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In orbital welding technology, it is difficult and time-consuming to make a standard weld by manual welding. This requires a skilled weld. Therefore, a more optimal welding method than automatic rotary welding has been created to replace the conventional manual welding method. This significantly improves weld quality and reduces welding time. In this explanation, we have experimented with an automatic orbital welding device that integrates a filler wire-feeder device into the welding process. We refer to the American National Standard AWS D18.1/D18.1M: 2009 documents to review the weld results. We then analyze the advantages and disadvantages of the weld and propose solutions.

The peel of purple passion fruit (Passiflora edulis Sims.) can account for up to 60% of the total weight and contains usable organic compounds such as polymeric sugars, including pectin. The influences of ripeness stages and the extraction conditions (solvents, peel to solvent ratio, temperature, time) on the extract of crude pectin yield were investigated. The results indicated that pectin yield was independent of fruit maturation. The extraction efficiency reached its peak when extracted with citric acid and demonstrated optimal pectin production yield with the ratio of 1:5 (w/v) and at 95°C for 80 minutes. The extracted crude pectin was then applied to mango jam production. Jam made from extracted pectin had a good structure, moderate hardness, nice color, and harmonious taste. The ability to form coagulation on mango jam of crude pectin was weaker than commercial pectin. This study found that the topic's practical applicability to the processing of food products was significant.

Lactic acid bacteria (LAB) and probiotics promise specific health benefits to their host. However, good storage stability is a prerequisite for their functioning and industrial use. This study aimed to evaluate glutathione (GSH) as a potential protective agent for improving microbial stability deteriorated by freeze-drying, freeze-thawing, and cold treatments. In this study, the optimal concentration of glutathione (50% w/w) was 1%, showing effective protection on the viability and stability of various LAB strains (Lactiplantibacillus plantarum MG4229 and MG4296, Lactococcus lactis MG5125, Limosilactobacillus fermentum MG4295, Lacticaseibacillus paracasei MG5012, and Bifidobacterium animalis ssp. lactis MG741). Glutathione-containing protectants considerably improved the viability of all of these strains after freeze-drying compared with non-coated probiotics. Survivability in the gastrointestinal (GI) tract, accelerated stability tests, and adhesion assays on intestinal epithelial cells were performed to determine whether glutathione enhances bacterial stability. Based on morphological observations, protectants containing GSH were coated onto the cell surface, resulting in effective protection against multiple external stress stimuli. The applicability of GSH as a new and effective protective agent can improve the stability and viability of various probiotics with anti-freezing and anti-thawing effects.

Brain-computer interface (BCI) technology represents a fast-growing field of research and applications for disabled and healthy people, which is a direct communication pathway to translate the neural information into an active command. Owing to the complicated headset structure, low accuracies, extended training periods, and nonstationary noises, BCI still has many challenges that should be dealt with for further facilitation of BCI technology use in daily life. In this study, a simplified synchronized hybrid BCI system is proposed for multiple command control by the electroencephalograph (EEG) signals in the motor cortex. This system can detect the single motor imagery (MI) task, single steady-state visually evoked potential (SSVEP) task, and hybrid MI + SSVEP tasks simultaneously (total ten mental tasks) via 2 EEG channels with high accuracy. The fast independent component analysis algorithm is employed to hybrid signals for obtaining clear EEG signals resulting from denoising. Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. Finally, the hybrid MI + SSVEP system with a simple structure achieves a high accuracy of 95.56%. Additionally, the single MI-based and the SSVEP-based BCI system obtain the classification accuracy of 90.16% and 93.21%, respectively. Experimental results indicate that the synchronized hybrid BCI system could achieve multiple command control with a simple structure. In comparison with the single MI-based and the SSVEP-based BCI system, the hybrid MI + SSVEP BCI system shows a stable performance and higher efficiency. The proposed investigation provides a new method for the multiple command control by a hybrid BCI system. Also, the proposed BCI system offers the possibility of friendly utilization for disabled people because of its reliability, ease of use, and simplified headset structure.

To expect the positive physiological functions from probiotic consumption, the lactic acid bacteria should survive, partially, in the gastrointestinal (GI) tract. This study aimed to evaluate the viable stability of probiotic bacterial cells (Lactobacillus plantarum MG989, L. fermentum MG901, Streptococcus thermophilus MG5140, Lactococcus lactis MG5125, and Enterococcus faecium MG89-2) that were coated with sodium alginate and pumpkin powder (SP) by comparing their resistance in simulated intestinal fluid (with pancreatin) and simulated gastric fluid (with pepsin) in vitro with that of the non-coated free cells. The viable stability was determined by counting cells with colony forming unit (CFU) from agar plate culture of SP coated and non-coated free cells in simulated GI conditions. Survival rate enhanced up to 28.7% and 14.0% in the condition of simulated gastric fluid and simulated intestinal fluid, respectively. The results showed that the SP coated cells exhibited considerably greater resistance to the simulated gastric fluid than the activated cells (p < 0.001), showing that the SP coating may enhance the survival of probiotic bacteria after consumption during their transit through the GI tract after freeze-drying process.

In the past decades, the emerging concern about food safety has led to the increasing demand for monitoring food quality across the world. Aiming towards a novel solution for monitoring food, this study proposes a non-destructive method with self-powering capability for online food monitoring, which can be extendable to IoT applications. Furthermore, the study introduces a novel deep neural network model to predict different states of food quality based on the monitoring results. To monitor the variation in food quality, the paper proposes the detection of total volatile organic compounds (TVOCs) inside the food packages, which have been released during food deterioration. A low-power sensor mote comprised of a capacity humidity sensor and a metal-oxide (MOX) gas sensor was manufactured for this purpose. The self-powering capability of the mote is provided through an energy harvester module, which benefits from the far-field Radio Frequency Energy Harvesting (RFEH) technology. The operating frequency of the module was chosen at the 915-MHz ISM band. The analysis of the harvester performance showed that the harvester could generate 3.3-V dc with an RF input power of as low as -8 dBm, which was sufficient for the mote operation. To verify the proposed solutions, a demonstration to monitor the deterioration of packaged pork and fish was conducted in eight days under ambient and refrigerated storage conditions, using the self-developed RF-powered sensor mote. The raw variations in TVOCs were analyzed to evaluate the reliability of the proposed TVOC-based method. A one-dimensional (1-D) convolutional neural network (CNN) model was trained on the TVOCs dataset to predict different states of food quality. To investigate the applicability of the proposed 1-D CNN to multi-class determination of food quality, two other supervised machine learning algorithms using 2-D inputs, including Multilayer Perceptron (MLP), and Support Vector Machine (SVM), are studied. Their classification accuracies based on the confusion matrix are identified and compared.

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

Oxidative stress is a phenomenon caused by an imbalance between the production of reactive oxygen species and antioxidant defenses. It plays an important role in numerous disease states, including chronic kidney disease, neurological disorders, cardiovascular diseases, diabetes, and cancer. Lactic acid bacteria (LAB) are known to have prominent antioxidant properties. Therefore, this study aimed to measure the antioxidant activity and anti-inflammatory potential of LAB isolated from animals for the efficient use of probiotics with host specificity. Antioxidant activity measurements of sixteen strains revealed that ABTS radical scavenging activities ranged from 26.3 to 57.4%, and DPPH free radical scavenging activities ranged from 4.7 to 13.5%. Based on the antioxidant activity assessment, five strains (Enterococcus faecium MG9003(YH9003), Enterococcus faecium MG9007(YH9007), Lactobacillus reuteri MG9012(YH9012), Lactobacillus fermentum MG9014(YH9014), and Pediococcus pentosaceus MG9015(YH9015)) were selected with the consideration of fermentation productivity (>1 × 109 CFU/g). The selected strains exhibited nitric oxide inhibition and inhibited inducible nitric oxide synthase and cyclooxygenase expression. Furthermore, probiotic properties, including intestinal adhesion and stability, were identified. Our results show that the selected animal-derived strains can be effective probiotic candidates for potential effects on animal hosts.

Most radio frequency (RF) energy harvesters exhibit extremely nonlinear behavior during the harvesting phase, which renders the simulation of these circuits most challenging in design. This paper proposes a method of efficient modeling this nonlinearity based on empirically achieved characteristics of RF energy harvesting. A model using a multi-layer artificial neural network (ANN) is introduced to approximate this behavior. A harvester prototype that uses the far-field RF energy harvesting technique at 915 MHz is developed to verify the proposed model. The results highlight good agreement between the predicted and the observed outcomes of the harvester. With its flexibility, the proposed model can be further applied to characterize any nonlinear device. In addition, a self-powered system embedded with a fully-passive sensor device to monitor food storage profile is presented in the paper. An analysis of the power harvesting performance shows that the device can self-start its operation where the RF power is as low as −10.5 dBm, and the maximum achievable power conversion efficiency is 43%. To demonstrate the applicability of the proposed self-powered food monitoring system, an experiment is carried out using pork. Support vector machine (SVM) classifiers with two types of features are investigated to compare their performance of classifying three levels of food condition. As a result, the best classification accuracy of 95.7% is achieved.

The red seaweed, Gelidium amansii, contains proteins such as R-phycoerythrin and R-phycocyanin, as well as polysaccharides. Initial protein extraction from G. amansii yielded 53 μg/g R-phycoerythrin and 56 μg/g R-phycocyanin. After protein extraction, monosaccharides were produced using hyper thermal acid hydrolysis and enzymatic saccharification. Acetone–butanol–ethanol (ABE) was produced by Clostridium acetobutylicum KCTC 1790. To overcome the different pH requirements for monosaccharide consumption and solvent production, a two-stage pH control culture strategy was developed. Fermentation at pH 6.0 supported a high cell growth rate and both glucose and galactose were consumed completely. The pH was then shifted from 6.0 to 4.5 to produce ABE after 84 h. The maximum ABE concentration reached 15.5 g/L in comparison to 3.1 g/L under fermentation without pH control. The two-stage pH control strategy is a suitable method for ABE production from G. amansii.

Water hyacinth (Eichhornia crassipes) was used as a feedstock for ethanol production. The optimal hyper-thermal (HT) acid hydrolysis conditions were 8% (w/v) slurry content, 200 mM H2SO4, at 160 °C for 20 min and enzymatic saccharification for 48 h using an enzyme mixture of 20 units/mL Viscozyme L and Cellic C Tec2. After pretreatment, 48.2 g/L monosaccharides were obtained. Fermentation was conducted with wild and adapted Saccharomyces cerevisiae, Pichia stipitis and Candida lusitaniae. Wild-type S. cerevisiae, P. stipitis, and C. lusitaniae produced 15.3, 19.5 and 22.7 g/L of ethanol, respectively. Adaptive evolution was carried out on 6% (w/v) xylose. S. cerevisiae, P. sipitis and C. lusitaniae adapted to xylose produced 15.3, 21.4 and 23.9 g/L of ethanol with YEtOH of 0.32, 0.44 and 0.49, respectively. These results indicate that water hyacinth has potential as a feed stock for ethanol.

Optimal conditions of hyper thermal (HT) acid hydrolysis of the Saccharina japonica was determined to a seaweed slurry content of 12% (w/v) and 144 mM H

Acetone, butanol, and ethanol (ABE) were produced following the separate hydrolysis and fermentation (SHF) method using polysaccharides from the green macroalgae Enteromorpha intestinalis as biomass. We focused on the optimization of enzymatic saccharification as pretreatments for the fermentation of E. intestinalis. Pretreatment was carried out with 10% (w/v) seaweed slurry and 270-mM H2SO4 at 121 °C for 60 min. Monosaccharides (mainly glucose) were obtained from enzymatic hydrolysis with a 16-U/mL mixture of Celluclast 1.5 L and Viscozyme L at 45 °C for 36 h. ABE fermentation with 10% (w/v) E. intestinalis hydrolysate was performed using the anaerobic bacteria Clostridium acetobutylicum with either uncontrolled pH, pH controlled at 6.0, or pH controlled initially at 6.0 and then 4.5 after 4 days, which produced ABE contents of 5.6 g/L with an ABE yield (YABE) of 0.24 g/g, 4.8 g/L with an YABE of 0.2 g/g, and 8.5 g/L with an YABE of 0.36 g/g, respectively.

This study aims to develop a BCI system using Electroencephalogram (EEG) for lie detection. EEG signal is obtained in a non-invasive manner with eight electrodes mounted at designated positions on the scalp employing a hand-made design EEG headset. The selected input for classification in this research comprises 11 features extracted across all three domains: time, frequency, and time-frequency. In which nine time-domain features include: Mean, Variance, Standard Deviation, Skewness, Kurtosis, Permutation Entropy, SVD Entropy, Approximate Entropy, and Sample Entropy. Over the frequency domain and time-frequency domain, we have the Spectral Entropy and an 8-dimensional image computed by Continuous Wavelet Transform. Modern researches indicate that Neural Networks have an extraordinary capacity to classify patterns with diverse inputs such as data sequences and pictures. Four variations are applied in this study, including the Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). In which MLP, LSTM, GRU are worked on the first ten time and frequency features, while the 8-dimensional image composed after the Continuous Wavelet Transform is adopted as the input of the CNN network. The evaluation results demonstrated the feasibility of our EEG-Based BCI system in Deception recognition.

Postbiotics are a promising functional ingredient that can overcome the limitations of viability and storage stability that challenge the production of probiotics. To evaluate the effects of postbiotics on oral health, eight spent culture supernatants (SCSs) of probiotics were prepared, and the effects of SCSs on Streptococcus mutans-induced cariogenic biofilm formation and the receptor activator of the nuclear factor κB ligand (RANKL)-induced osteoclastogenesis were evaluated in RAW 264.7 macrophages. SCS of Lactobacillus salivarius MG4265 reduced S. mutans-induced biofilm formation by 73% and significantly inhibited tartrate-resistant acid phosphatase (TRAP) activity, which is a biomarker of mature osteoclasts in RAW 264.7 macrophages. The suppression of RANKL-induced activation of mitogen activated the protein kinases (c-Jun N-terminal kinase, extracellular signal-regulated kinase, and p38) and nuclear factor κB pathways, as well as the upregulation of heme oxygenase-1 expression. The suppression of RANK-L-induced activation of mitogen also inhibited the expression of transcriptional factors (c-fos and nuclear factor of activated T cells cytoplasmic 1) and, subsequently, osteoclastogenesis-related gene expression (tartrate-resistant acid phosphatase-positive (TRAP), cathepsin K, and matrix metalloproteinase-9).Therefore, SCS of L. salivarius MG4265 has great potential as a multifunctional oral health ingredient that inhibits biofilm formation and suppresses the alveolar bone loss that is associated with periodontitis.

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study&mdash, a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time&mdash, might also offer a reference for the other BCI controlled applications.

The waste seaweed from Gwangalli beach, Busan, Korea was utilized as biomass for ethanol production. Sagassum fulvellum (brown seaweed, Mojaban in Korean name) comprised 72% of the biomass. The optimal hyper thermal acid hydrolysis conditions were obtained as 8% slurry contents, 138 mM sulfuric acid, and 160oC of treatment temperature for 10 min with a low content of inhibitory compounds. To obtain more monosaccharides, enzymatic saccharification was carried out with Viscozyme L for 48 h. After pretreatment, 34 g/l of monosaccharides were obtained. Pichia stipitis and Pichia angophorae were selected as optimal co-fermentation yeasts to convert all of the monosaccharides in the hydrolysate to ethanol. Co-fermentation was carried out with various inoculum ratios of P. stipitis and P. angophorae. The maximum ethanol concentration of 16.0 g/l was produced using P. stipitis and P. angophorae in a 3:1 inoculum ratio, with an ethanol yield of 0.47 in 72 h. Ethanol fermentation using yeast co-culture may offer an efficient disposal method for waste seaweed while enhancing the utilization of monosaccharides and production of ethanol.

This paper aims to develop a Brain-computer interface (BCI) speller utilizing eyes-closed and double-blinking electroencephalogram (EEG) signals and based on asynchronous mechanism. The proposed system comprises a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected “eyes-closed”(EC) event induces the “select”command, whereas a “double-blinking”(DB) event functions as the “undo”command. A three-class support vector machine classifier involving EEG signal analysis of three groups of events (“eyes-open” - idle state, EC, and DB) is proposed. The results show that the proposed BCI can achieve an overall accuracy of 93.8% for multi-class classification. The proposed speller is then employed in the online experiment of spelling the word “bcispeller”using a 1 s time window. Consequently, it achieves an average accuracy of 92.3% and an average spelling rate of 5 letters/min. Overall, this paper shows improvement with high accuracy and spelling rate demonstrating the feasibility and reliability of implementing a real-world BCI speller.

Owing to industrial evolution, a huge mass of toxic metals, including Co, Cu, Cr, Mn, Ni, Pb, and Zn, and metalloids, such as As and Sb, has inevitably been released into the natural environment and accumulated in soils or sediments. Along with modern industrialization, many mineral mines have been explored and exploited to provide materials for industries. Mining industries also generate a vast amount of waste, such as mine tailings, which contain a high concentration of toxic metals and metalloids. Due to the low economic status, a majority of mine tailings are simply disposed into the surrounding environments, without any treatment. The mobilization and migration of toxic metals and metalloids from soils, sediments, and mining wastes to water systems via natural weathering processes put both the ecological system and human health at high risk. Considering both economic and environmental aspects, bioleaching is a preferable option for removing the toxic metals and metalloids because of its low cost and environmental safety. This chapter reviews the recent approaches of bioleaching for removing toxic metals and metalloids from soils, sediments, and mining wastes. The comparison between bioleaching and chemical leaching of various waste sources is also discussed in terms of efficiency and environmental safety. Additionally, the advanced perspectives of bioleaching for environmental remediation with consideration of other influencing factors are reviewed for future studies and applications.

This study examined the pretreatment, enzymatic saccharification, and fermentation of the red macroalgae Gracilaria verrucosa using adapted saccharomyces cerevisiae to galactose or NaCl for the increase of bioethanol yield. Pretreatment with thermal acid hydrolysis to obtain galactose was carried out with 11.7% (w/v) seaweed slurry and 373 mM H2SO4 at 121 °C for 59 min. Glucose was obtained from enzymatic hydrolysis. Enzymatic saccharification was performed with a mixture of 16 U/mL Celluclast 1.5L and Viscozyme L at 45 °C for 48 h. Ethanol fermentation in 11.7% (w/v) seaweed hydrolysate was carried out using Saccharomyces cerevisiae KCTC 1126 adapted or non-adapted to high concentrations of galactose or NaCl. When non-adapted S. cerevisiae KCTC 1126 was used, the ethanol productivity was 0.09 g/(Lh) with an ethanol yield of 0.25. Ethanol productivity of 0.16 and 0.19 g/(Lh) with ethanol yields of 0.43 and 0.48 was obtained using S. cerevisiae KCTC 1126 adapted to high concentrations of galactose and NaCl, respectively. Adaptation of S. cerevisiae KCTC 1126 to galactose or NaCl increased the ethanol yield via adaptive evolution of the yeast.

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

Unfortunately, the author name was wrongly published as Pailin Sukwang.instead of Pailin Sukwong.

In this study, bioethanol was produced from the seaweed Gelidium amansii as biomass through separate hydrolysis and fermentation (SHF) processes. The SHF processes examined in this study include thermal acid hydrolysis pretreatment, enzymatic saccharification, detoxification, and fermentation. Thermal acid hydrolysis pretreatment was conducted using H2SO4, with a slurry content of 8–16% and treatment time of 15–75 min. The optimal conditions for thermal acid hydrolysis pretreatment were 12% (w/v) seaweed slurry content and 180 mM H2SO4 at 121 °C for 45 min, at which 26.1 g/L galactose and 6.8 g/L glucose were produced. A monosaccharide (mainly glucose) was also obtained from the enzymatic saccharification of thermal acid hydrolysate using 16 U/mL Celluclast 1.5 L enzyme at 45 °C for 36 h. Detoxification was performed using the adsorption method with activated carbon, the overliming method with Ca (OH)2, and the ion exchange method with polyethyleneimine. Among those detoxification methods, activated carbon showed the best performance for hydroxymethylfurfural removal. Ethanol fermentation was performed using 12% (w/v) seaweed hydrolysate with Saccharomyces cerevisiae adapted to galactose as well as various detoxification treatments.

Bioethanol was produced from Kappaphycus alvarezii seaweed biomass using separate hydrolysis and fermentation (SHF). Pretreatment was evaluated for 60 min at 121°C using 12% (w/v) biomass slurry with 364 mM H2SO4. Enzymatic saccharification was then carried out at 45°C for 48 h using Celluclast 1.5 L. Ethanol fermentation with 12% (w/v) K. alvarezii hydrolyzate was performed using the yeasts Saccharomyces cerevisiae KCTC1126, Kluyveromyces marxianus KCTC7150, and Candida lusitaniae ATCC42720 with or without prior adaptation to high concentrations of galactose. When non-adapted S. cerevisiae, K. marxianus, and C. lusitaniae were used, 11.5 g/l, 6.7 g/l, and 6.0 g/l of ethanol were produced, respectively. When adapted S. cerevisiae, K. marxianus, and C. lusitaniae were used, 15.8 g/l, 11.6 g/l, and 13.4 g/l of ethanol were obtained, respectively. The highest ethanol concentration was 15.8 g/l, with YEtOH = 0.43 and YT% = 84.3%, which was obtained using adapted S. cerevisiae.

Hyper thermal (HT) acid hydrolysis of Kappaphycus alvarezii, a red seaweed, was optimized to 12% (w/v) seaweed slurry content, 180mM H2SO4 at 140°C for 5min. The maximum monosaccharide concentration of 38.3g/L and 66.7% conversion from total fermentable monosaccharides of 57.6g/L with 120gdw/L K. alvarezii slurry were obtained from HT acid hydrolysis and enzymatic saccharification. HT acid hydrolysis at a severity factor of 0.78 efficiently converted the carbohydrates of seaweed to monosaccharides and produced a low concentration of inhibitory compounds. The levels of ethanol production by separate hydrolysis and fermentation with non-adapted and adapted Kluyveromyces marxianus to high concentration of galactose were 6.1g/L with ethanol yield (YEtOH) of 0.19 at 84h and 16.0g/L with YEtOH of 0.42 at 72h, respectively. Development of the HT acid hydrolysis process and adapted yeast could enhance the overall ethanol fermentation yields of K. alvarezii seaweed.

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO2) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO2 concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO2 concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO2 concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO2 concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.