Soil carbon supplementation is known to stimulate plant growth by improving soil fertility and plant nutrient uptake. However, the underlying process and chemical mechanism that could explain the interrelationship between soil carbon supplementation, soil micro-ecology, and the growth and quality of plant remain unclear. In this study, we investigated the influence and mechanism of soil carbon supplementation on the bacterial community, chemical cycling, mineral nutrition absorption, growth and properties of tobacco leaves. The soil carbon supplementation increased amino acid, carbohydrates, chemical energy metabolism, and bacterial richness in the soil. This led to increased content of sugar (23.75%), starch (13.25%), and chlorophyll (10.56%) in tobacco leaves. Linear discriminant analysis revealed 49 key phylotypes and significant increment of some of the Plant Growth-Promoting Rhizobacteria (PGPR) genera (Bacillus, Novosphingobium, Pseudomonas, Sphingomonas) in the rhizosphere, which can influence the tobacco growth. Partial Least Squares Path Modeling (PLS-PM) showed that soil carbon supplementation positively affected the sugar and starch contents in tobacco leaves by possibly altering the photosynthesis pathway towards increasing the aroma of the leaves, thus contributing to enhanced tobacco flavor. These findings are useful for understanding the influence of soil carbon supplementation on bacterial community for improving the yields and quality of tobacco in industrial plantation.
This study was conducted to study the influence of the development of values in 21st century education on the forming of student’ personality in rural Under-Enrolled School (SKM) in Sabah. In addition, this study examines the difference in the mean score of the development of value approach in 21st century education and the forming of student’ personality in rural Under-Enrolled School (SKM) in Sabah. This study uses non-experimental design and quantitative methods. A total of 209 SKM teachers were selected to be sampled in this study. This sample was determined using a simple random sampling method. Questionnaire instruments were used to obtain information from the respondents. The primary data obtained from the respondents were analyzed using Statistical Package for Social Sciences (SPSS) software using inferential statistics analysis involving Linear Regression analysis. The results of the analysis show that there is a significant positive and strong influence between the development of value approach in 21st century education on the forming of student’ personality in rural Under-Enrolled School (SKM) in Sabah (Beta=0.88, t=27.65). The results of this study are expected to provide useful input to various parties, especially the Ministry of Education Malaysia in an effort to improve the school's ability to produce students who are personal and able to contribute to the development of the country.
Behaviour is the way an individual translate input derived from interactions into action and reaction, either through verbal communication or through nonverbal communication. Behaviour is also influenced by an individual's emotions to respond or to react when interacting in the social context of society. However, social symptoms are getting a huge blow from the teens. The negativity in socialization are intensely prominent among teenagers. The behaviour of a school teen who violates the norm is a behaviour of a delinquent. These cases of misconduct have a negative impact on the wellbeing and peace of life in the community. In addition, there have been cases of social collapse of morals now widely circulating in the media regarding sexually explicit acts such as rape and premarital pregnancy. Several social factors can be identified to influence teens, of which most of them are still schooling. Therefore, this research aims to study the factors, relevance and differences of socialization in influencing individual behaviour. Total of 120 students, including 70 boys from Sekolah Tunas Bakti Sg. Besi and 50 girls from Asrama Bahagia Kg. Pandan. Both schools are those who are involved in juvenile cases and under control of the Department of Social Welfare (JKM). The design of the study is descriptive. Data was collected through a three-part questionnaire, which comprises of A Background Information of Students, B Five Socialization Factors, and C Aggressive Behaviour. The data collected was then analysed using SPSS (Statistical Package for Social Science) to evaluate percentages, frequency, correlation, T-test and Anova. Results found that male students were more likely to be influenced by mass media factors while female students were more likely to be influenced by peers. Ultimately, the research results reveal that the behaviour of individuals is influenced by three main factors, namely the media, peers and individual self. There were no significant differences between male and female gender for socialization factors influencing aggressive behavior. Therefore, some proposals have been formulated such as form new acts, emphasizing the importance of family as well as educational institutions such as schools.
In recent years, the growing number and active spread of antibiotic
resistance have become a major concern globally. This forces the need to
discover, analyse and develop new kinds of antibiotics, especially among
plants. There are still limited data on the extracts from Hylocereus
polyrhizus fruit as antimicrobials. In this study, the disc diffusion and brothmicrodilution methods are used to investigate the antimicrobial activity of
methanol and ethanol extracts of Hylocereus polyrhizus flesh towards
selected bacteria (Staphylococcus epidermidis, Staphylococcus aureus,
Proteus mirabilis, Bacillus cereus, Pseudomonas aeruginosa, and
Escherichia coli). The methanolic extract possesses better antimicrobial
properties. The methanolic extract of H. polyrhizus showed significant
antimicrobial activities against all Gram-positive bacteria, and one of the
Gram-negative bacteria, which is better compared to ethanolic extract. The
range of minimum inhibitory concentration (MIC) and minimum bactericidal
concentration (MBC) are 125mg/mL to 250mg/mL. In conclusion, this study
shows that H. polyrhizus could be used as an alternative for the pre-existing
antimicrobial agent.
Ascorbic acid is a water-soluble vitamin and commonly known as Vitamin
C. Human cannot synthesize the ascorbic acid. It is naturally rich in citrus
fruits and most of the vegetables. Hence, these fruits and vegetables become
main source of ascorbic acid to meet the requirement of a dietary intake. The
differential pulse anodic stripping voltammetric (DPASV) technique has
been proposed for ascorbic acid analysis in commercial Roselle juices based
on the electrochemical oxidation of the ascorbic acid at glassy carbon
electrode (GCE). Phosphate buffer solution (PBS) at pH 5.0 was used as a
supporting electrolyte. The optimum parameters used were Ei = +0.00 V, Ef
= 0.80 V, tacc = 60 s,
Nurkhalidah Sabrina Ikmalhisam, Nur Syakirah Abdul Aziz, Michelle Clare Mah, Tengku Intan Baizura Tengku Jamaluddin, Hazmyr Abdul Wahab, Mohd Shawal Firdaus Mohammad, et al.
The objective of this study is to assess Universiti Teknologi MARA (UiTM)
dental students’ perspective on a novel patient educational kit in helping them
to deliver post-dental extraction care instructions. All undergraduate clinical
year dental students of the Faculty of Dentistry, UiTM were recruited for this
cross-sectional study. 87.8% of these students have volunteered to participate
in this study. An 8-minutes introductory video of the Post-Dental Extraction
Care kit (PDEC-kit) was played when simultaneously showcasing the tools in
the PDEC-kit to the participants. The participants then answered a set of
validated self-administered questionnaires online on their perception and
suggestions for improvement of the PDEC-kit, which comprises of 20 items that
are rated by a 7-point Likert scale along with 7 open-ended questions. A total
of 216 students participated voluntarily in this study. A vast majority of
participants agreed that the PDEC-kit is useful (99.1%), easy to use (98.7%),
and can improve patient’s understanding regarding post-dental extraction care
instructions (99.1%). The information provided in the kit was also found to be
appropriate for the patients (97.2%). Interestingly, students who had clinical
experience in performing dental extractions have rated significantly higher
scores for half of the questions (p
Pogostemon cablin (patchouli) is a medicinal herb well known for its
essential oil derived from the leaves. Patchouli oil shows excellent base note
in fragrance industries for its fixatives properties and its patchouli alcohol
(patchoulol) is used as quality indicator for its oil. However, the P. cablin is
the only commercial source of patchoulol and cannot be obtained
synthetically in the laboratory. Higher demand in the production of its
essential oil gave a significant contribution for in vitro grown P.cablin to
meet the market supply for industries. Hence, in this study, the essential oil
in both in vitro and ex vitro P.cablin were extracted from its leaves by means
of hydrodistillation method and its phytochemical constituents were
identified and compared using Gas Chromatography Mass Spectrometry.
The yield and quality of its essential oil from both in vitro and ex vitro
P.cablin’s leaves were investigated. In vitro patchouli essential oil extraction
gaves higher yield (40 ml) than the ex vitro patchouli essential oil (26 ml)
under similar condition for hydrodistillation. Six major components were
identified through GC-MS and was compared between two samples which
are β- patchoulene, Caryophyllene, α- guaiene, α- cedrene, α- bulnesene and
Patchouli alcohol. The patchoulol, which is the main constituents that is
important in fixative had doubled (42.18 %) in the in vitro P.cablin essential
oil compared to ex vitro (29.24%). This finding was reflected based on the
peak area percentage of each substance through GC-MS. Other constituents
in the in vitro P.cablin were found still competitive to the ex vitro in slightly
lower values. Overall, in vitro P.cablin showed higher yield and quality
compared to the ex vitro grown P.cablin.
Repair and maintenance in power distribution is an important factor that
affects the continuous productivity services and power efficiency in electrical
supply systems. Thermographic inspection has been often used as a
maintenance tool, as it allows detection of early-stage failure from the system
in electrical distribution. Failure in the system can lead to catastrophic
failure like a high-voltage arc fault. The presence of fault is caused by the
higher temperature of the instrument that leads to the formation of hotspots.
The use of infrared inspection is useful in detecting the hotspot that is hardly
noticeable. It helps to overcome the problems that arise during operation
and maintenance in the distribution systems. In this research, a fault
detection system is proposed with the application of Artificial Neural
Network (ANN) in identifying faults on electrical equipment. This method
was trained by using the temperature parameter on the IR images taken from
TNB Distribution. As a result, it will lead to faults detection. Thus, the
purpose of this project is to ensure the correct recommendation of corrective
actions in the maintenance procedure of the electrical system. The actions to
the detection of faults taken are based on the results of the temperature
measured. The neural network training performance for the temperature of
hotspot detection was developed with a minimum error of 0.00084165 MSE
at epoch 39. The study shows the best-fitting allows detection of early-stage
failure. It can be concluded that the current method in conducting the
prediction process by using Thermographic inspection is suitable for
electrical equipment based on the training result.
Dengue is a globally known infection in which the virus is transmitted by
mosquitoes and can lead to death. Selangor has been reported to have the
highest incidence of dengue infections among the communities in Malaysia.
There is currently a new pandemic, COVID-19, which occurred worldwide,
including Selangor, which led to this study on the pattern of dengue cases
during COVID-19. The aim of this study is to develop the best model to
predict the future value of dengue cases in Selangor. In order to meet the
objectives, the ARIMA method and the Holt-Winters method were used to
evaluate dengue case data collected in Selangor. The best model was chosen
by evaluating the Mean Square Error (MSE), Root Mean Square Error
(RMSE) and Mean Absolute Percent Error (MAPE) measurement errors.
Then, the forecasted number of dengue cases was calculated using the best
model generated. The best model to forecast dengue cases in Selangor is the
Additive Holt-Winters model since it showed the lowest values of all
measurement errors compared to the Multiplicative Holt-Winters and
ARIMA (1,1,0) models.
Covid-19 outbreak has caused economic policy uncertainties. The first
COVID-19 case was reported in Wuhan, China at the end of 2019. The virus
spread escalated in volume during the Chinese New Year festival. World
Health Organisation declared a global health emergency on January 30,
2020. The global financial market was badly hit when oil price slumped to
over 30% and oil price war also occurred between Saudi and Russia. The
stock market, too displayed signs of being impacted by the virus outbreak. It
is very important to determine if COVID-19 has affected economic
uncertainty and oil prices or the oil price fall has affected the economic
instability and stock market volatility Three models were used in this study
to analyse the relationship between the recent spread of COVID-19 in
Malaysia, Malaysia stock market, oil prices in Malaysia and Global
Economic Policy Uncertainty (GEPU) in time-frequency domain. The
coherence wavelet methods were used to analyse the movement of each
variable and to evaluate the interactions between the selected variables from
January 25, 2020 to May 25, 2020. The Wavelet-based Granger Causality
were applied to test the robustness of the coherence wavelets. Three main
conclusions from this study were i) oil prices were influenced by stock market
and GEPU index, ii) stock markets and GEPU index had interactions with
the pandemic and iii) short term effects existed between the pandemic and
oil prices. More accurate results concerning the volatility of GEPU, stock
market and oil price, can be obtained in future research works in this area if
Malaysia Economic Policy Uncertainty index is used.
Ge is considered to have several advantages over Si due to its high mobility
and direct bandgap, which makes it ideal for optoelectronic applications.
The manipulation of bulk Ge into small structures has drawn a lot of interest
due to the numerous distinctive properties caused by the impact of size
quantization. Porous materials are ideally suited for sensing application due
to theirlarge effective surface area beside the fabrication of porous is simple.
In this work, porous Ge is investigated for potential visible to near-infrared
metal semiconductor metal (MSM) photodetector. The study investigated the
performance and characterization of porous Ge (P-Ge) on Si substrate at
different depths of porous (1 µm, 0.25 µm and 0.01 µm) by using SILVACO
Athena and Atlas device simulator. Athena process simulator was used to
construct the device structure while ATLAS device simulator was used to
characterize the electrical and optical characteristics’ effect on the different
sizes of the P-Ge fabricated on the Si substrate. The comparison of the porous
devices were then made with bulk Ge devices (bulk Ge-on-Si, bulk Ge-onGe) to identify the exploitation of porosity resulted in a significant
performance of current gains, spectral response, Schottky barrier height,
and also photo and dark current. It was found that the P-Ge at 0.01 µm depth
showed an improved current gain compared to other porous structures while
bulk Ge-on-Si obtain greater current gain than bulk Ge-on-Ge. This evidence
indicates that P-Ge produces a better performance of MSM photodetector
than the bulk device. The spectral response of P-Ge shows a peak response
at 800nm, which is the near-infrared (IR) region supporting the feasibility of
the P-Ge to be utilized for visible to near IR photodetection.
The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.
Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. Different image processing techniques have been proposed to detect and diagnose neovascularization from fundus images. Recently, deep learning methods are getting popular in neovascularization detection due to artificial intelligence advancement in biomedical image processing. This paper presents a semantic segmentation convolutional neural network architecture for neovascularization detection. First, image pre-processing steps were applied to enhance the fundus images. Then, the images were divided into small patches, forming a training set, a validation set, and a testing set. A semantic segmentation convolutional neural network was designed and trained to detect the neovascularization regions on the images. Finally, the network was tested using the testing set for performance evaluation. The proposed model is entirely automated in detecting and localizing neovascularization lesions, which is not possible with previously published methods. Evaluation results showed that the model could achieve accuracy, sensitivity, specificity, precision, Jaccard similarity, and Dice similarity of 0.9948, 0.8772, 0.9976, 0.8696, 0.7643, and 0.8466, respectively. We demonstrated that this model could outperform other convolutional neural network models in neovascularization detection.
In acoustic receiver design, the receiving sensitivity and bandwidth are two primary parameters that determine the performance of a device. The trade-off between sensitivity and bandwidth makes the design very challenging, meaning it needs to be fine-tuned to suit specific applications. The ability to design a PMUT with high receiving sensitivity and a wide bandwidth is crucial to allow a wide spectrum of transmitted frequencies to be efficiently received. This paper presents a novel structure involving a double flexural membrane with a fluidic backing layer based on an in-plane polarization mode to optimize both the receiving sensitivity and frequency bandwidth for medium-range underwater acoustic applications. In this structure, the membrane material and electrode configuration are optimized to produce good receiving sensitivity. Simultaneously, a fluidic backing layer is introduced into the double flexural membrane to increase the bandwidth. Several piezoelectric membrane materials and various electrode dimensions were simulated using finite element analysis (FEA) techniques to study the receiving performance of the proposed structure. The final structure was then fabricated based on the findings from the simulation work. The pulse-echo experimental method was used to characterize and verify the performance of the proposed device. The proposed structure was found to have an improved bandwidth of 56.6% with a receiving sensitivity of -1.8864 dB rel 1 V µPa. For the proposed device, the resonance frequency and center frequency were 600 and 662.5 kHz, respectively, indicating its suitability for the targeted frequency range.
Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.
Biodegradable polymers are an exceptional class of polymers that can be decomposed by bacteria. They have received significant interest from researchers in several fields. Besides this, biodegradable polymers can also be incorporated with fillers to fabricate biodegradable polymer composites. Recently, a variety of ionic liquids have also been applied in the fabrication of the polymer composites. In this brief review, two types of fillers that are utilized for the fabrication of biodegradable polymer composites, specifically organic fillers and inorganic fillers, are described. Three types of synthetic biodegradable polymers that are commonly used in biodegradable polymer composites, namely polylactic acid (PLA), polybutylene succinate (PBS), and polycaprolactone (PCL), are reviewed as well. Additionally, the influence of two types of ionic liquid, namely alkylimidazolium- and alkylphosphonium-based ionic liquids, on the mechanical, thermal, and chemical properties of the polymer composites, is also briefly reviewed. This review may be beneficial in providing insights into polymer composite investigators by enhancing the properties of biodegradable polymer composites via the employment of ionic liquids.
The utilization of waste polyethylene terephthalate (WPET) as aggregate substitutes in pavement has been extensively promoted because of its environmental advantages. However, previous studies have shown that a high percentage of WPET reduces the performance of the pavement. To increase the durability of pavement and mitigate the environmental issues caused by WPET, WPET is treated with gamma-irradiation as a component in asphalt mixtures. The study objectives were to investigate the feasibility of using WPET granules as a sustainable aggregate on asphalt mixture stiffness and rutting and predict the asphalt mixture performance containing irradiated WPET via an RSM-ANN-framework. To achieve the objectives, stiffness and rutting tests were conducted to evaluate the WPET modified mixtures' performance. The result indicated that samples containing 40% irradiated WPET provided a better performance compared to mixtures containing 20% non-irradiated WPET, increasing the stiffness by 27% and 21% at 25 °C and 40 °C, respectively, and rutting resistance by 11% at 45 °C. Furthermore, both predictive models developed demonstrated excellent reliability. The ANN exhibited superior performance than the RSM. The utilization of WPET as aggregate in asphalt mixtures represents a way to addressing related recycling issues while also improving performance. With gamma-irradiation treatment, the utilization of WPET can be increased with improved asphalt mixture performance.
Wood is a versatile material that is used for various purposes due to its good properties, such as its aesthetic properties, acoustic properties, mechanical properties, thermal properties, etc. Its poor dimensional stability and low natural durability are the main obstacles that limit its use in mechanical applications. Therefore, modification is needed to improve these properties. The hydrothermal modification of wood exposes wood samples to elevated temperatures and pressure levels by using steam, water, or a buffer solution as the treating medium, or by using superheated steam. Abundant studies regarding hydrothermally treated wood were carried out, but the negative effect on the wood's strength is one of the limitations. This is a method that boosts the dimensional stability and improves the decay resistance of wood with minimal decrements of the strength properties. As an ecofriendly and cost-effective method, the hydrothermal modification of wood is also a promising alternative to conventional chemical techniques for treating wood. Researchers are attracted to the hydrothermal modification process because of its unique qualities in treating wood. There are many scientific articles on the hydrothermal modification of wood, and many aspects of hydrothermal modification are summarized in review papers in this field. This paper reviews the hydrothermally modified mechanical properties of wood and their potential applications. Furthermore, this article reviews the effects of hydrothermal modification on the various properties of wood, such as the dimensional stability, chemical properties, and durability against termites and fungi. The merits and demerits of hydrothermal wood modification, the effectiveness of using different media in hydrothermal modification, and its comparison with other treating techniques are discussed.
Vocal fold injection is a preferred treatment in glottic insufficiency because it is relatively quick and cost-saving. However, researchers have yet to discover the ideal biomaterial with properties suitable for human vocal fold application. The current systematic review employing PRISMA guidelines summarizes and discusses the available evidence related to outcome measures used to characterize novel biomaterials in the development phase. The literature search of related articles published within January 2010 to March 2021 was conducted using Scopus, Web of Science (WoS), Google Scholar and PubMed databases. The search identified 6240 potentially relevant records, which were screened and appraised to include 15 relevant articles based on the inclusion and exclusion criteria. The current study highlights that the characterization methods were inconsistent throughout the different studies. While rheologic outcome measures (viscosity, elasticity and shear) were most widely utilized, there appear to be no target or reference values. Outcome measures such as cellular response and biodegradation should be prioritized as they could mitigate the clinical drawbacks of currently available biomaterials. The review suggests future studies to prioritize characterization of the viscoelasticity (to improve voice outcomes), inflammatory response (to reduce side effects) and biodegradation (to improve longevity) profiles of newly developed biomaterials.