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As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth's surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models. [ABSTRACT FROM AUTHOR]

Centrifugal granulation technology using a spinning cup opens a potential way to recycle steel slag that is currently difficult to reuse. The objective of this research was to study the flow characteristics of a molten slag–metal mixture that was produced during smelting reduction in molten steel slag, passing over a spinning cup, so as to explore the feasibility of using centrifugal granulation technology to treat the steel slag. This was achieved by developing and implementing a computational fluid dynamics (CFD) model that incorporated free-surface multiphase flow to predict the thickness of the liquid slag film at the edge of the spinning cup (slag film thickness for short), which was an important parameter for estimating the size of the slag particles resulting from centrifugal granulation of the molten slag–metal mixture. The influences of various relevant parameters, including spinning cup diameter, slag feeding rate, cup spinning speed, etc., on the slag film thickness were analyzed. Additionally, hot experiments on centrifugal granulation of a molten slag–metal mixture were conducted to verify the results of the numerical simulations. The experimental results indicated a progressive reduction in the Sauter mean diameter of the slag particles as the metallic iron content in the slag increased. Specifically, when the iron content rose from 5% to 15% at a cup spinning speed of 2500 RPM, the Sauter mean diameter decreased by 13.77%. The numerical simulation results showed that the slag film thickness had a positive relationship to the slag feeding rate but a negative relationship to the spinning cup diameter and the cup spinning speed. Furthermore, the ratio between the mean slag particle diameter and the slag film thickness decreased nearly linearly with the increase in the metallic iron content in slag, with the average ratio being approximately 4.25, and this relationship was useful for estimating the slag particle size from the slag film thickness. Therefore, the present research results can provide theoretical guidance for the industrial application of spinning cup centrifugal granulation technology to effectively treat and recycle steel slags. [ABSTRACT FROM AUTHOR]

A microbial fungicide developed from Bacillus subtilis NCD-2 has been registered for suppressing verticillium wilt in crops in China. Spores are the main ingredient of this fungicide and play a crucial role in suppressing plant disease. Therefore, increasing the number of spores of strain NCD-2 during fermentation is important for reducing the cost of the fungicide. In this study, five kinds of carbon sources were found to promote the metabolism of strain NCD-2 revealed via Biolog Phenotype MicroArray (PM) technology. L-arabinose showed the strongest ability to promote the growth and sporulation of strain NCD-2. L-arabinose increased the bacterial concentration and the sporulation efficiency of strain NCD-2 by 2.04 times and 1.99 times compared with D-glucose, respectively. Moreover, L-arabinose significantly decreased the autolysis of strain NCD-2. Genes associated with arabinose metabolism, sporulation, spore resistance to heat, and spore coat formation were significantly up-regulated, and genes associated with sporulation-delaying protein were significantly down-regulated under L-arabinose treatment. The deletion of msmX, which is involved in arabinose transport in the Bacillus genus, decreased growth and sporulation by 53.71% and 86.46% compared with wild-type strain NCD-2, respectively. Complementing the mutant strain by importing an intact msmX gene restored the strain's growth and sporulation. [ABSTRACT FROM AUTHOR]

Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The F 1 s c o r e and A c c u r a c y of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the C o m p u t a t i o n a l c o m p l e x i t y of our method is reduced by 72.3% and 98.7%, respectively. The P a r a m s s i z e of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately. [ABSTRACT FROM AUTHOR]

In the final approach stage of rendezvous and docking of a spacecraft, the pose parameters of the target spacecraft need to meet docking or berthing capture conditions. Visible light visual measurement systems are increasingly employed in spacecraft ground tests to extract the geometric features of spacecraft to calculate and verify the accuracy of pose parameters. Most current feature-segmentation algorithms are unable to break through the scale transformation problem of spacecraft movement and the noise interference of multi-layer insulation materials in imaging. To overcome these challenges, we propose a novel feature segmentation algorithm based on the framework of deep convolutional neural networks. Firstly, a full convolution model of the encoding-decoding structure is constructed based on data for the ground test. A feature concatenation module is applied and combined with a network backbone to improve the segmentation performance. Then, a comprehensive loss function is presented and optimized by the pose characteristics of the spacecraft in the approach phase. Furthermore, a specific spacecraft simulation dataset to train and test our segmentation model is built through data augmentation. The experimental results verify that the proposed method achieves accurate segmentation of spacecraft of different scales, suppresses the interference caused by multilayer insulation material, and has strong robustness against motion ambiguity. The pixel accuracy of our proposed method reaches 96.5%, and the mean intersection over union is 93.0%. [ABSTRACT FROM AUTHOR]

Based on the three-dimensional (3D) steady-state CFD numerical simulations conducted previously on an industrial Kalugin top combustion hot blast stove, a two-dimensional (2D) transient CFD numerical model for a single channel (hole) of a column of checker bricks in the regenerator of the same hot stove was established in the present work. The average mass flowrate and temperature of the flue gas flowing into the checker brick holes during the combustion period predicted by the 3D model were used as the inlet boundary conditions of the 2D model. Inside the hole of the checker bricks, processes of fluid flow and heat transfer of the flue gas during the combustion period and those of cold air during the hot-air-supply period were simulated using the 2D model for multiple operation cycles (combustion and hot-air-supply periods) of the hot stove, enabling rapid predictions of hot-air temperature under different operating conditions. The simulation results show that when the fuel gas flowrate and air consumption coefficient during the combustion period are controlled within the range of 80,000–100,000 Nm3/h and 1.02–1.28, respectively, a hot-air temperature in the range from 1273 °C to 1295 °C can be obtained during the hot-air-supply period. Applying this optimized operating condition to the industrial hot stove investigated in this study can achieve significant effects of reducing fuel gas flowrate by 8.6% and increasing hot-air temperature by 32 °C. In addition, a regression analysis on the numerical simulation results and the data measured from the industrial hot stove yields a roughly linear relationship between the dome temperature during the combustion period and the hot-air temperature during the hot-air-supply period, that is, the hot-air temperature would be increased by about 16 °C for every increment of 10 °C in the dome temperature, for instance. Therefore, the influences of the operating parameters on heat transfer characteristics in the regenerator and on hot-air temperature obtained in the present work provide a useful reference for guiding the hot stove operation optimization to achieve significant energy saving and emission reduction through facilitating more efficient combustion to minimize fuel gas consumption in steel plants. [ABSTRACT FROM AUTHOR]

Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]

Fusarium wilt, caused by Fusarium oxysporum, is one of the most notorious diseases of cash crops. The use of microbial fungicides is an effective measure for controlling Fusarium wilt, and the genus Bacillus is an important resource for the development of microbial fungicides. Fusaric acid (FA) produced by F. oxysporum can inhibit the growth of Bacillus, thus affecting the control efficacy of microbial fungicides. Therefore, screening FA-tolerant biocontrol Bacillus may help to improve the biocontrol effect on Fusarium wilt. In this study, a method for screening biocontrol agents against Fusarium wilt was established based on tolerance to FA and antagonism against F. oxysporum. Three promising biocontrol bacteria, named B31, F68, and 30833, were obtained to successfully control tomato, watermelon, and cucumber Fusarium wilt. Strains B31, F68, and 30833 were identified as B. velezensis by phylogenetic analysis of the 16S rDNA, gyrB, rpoB, and rpoC gene sequences. Coculture assays revealed that strains B31, F68, and 30833 showed increased tolerance to F. oxysporum and its metabolites compared with B. velezensis strain FZB42. Further experiments confirmed that 10 µg/mL FA completely inhibited the growth of strain FZB42, while strains B31, F68, and 30833 maintained normal growth at 20 µg/mL FA and partial growth at 40 µg/mL FA. Compared with strain FZB42, strains B31, F68, and 30833 exhibited significantly greater tolerance to FA. [ABSTRACT FROM AUTHOR]

Bacillus spp. is one kind of the important representative biocontrol agents against plant diseases and promoting plant growth. In this study, the whole genomic sequence of bacterial strain HMB26553 was obtained. A phylogenetic tree based on the genome and ANI (average nucleotide identity), as well as dDDH (digital DNA–DNA hybridization), was constructed, and strain HMB26553 was identified as Bacillus velezensis. Fourteen biosynthetic gene clusters responsible for secondary metabolite were predicted via anti-SMASH, and six secondary metabolites were identified by UHPLC-QTOF-MS/MS (ultra-high-performance liquid chromatography coupled to quadrupole-time-of-flight tandem mass spectrometry). When the phytopathogen Rhizoctonia solani was treated with B. velezensis HMB26553, the mycelial structure changed, ROS (reactive oxygen species) accumulated, and the mitochondrial membrane potential decreased. Characteristics of strain HMB26553 were predicted and confirmed by genomic information and experiments, such as producing IAA, siderophore, extracellular enzymes and biofilm, as well as moving and promoting cotton growth. All these results suggested the mechanisms by which B. velezensis HMB26553 inhibits pathogen growth and promotes cotton growth, which likely provided the potential biocontrol agent to control cotton Rhizoctonia damping-off. [ABSTRACT FROM AUTHOR]

In order to study the behavior of molten blast furnace slag ligament breakup into droplets by centrifugal granulation with a spinning cup, three-dimensional transient CFD model simulations were performed in the present work to study the process of the slag deformation into ligaments upon leaving the spinning cup, which eventually disintegrate into droplets. The formation of molten slag ligaments at the edge of the spinning cup and their disintegration into droplets were numerically revealed so that the behavior and mechanism of the slag ligament breakup into droplets could be investigated. This work specifically examined the influence of cup spinning speed on the diameter and length of the molten slag ligaments around the cup periphery and the diameter of the droplets produced. The simulation results show that, for the same slag flowrate, with the increase in cup spinning speed, the slag film thickness at the cup edge decreases, the number of molten slag ligaments increases, and the diameter of the ligaments decreases, thus reducing the diameter of slag droplets. Moreover, as the number of molten slag ligaments increases as a result of the increased cup spinning speed, the flowrate of a single ligament decreases, so that the ligament disintegrates in a shorter radial distance, that is, the length of the ligament is shortened. In addition, this work also investigated the behavior and mechanism of a single molten slag ligament breakup into droplets. It was found that the process of molten slag ligament breakup into droplets under the action of centrifugal force and surface tension can also be approximately explained by the theory of the Rayleigh disintegration mechanism. [ABSTRACT FROM AUTHOR]

Bacillus subtilis strain NCD-2 is a promising biocontrol agent for soil-borne plant diseases and shows potential for promoting the growth of some crops. The purposes of this study were to analyze the colonization ability of strain NCD-2 in different crops and reveal the plant growth promotion mechanism of strain NCD-2 by rhizosphere microbiome analysis. qRT-PCR was used to determine the populations of strain NCD-2, and microbial communities' structures were analyzed through amplicon sequencing after application of strain NCD-2. Results demonstrated that strain NCD-2 had a good growth promotion effect on tomato, eggplant and pepper, and it was the most abundant in eggplant rhizosphere soil. There were significantly differences in the types of beneficial microorganisms recruited for different crops after application of strain NCD-2. PICRUSt analysis showed that the relative abundances of functional genes for amino acid transport and metabolism, coenzyme transport and metabolism, lipid transport and metabolism, inorganic ion transport and metabolism, and defense mechanisms were enriched in the rhizospheres of pepper and eggplant more than in the rhizospheres of cotton, tomato and maize after application of strain NCD-2. In summary, the colonization ability of strain NCD-2 for five plants was different. There were differences in microbial communities' structure in rhizosphere of different plants after application of strain NCD-2. Based on the results obtained in this study, it was concluded that the growth promoting ability of strain NCD-2 were correlated with its colonization quantity and the microbial species it recruited. [ABSTRACT FROM AUTHOR]

Aiming at the mode of liquid ligament disintegration into droplets in the process of the centrifugal granulation of molten blast furnace slag using a spinning cup, in order to gain an in-depth understanding of the behavior and mechanism of droplet formation by liquid ligament disintegration and to obtain appropriate conditions to control the size of the produced slag granules, a three-dimensional CFD model describing water–air two-phase flow with a free surface was established in this work for simulating the process of a single water ligament breakup into droplets under the action of gravity, surface tension and inertial forces, so as to compare with the process of a molten slag ligament disintegration into droplets due to centrifugal force exerted by the spinning cup. By studying the disintegration behavior and mechanism of a single water ligament, the similar phenomenon of a molten slag ligament disintegration is examined. The numerical simulation results on the breakup of a single water ligament show that the length of the ligament before its disintegration and the diameter of the droplets formed both increase with the increase of the velocity of the ligament initially exiting a capillary nozzle. In addition, an experimental set-up was established in the laboratory to conduct cold physical model simulation experiments on single water or liquid paraffin ligament disintegration. The experimental results are compared with the numerical simulation results so that the reliability of the CFD model is verified. The results of the present study show that, in the centrifugal granulation process of blast furnace slag using a spinning cup operated in the ligament disintegration mode, the breakup of a single molten slag ligament is very similar to that of a single water or liquid paraffin ligament, and both approximately follow the Rayleigh Disintegration Mechanism, which provides a theoretical basis for analyzing the breakup process of the ligaments of different liquids as references for guiding the operation of centrifugal granulation of molten slag. [ABSTRACT FROM AUTHOR]

Microbial treatment of cyanide pollution is an effective, economical, and environmentally friendly method compared with physical or chemical approaches. A cyanide-degrading bacterium was isolated from electroplating sludge and identified as Aerococcus viridans (termed A. viridans T1) through an analysis of the biochemical reaction and 16 S rDNA gene sequence. A. viridans T1 showed a maximum resistance to 550 mg L−1 CN−. The effect of pH and temperature on cyanide degradation and bacterial growth was evaluated. The highest cyanide removal efficiency and bacterial growth occurred at pH 8 and pH7, respectively. The optimum temperature for cyanide degradation and bacterial growth was 34 °C. In addition, the carbon source and nitrogen source for cyanide degradation were optimized. The optimal carbon source and nitrogen source were glycerol and peptone, respectively. The cyanide degradation experiment indicated that A. viridans T1 was able to remove 84.1% of free cyanide at an initial concentration of 200 mg L−1 CN− within 72 h and 86.7% of free cyanide at an initial concentration of 150 mg L−1 CN− within 56 h. To improve the cyanide-degrading efficiency of A. viridans T1, eight process variables were further optimized using a response surface methodology. Three significant variables (soybean meal, corn flour, and L-cysteine) were identified using a Plackett–Burman design, and the variable levels were optimized using a central composite design. The optimal values of soybean meal, corn flour, and L-cysteine were 1.11%, 1.5%, and 1.2%, respectively. Under these optimal conditions, the confirmatory experiments showed that the actual degradation rate was 97.3%, which was similar to the predicted degradation rate of 98.87%. Its strong resistance to cyanide and cyanide-degrading activity may allow A. viridans T1 to be a candidate for the bioremediation of cyanide-contaminated environments. [ABSTRACT FROM AUTHOR]

Simple Summary: Guanine nucleotide-binding protein-like 3-like (GNL3L) is a novel GTP-binding nucleolar protein. In this study, we analyzed the expression, prognosis, and immune roles of GNL3L in pan-cancer from multiple omics analyses. The final results showed that GNL3L is differentially expressed in a variety of cancers, plays a prognostic role, and has good immune value. Moreover, GNL3L may affect the occurrence of cancer through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, we established an esophageal cancer (ESCA) prediction model with strong predictive ability and proved that GNL3L can significantly affect the proliferation ability of esophageal cancer cells through clone formation assays. In conclusion, GNL3L is an important biomarker. Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors. [ABSTRACT FROM AUTHOR]

This paper researches the identification problem for the unknown parameters of the multivariate equation-error autoregressive systems. Firstly, the original identification model is decomposed into several sub-identification models according to the number of system outputs. Then, based on the characteristic that the information vector and the parameter vector are common among the sub-identification models, the coupling identification concept is used to propose a partially coupled generalized stochastic gradient algorithm. Furthermore, by expanding the scalar innovation of each subsystem model to the innovation vector, a partially coupled multi-innovation generalized stochastic gradient algorithm is proposed. Finally, the numerical simulations indicate that the proposed algorithms are effective and have good parameter estimation performances. [ABSTRACT FROM AUTHOR]

Global food production faces immense pressure, much of which can be attributed to climate change. A detailed evaluation of the impact of climate change on the yield of staple crops in Kazakhstan, a major food exporter, is required for more scientific planting management. In this study, the Mann–Kendall test and Theil–Sen Median slope were used to determine climate trends and staple food yields over the past 30 years; random forest was used to analyze the importance of monthly climatic factors; states were classified according to climatic factors through systematic clustering method; and lastly, the influence of climate on yield was analyzed using panel regression models. The upward trend in wind speed and potato yield throughout Kazakhstan was apparent. Furthermore, barley and wheat yields had increased in the southeast. We determined that for wheat, frostbite should be prevented after the warmer winters in the high-latitude areas. Except for July–August in the low-latitude areas, irrigation water should be provided in the other growth periods and regions. As similar effects were reported for barley, the same preventive measures would apply. For potatoes, tuber rot, caused by frost or excessive precipitation in May, should be prevented in high-latitude areas; soil dryness should be alleviated during the germination and seedling stages in low-latitude areas; and irrigation and cooling should be maintained during tuber formation and maturation. Furthermore, hot dry air in March and April could damage the crops. [ABSTRACT FROM AUTHOR]

Radio communication is a vital challenge during vehicle reentry into the Earth's atmosphere in practice, because the plasma covering the aerospace vehicle can block the communication as the spacecraft reenters the atmosphere. To investigate the potential of the terahertz wave, the transfer function method is first applied to study the wave propagation behavior in plasma, validated by the numerical and the experimental results. The comparison of all three results shows a decent agreement, with the average absolute difference around 1.1 dB for the power loss between the theoretical and numerical results for 100 GHz and 220 GHz and around 0.8 dB for the power loss between the simulation and measurement for 100 GHz and 220 GHz, which shows the validity of the transfer function method and the great potential of the numerical model for future study. Moreover, the results shows the possibility of the application of the THz wave to deal with the blackout problem. [ABSTRACT FROM AUTHOR]

In the context of sustainable development, agricultural production and environmental protection are inseparable, and environmental quality directly affects regional agricultural production safety. Kazakhstan is the largest food producer and exporter in Central Asia, and the quality of its agricultural environment is of great significance to international food security. This study focuses on the rationality of the entropy weight, factor weight, and CRITIC weight in the agricultural environmental evaluation within the common objective weight method, and comprehensively evaluates the coordination of environmental protection and agricultural production in Kazakhstan. The results show that (1) CRITIC weights are the most stable, followed by factor weights, while entropy weighting is the most unstable; objective weighting methods have their limitations and must be related to actual conditions and subjective experience. (2) The level of environmental protection and the degree of coordination are most problematic near the Aral Sea, followed by the remaining western region; the results reveal that these evaluation indexs are also insufficient at Kostany and Karagandy in the central region; this is caused by historical issues, climate change, natural conditions, and agricultural management patterns. Investment in environmental protection and agricultural production management should be coordinated in a targeted manner. (3) Except for the areas near the Aral Sea, the level of agricultural production in other states is very promising. This research serves as a reference for environmental assessment research, environmental governance investment, and agricultural production management in Kazakhstan. [ABSTRACT FROM AUTHOR]

Bath smelting technologies based on top submerged lance (TSL) injection have been widely used for pyrometallurgical metal production and solid waste treatment. In this work, a two-dimensional CFD simulation model of a pilot-scale 300 kg TSL furnace was established and applied to investigate the slag splashing phenomenon caused by submerged gas injection and combustion, with a special focus on the effect of submerged combustion on bubble formation, splash generation, splash distribution and heat transfer in the top space of the TSL furnace. The slag splash amount and distribution, and the temperature distribution characteristics inside the TSL furnace, especially under the influence of submerged combustion, were predicted, and influences of lance immersion depth and total injection gas flowrate on the splash behavior and heat transfer were investigated. As the lance immersion depth increases, more splashes are generated that distribute more evenly in the furnace top space and consequently heat transfer is enhanced. A larger injection gas flowrate generally increases the splash amount but the effect becomes weak when the injection gas flowrate exceeds a certain level, and there exists an appropriate range in injection gas flowrate for achieving the best heat transfer efficiency in TSL furnace. [ABSTRACT FROM AUTHOR]