldehyde catalysed by inorganic acids, most commonly HCl. The reaction parameters used during the synthesis of MDA have a fundamental impact on the quality parameters of the resulting MDA product mixture, and thus on the properties of the final MDI product mixture as well. Although MDA is an important industrial intermediate, currently its synthesis at the industrial level is based on empirical rules, knowledge and rule of thumbs, the effects of production parameters and their magnitude on product properties are not known and not published in the literature. In this study, the correlations between the independent operating parameters and dependent product quality parameters characterizing the MDA mixture were explored by developing different regression models with the use of experimental laboratory synthesis data in order to support the industrial synthesis process with easily deployable models with satisfactorily high accuracy. After investigating the laboratory experiment data from a total of 46 individual laboratory experiments, it was found that with machine learning models almost all of the independent parameters can be described with satisfactory accuracy. The models presented in this work demonstrate that it is possible to develop models that can be used to adjust the ring distribution, isomer ratios and selectivity of the reaction with minimising by-product quantities according to market demands or to different objective functions by fine-tuning the production parameters., thus achieving an optimal product portfolio and operating cost for the industrial process as well.

Implementation of first order statistical processor on FPGA for feature extraction

Statistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%.

A Cross-Sectional Study on Patient Preferences for Selecting Surgeons for Joint Replacement Surgery in India.

Introduction This study aims to investigate the complex decision-making process of patients in India when choosing surgeons for joint replacement surgery, with a focus on both clinical and non-clinical factors influencing their preferences. Methods This was a cross-sectional observational study conducted at the KIMS-Sunshine Hospitals, Hyderabad, a high-volume tertiary care institute in India, in which patients with end-stage osteoarthritis requiring primary total knee arthroplasty were evaluated using a self-administered questionnaire, which assessed both patient-related and surgeon-related factors in choosing their joint replacement surgeon. Results A total of 210 participants were surveyed among whom the majority were females with an average age of 60.2 years with the majority belonging to the upper-middle-class socioeconomic status (48.6%, N=102). Fifty-nine percent preferred surgeons with over 20 years of experience, and 63.8% were willing to travel out-of-state for recognized expertise. Family recommendations (33.8%) and surgeon reputation (24.3%) were primary factors in surgeon selection. A vast majority (73.3%) preferred surgeons who were skilled in robotic surgery and had foreign training (32.9%). However, the majority (67.6%) did not express any gender preference.The survey highlighted a broad range of informational sources affecting decisions, including financial consideration (63.8%), personal referrals, and online platforms (17.1%). Preferences were also shaped by hospital reputation and insurance options (10.5%), illustrating a nuanced interplay of quality, cost, and personal connections in the selection process. Conclusion The findings of this survey illuminate the intricate and diverse preferences exhibited by patients when selecting a surgeon for joint replacement surgery. A significant rise in patient expectations is evident, underscoring a demand for more personalized, contemporary, and high-quality healthcare services. Importantly, geographical proximity appears to be a diminishing concern in their decision-making process. This trend presents an opportunity for centers of excellence to extend their influence and attract patients on both a regional and national level.

Treatment of Slaughterhouse Plant Wastewater by Sequential Chemical Coagulation-Continuous Flow Electrooxidation Process

Wastewater from a small animal slaughterhouse (SWW) was treated by a two-step process: coagulation/flocculation (CF) followed by continuous flow electrooxidation (CFEO). Initially, a coagulant dose of 0.8 kg m−3 in the CF process, using FeCl3 at pH 8.5, achieved 52% COD and 63% turbidity removal (effluent: 2000 mg l−1 and 65.2 NTU). Alum, (optimum pH = 6.5), yielded 50% COD and 55% turbidity removal (effluent of 2100 mg l−1 and 78.5 NTU). Subsequently, when employing the CFEO process following the CF process with FeCl3, the study achieved highly efficient results. Specifically, under optimum conditions (residence time in the CFEO reactor, τ = 240 min, wastewater feed rate to the reactor = 15 ml min−1, and current density = 300 A m−2), the COD and turbidity removal efficiencies reached 99.60% (resulting in an effluent of 8 mg l−1) and 99.9% (resulting in an effluent of <0.10 NTU), respectively. In conclusion, the CF + CFEO consecutive treatment process demonstrated remarkable treatment efficiencies, with COD and turbidity removal rates of 99.9% and 99.9%, respectively. Moreover, the total operating cost of this treatment process was found to be 3.60 US $/m3.

Development of rapid and efficient enterovirus typing and classification platform for real-time diagnostics

Enteroviruses are highly prevalent among children and adults worldwide. Accurate detection and genotyping of enteroviruses can be achieved using consensus-degenerate hybrid oligonucleotide primer polymerase chain reaction (PCR)-based methods. Nevertheless, the popularized use of such methods for enterovirus detection and genotyping is limited by their relatively long processing time (>2 days), high processing cost, and the requirement for specialized and expensive instrumentation. Here, we report a relatively simple asymmetric PCR-electrochemical biosensing method that can detect enteroviruses and accurately characterize them into four distinct genotypic subgroups in less than 2 h. Specifically, genes of interest in the enteroviruses were amplified via asymmetric PCR (asy-PCR) using the designed PCR primers. The asy-PCR products were hybridized with enterovirus genotype-specific single-stranded DNA primer probes. Changes in response to electrochemical impedance signals can rapidly clarify the genotypes of enteroviruses. We observed that this method could not only effectively amplify the target gene within minimal PCR-amplified cycles of consensus-degenerate hybrid oligonucleotide (COHOP) products from the specimen but also allowed the rapid differentiation of the four subgroups of enteroviruses in 2 h. In summary, we developed an efficient and rapid electrochemical biosensing platform for enterovirus subtyping diagnostics that can be applied to other emergent infectious diseases.

Re-using an oleic by-product in the manufacturing of fired clay bricks

BackgroundThe high demand for building bricks underscores their essential role in the construction industry, which pushes researchers to continuously improve the characteristics of these products. This study aimed to investigate the impact of incorporating oleic components (Margins) in fired bricks manufacturing of by substituting water with different percentages of margins. MethodsFired bricks were produced at the laboratory using clays taking from the Tangier region in northern Morocco. During this process, water used in the bricks production was substituted with margins in increasing percentages ranging from 10 % up to 30 %. To validate the results obtained from the physicochemical analyses performed on the manufactured bricks, three types of vegetable samples from three different regions of Morocco were utilized. ResultThe study results are very remarkable, demonstrating a significant enhancement in compressive strength. The strength increased from 15.16 MPa for conventional bricks to 33.77 MPa for bricks manufactured with a 30 % substitution. Furthermore, porosity decreased from 33 % to 19 %. ConclusionBy integrating oleic components into the manufacturing process, we were able to achieve three main objectives.1. We have improved the characteristics of the bricks, which tend towards the load-bearing wall standards.2. We reduced the water quantity used in the bricks manufacturing by 30 %.3. We minimized the processing cost of the waste resulting from olive oil production, as well as its harmful effects on the environment.

Full Vector Inversion of Magnetic Microscopy Images Using Euler Deconvolution as Prior Information

AbstractPaleomagnetic data is collected from bulk samples, containing a mixture of stable and unstable magnetic particles. Recently, magnetic microscopy techniques have allowed the examination of individual magnetic grains. However, accurately determining the magnetic moments of these grains is difficult and time‐consuming due to the inherent ambiguity of the data and the large number of grains in each image. Here we introduce a fast and semi‐automated algorithm that estimates the position and magnetization of dipolar sources solely based on the magnetic microscopy data. The algorithm follows a three‐step process: (a) employ image processing techniques to identify and isolate data windows for each magnetic source; (b) use Euler Deconvolution to estimate the position of each source; (c) solve a linear inverse problem to estimate the dipole moment of each source. To validate the algorithm, we conducted synthetic data tests, including varying particle concentrations and non‐dipolarity. The tests show that our method is able to accurately recover the position and dipole moment of particles that are at least 15 μm apart for a source‐sensor separation of 5 μm. For grain concentrations of 6,250 grains/mm3, our method is able to detect over 60% of the particles present in the data. We applied the method to real data of a speleothem sample, where it accurately retrieved the expected directions induced in the sample. The semi‐automated nature of our algorithm, combined with its low processing cost and ability to determine the magnetic moments of numerous particles, represents a significant advancement in facilitating paleomagnetic applications of magnetic microscopy.