Processing Methods Research Articles

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

The use of precious metals in restorative dentistry

This review delves into the historical and contemporary use of precious metals in restorative dentistry, tracing the evolution from ancient practices to modern technological advancements. It underscores the critical role of metallic materials, such as gold, silver, copper, and alloys like cobalt-chromium and titanium, in dental restorations due to their superior mechanical properties, including high elastic modulus, tensile strength, and hardness. These materials are favored for their biocompatibility, corrosion resistance, and adaptability to various manufacturing processes, including casting, mechanical processing, and advanced methods like CAD/CAM, laser sintering, and welding. Despite the advent of alternative materials catering to aesthetic preferences, metals maintain their relevance in dentistry for structural components in prosthetics, demonstrating significant advancements in dental material technology. The review highlights the importance of understanding the properties and behaviors of these materials within the oral cavity to ensure the quality and durability of dental restorations. Through a comprehensive literature search and analysis of metallurgy, manufacturing, and classification, this paper presents a thorough exploration of the indispensable role of precious metals in enhancing dental health and restorative practices.

Identification of the effect of ripening conditions on the yield of rose hips and their processed products

Wild rose hip fruit, pulp, beverage, nectar, as well as technological methods and tools are taken as the object of research. Rose hips have a, bright red color and a rich chemical composition, which makes it a very important raw material for industry. However, insufficient research slows down this work and makes the use of raw materials inefficient. With this in mind, the aim was to study the mechanical and technological characteristics of the femur, which are considered important for industrial processing. The reflection of ripening conditions on the mechanical, physical and chemical properties of rose hips, as well as the influence of processing methods, especially hot processing, on the quality of the product has been established. Research has shown that the percentage of fruit cultivation in the Dashkasan region was the lowest, and the highest in the Gadabay region, and the Goygol region took an intermediate position. The Latin hardness of fruits grown in Dashkasan region was 6.41 N. Gadabay region with 5.56 N and Goygol region with 5.47 N. This indicator plays an important role in the storage and processing of fruits. As a result of the treatment, the amount of dry matter in fruit, pulp and beverage increased slightly, titratable acids, ascorbic acid and pH decreased. An increase in total phenolic compounds was observed during the initial heat treatment of the raw material and was noted to be much lower in the pulp and beverage. Hot treatment had a reducing effect on the amount of ascorbic acid, which is considered one of the main components of indicators in the thigh. The obtained results can be used in individual family farms and in the food industry

Investigating the optical and electrical performance of rod coated silver nanowire-based transparent conducting films

Silver nanowires (Ag NWs) are highly promising building blocks for developing transparent conducting films (TCFs) due to their high electrical conductivity and good optical transparency. The large-scale production of Ag NW-based high-quality TCFs using low-cost processing methods can replace the traditional oxide based TCFs. Therefore, developing a reliable technique for large-scale fabrication of Ag NW-based TCFs is vital. This work involves the synthesis of Ag NWs, the fabrication of large-area Ag NW-based TCFs using a simple rod coating process, its optimization, and the performance analysis of the fabricated TCFs, including their demonstration as transparent heaters. The polyol synthesis method produces Ag NWs of lengths ranging from 25-110µm and diameters from 80-180 nm. The effect of Ag NW length, the number of coating passes, and the volume of the NW dispersion used per coating pass on the electrical and optical properties of the TCFs are studied by quantifying sheet resistance(Rs)and transmittance (T) of the film. The performance of the fabricated film is evaluated by estimating the figure of merit (FoM) in both percolative and bulk regimes. The TCF made with NWs of length 25.7µm and diameter 85.1 nm had the largest value of bulk FoM (101.3), percolative FoM (43.9), and, conductivity exponent (0.6). This elucidated the superior performance of the fabricated TCFs over those fabricated by other techniques. The critical thickness of the film (tmin), at the crossover between the percolation and bulk, scales with the shortest dimension of the NW, namely its diameter. The percolative FoM showed an increase, with a decrease in both sheet resistance and diameter of the NWs, with lowern. The fabricated TCF is tested as a transparent heater and the demonstration proves that rod coated Ag NW-based TCFs can be used for transparent electrode applications.

A grey target performance evaluation model for aeroengine pressure test bench

PurposeThis paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.Design/methodology/approachBased on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.FindingsA case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueThe pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Analysis of Occupational Safety in the Flour Production Process Application of Occupational Safety and Health Using the HIRADC Method in the HIRARC Flour Production Process

Analysis of Occupational Safety in the Flour Production Process Application of Occupational Safety and Health Using the HIRADC Method in the HIRARC Flour Production Process

STRATEGIES TO IMPROVE LOCAL GOVERNMENT FINANCIAL PERFORMANCE

This research aims to analyze the effect of PAD, capital expenditure, and balancing funds on the financial performance of local governments. With all regencies/cities in Lampung Province as the population in this study and 15 regencies/cities as samples. The data used in this study is in the form of secondary data and the data processing method used is multiple linear regression. The results of this study are: (1) PAD has a significant positive effect on the Financial Performance of Local Governments. (2) Capital Expenditure has a significant negative effect on the Financial Performance of Local Governments. (3) The Balancing Fund has a significant negative effect on the Financial Performance of Local Governments.

Incremental effects of near-atmospheric-pressure low-temperature air plasma jet irradiation on polyphenol content in harvested onions

Abstract We present a new food processing method to increase the content of polyphenols, which are a type of health-promoting food component, in harvested onions using atmospheric-pressure low-temperature plasma technologies. Harvested onions were locally irradiated for different times with a low-temperature air plasma jet generated near atmospheric pressure and stored in the dark for various periods. The plasma-irradiated area was 7 mm2. The plasma irradiation was performed without removing the onion peel. After storage following plasma irradiation, the onion was peeled and cut into the outer, middle, and inner parts, analysing polyphenol content at each edible onion part, namely the bulb. The polyphenol content in the onions irradiated for 30 min and stored for 3 days increased over that of unirradiated onions stored for the same period. This increase occurred regardless of the bulb parts. Neither the plasma-irradiated onions without storage nor the air gas-irradiated onions without air plasma irradiation exhibited a higher polyphenol content. In particular, quercetin aglycone, quercetin 4’-O-glucoside, quercetin 3-O-glucoside, and quercetin 3,4’-O-glucoside were increased in the polyphenol content, contributing to an increase in the antioxidative activity. NO3− and NO2− were introduced into water under an onion peel by air plasma irradiation for 30 min and decreased after 3 days of storage but remained. However, no H2O2 was introduced. The plasma-introduced NO3− was distributed throughout the onion owing to the plasmodesmata serving as channels for cell-to-cell transports of molecules. These results suggest that the increased polyphenol content originates from the transcriptional factors acting in stress responses to the plasma-introduced NO3− and NO2−, not the electric field-induced electroporation, in onion cells. The present study provides valuable insights into interactions between low-temperature air plasma jets and onion cells.

Thermal Performance of Lightweight Earth: From Prediction to Optimization through Multiscale Modeling

This study investigates the prediction of the thermal conductivity of lightweight earth and raw earth blocks incorporating plant aggregates. Given the high variability of raw materials, it is not currently possible to predict the thermal performance of this type of material before sample production. This is a major obstacle to using these eco-materials, although their use is widely encouraged to improve building performance under evolving regulatory frameworks such as The French RE2020 standard. The incorporation of plant aggregates into earth-based materials offers improved insulation properties without compromising their mechanical integrity, positioning them as promising sustainable alternatives. Mean-field homogenization techniques, including the Mori-Tanaka as well as double inclusion models, are used to develop predictive tools for thermal behavior, using rigorously selected experimental data. The selected methods are particularly relevant. The Mori-Tanaka model appears to be better suited when the proportion of aggregates is limited, whereas the double inclusion scheme proves its worth when a higher proportion of aggregates is incorporated. This study emphasizes the influence of aggregate types and processing methods on thermal conductivity, highlighting the need for precise formulation and processing techniques to optimize performance. This paper demonstrates the relevance of the applied homogenization techniques applied. It enables the real morphology of the materials studied, such as aggregate shape and intrinsic cracking, to be taken into account. It contributes to the advancement of eco-material modeling toward predictive digital twins, with the goal of simulating and optimizing complex material behavior under various environmental conditions.

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

Student wellbeing in vertical schools: a multilayered student voice approach for inclusion and influence

AbstractVertical schools are a new type of school in Australia, with little research available to guide designers and school leaders how to address the physical and social challenges that density and interiority add to the students’ schooling experience. As students capably communicate their experiences and perspectives about school spaces for wellbeing, pioneering students in three new vertical schools demonstrated the power of student voice in the Thriving in Vertical Schools project, a 3-year mixed-methods Australian Research Council Linkage project. Young people showed adults how their school spaces enable them to be, feel, and do activities where they feel capable, and how the vertical school environment contributes to wellbeing. Students communicated their voice through multiple layers: the student voice processes (methods), stories with sensory atmospheres (experience), and participating in impactful discussions with adults (action/influence). Adult designers and education leaders were interviewed several weeks after listening to students, identifying how student perspectives had influenced their work. This paper demonstrates how the combination of participatory voice-inclusive methods enabled students to communicate immersive experiences that brought light to interactions for school wellbeing at a level of granularity that adults had not had access to before to influence future designs. This paper argues for the value of attending to student voice and sensitivity in providing choice and options when doing so, so that students are supported to express themselves and their rich experiences in ways of their choosing and ways comfortable to them.

Tailoring Deformation Homogeneity of WE43 Alloy through Strain Rate Sensitivity and Back Pressure during Equal Channel Angular Pressing

AbstractEqual channel angular pressing (ECAP) of the magnesium alloys at room temperature owing to their limited workability is challenging. Successful ECAP processing of WE43 magnesium faces two main difficulties, heterogeneous distribution of the strain rate and also tensile strain accommodation on the top surface of the workpiece, leading to catastrophic segmentation of the alloy. In this paper, strain rate sensitivity (SRS) was studied to adopt a proper preprocessing of the material before ECAP processing. The SRS exponents, obtained from compression tests, revealed that solution treatment reduced the SRS of the alloy. To mitigate strain accommodation, an ECAP core-sheath configuration was used to induce back pressure for the sake of deformation homogeneity improvement. A combination of experimental processing and 3D finite element method simulations was applied to the solution-treated WE43 alloy with different core sizes and sheath materials. By finding the optimized core sizes and sheath materials with higher strengths, the differences in microhardness and equivalent plastic strain were reduced. Besides, the adequate magnitude of back pressure and the imposed fully compressive stress prevent fragmentation of the WE43 core during ECAP. After stepwise modifications, the plastic strain inhomogeneity index decreased from 1.340 to 0.671.

TinyBioGait—Embedded intelligence and homologous time approximation warping for gait biometric authentication from IMU signals

The gait of a subject follows a specific pattern, but variations exist that are unique to a subject but contrasting to other subjects. This can be utilized for biometric authentication to prevent impersonation during gait studies. However, due to the dynamic nature of gait, like changes in gait speed while walking, gait biometric authentications are challenging. In the state-of-the-art, although attempts have been made to use deep learning and other signal processing methods for biometric authentication, which obtained reliable results, these are either highly resource-consuming, require several sensors or need an expensive framework, making it challenging to implement this in many scenarios. Therefore, a knowledge gap exists to build a reliable, inexpensive and resource-efficient gait biometric authentication system. The paper proposes a method for using only one embedded IMU sensor with a microcontroller for tracking the motion of a subject, resource-efficient on-device elimination of the gait speed differences by proposing a homologous time approximation warping algorithm and building a resource-efficient TinyML model for reliable biometric authentication. Based on an experiment consisting of 20 human subjects with consent, the microcontroller’s on-device accuracy score for decision-making by TinyML was found to be 0.9276. The resource efficiency of the model based on memory profiling has been further discussed. Also, the prediction performance of the microcontroller with the proposed optimization was found to be only 8% slower compared to a personal computer, given that several thousands of processes run parallel on a personal computer. The work needs to be further tested for a larger sample space, and data privacy needs to be addressed.

Detection and assessment of hull plate corrosion damage based on image recognition techniques

ABSTRACT Corrosion damage can lead to a decrease in the ultimate strength and carrying capacity of ship structures, and even result in buckling or fracture. With the rapid development of deep learning, image recognition processing and convolutional neural networks are playing an important role in the field of corrosion damage detection and assessment. In this paper, corrosion damage images are obtained by taking photos of test pieces of accelerated corrosion tests, and a corrosion damage database is established to provide data for the establishment of the subsequent image corrosion damage level dataset and the training of the convolutional neural network model. Digital image processing methods and unsupervised learning clustering algorithms are used to identify and process corrosion images, and obtain corrosion parameters. Based on the corrosion parameters, the images are classified into different corrosion damage levels to establish a corrosion damage dataset with corrosion damage level labels. This dataset is used to train a convolutional neural network model and establish a corrosion damage level assessment interface. By inputting corrosion damage images, the corrosion damage level assessment results can be obtained.

Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information

In maritime transportation, a ship’s draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship’s draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship’s draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks.

Effects of color protection and enzymatic hydrolysis on the microstructure, digestibility, solubility and swelling degree of chestnut flour

Chestnuts, despite their nutritional value, pose challenges in starch processing, digestion, and absorption. This study employed various color-fixing formulations and processing methods to simulate the in vitro digestion of both untreated and enzymatically hydrolyzed chestnut flour. Changes in starch properties, digestion characteristics, and estimated glycemic index (eGI) were analyzed to understand how enzymatic hydrolysis affects chestnut flour properties. The results showed that the browning of chestnut flour was the least when the mass ratio of vitamin C, citric acid, and EDTA-Na2 was 9:1:0.3. Following treatment with pullulanase and glucoamylase, the content of rapidly digestible starch decreased to 10 %, while the content of slowly digestible starch and resistant starch increased to 62 % and 27 %, respectively. The eGI value of chestnut flour after enzymatic hydrolysis increased to 61.85–65.14, the hydrolysis rate was 78.37 %–89.20 %, the water holding capacity was 5.3–8.6, the solubility was 51.33 %–58.33 %, and the swelling degree decreased to 2.21–3.33 mL/g.

Impact of wheat processing on growth, serum biochemistry, and ruminal microbiota in sheep (Ovis aries)

This study investigated the impact of wheat processing methods (wheat flour vs wheat pellets) on the growth performance, serum biochemical parameters, and rumen microbiome composition in sheep. Results indicated that feeding of wheat flour resulted in significantly higher terminal weight and average daily gain (P < 0.05) and lower cholesterol and ALP04 levels (P < 0.05) in sheep compared to those fed wheat pellets. Analysis of 16s rDNA high-throughput sequencing data revealed significantly higher microbial richness (Chao1 index) in the rumen of sheep fed wheat flour (P < 0.05), even though the phylum-level composition dominated by Firmicutes, Bacteroidetes, and Proteobacteria was similar in both groups of sheep. Notably, sheep fed wheat flour were found to have a significantly higher relative abundance of Bacteroidetes (P < 0.05). At the genus level, Succinivibrionaceae_UCG-001 and Prevotella_1 were significantly more abundant in the rumen of sheep fed wheat flour (P < 0.05). Correlation analysis identified that both terminal weight and average daily gain were positively correlated with ruminal abundance of Bacteroidetes and Prevotella_1, while ALP04 was negatively correlated with the abundance of these taxa. Functional prediction using PICRUSt2 indicated enrichment of pathways related to the ABC-type glycerol-3-phosphate transport system, and periplasmic components in both wheat flour and pellet fed sheep. Overall, these findings suggest that dietary wheat flour modulates rumen microbiota composition, and improves growth performance in sheep.

Innovative approach in pistachio processing: Effect of explosion puffing drying on the quality characteristics of dried pistachios

The study examined the impact of explosion puffing (EP) drying at varying pressure levels (70, 140, and 210 kPa) followed by vacuum drying for 85, 100, and 115 min on pistachio quality. EP drying significantly reduced drying time, moisture content, and water activity. Samples dried at the pressure of 210 kPa for 85 min (P3T1) had a crispy texture of the kernel (p < 0.05). EP drying decreased the density of the pistachio kernels (p < 0.05), which led to a porous structure and a more favorable texture. Peroxide value (POV) decreased at lower pressure and drying time (p < 0.05), and the lowest POV was observed at 70 kPa for 100 min (P1T2). The EP had no significant effects on the free fatty acid (FFA), dimensions, and shell splitting degree of pistachios (p < 0.05). Among the sensory attributes, the sensory scores of texture and overall acceptance were significantly higher than those of the control (p < 0.05). In general, the textural and sensory characteristics of pistachio samples and the shorter drying time (2.5 times) in the EP with the 210 kPa pressure for 85 min (P3T1) were better than the control sample and other EP treatments, and it is suggested as a new drying method for pistachio processing.

Joint state and process inputs estimation for state-space models with Student’s t-distribution

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student’s t-distribution. The identification of UIs is accomplished through the recursive expectation–maximization (REM) approach. Specifically, in the E-step, a recursively calculated Q-function is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

Fermented sunflower meal in diets for Atlantic salmon under commercial-like farming conditions promotes gut lactic acid bacteria and controls inflammation in the distal intestine

The global increase of aquaculture production has caused an escalating demand for alternative protein feed ingredients. Agro-industrial by-products such as sunflower meal are relatively abundant and cheap, but the content of antinutritional factors and fiber in the raw products reduce nutritional value and limit the inclusion levels in the feed for nutritionally demanding species, including salmonids. Solid-state fermentation is a microbial processing method with the potential to reduce the content of fiber and antinutritional factors and improve nutritional value in plant protein raw materials. In this study, Atlantic salmon (Salmo salar) at two commercial-like fish farms were fed experimental diets containing either 5 % non-fermented sunflower meal (as a control diet), 5 % fermented sunflower meal or 10 % fermented sunflower meal. The aim was to compare the effects of fermented sunflower meal on growth performance, gut microbiota, distal intestine histology and gene expression of Atlantic salmon under different environmental conditions. The field trial lasted for 8 and 11 months in a coastal and fjord location, respectively. The findings revealed that diets with fermented sunflower meal sustained growth performance, improved intestinal health by reducing the prevalence of prominent inflammation and ectopic goblet cells, as well as down-regulated the expression of physiological pathways related to tissue damage and cell division. Additionally, 10 % fermented sunflower meal promoted the relative abundance of gut lactic acid bacteria Lactiplantibacillus and Lactobacillaceae after long-term feeding. Our results suggest that fermented sunflower meal is a suitable protein source for Atlantic salmon when included at a level of up to 10 % in the diet.