Critical Problem Research Articles

Waqf-based Waste Management: A Proposed Model in Indonesia

Indonesia is now facing critical environmental problems, in addition to cases of growing waste, deforestation of scale such as air pollution, and lack of air supply and air services, raining down on Indonesia for some serious irregularities and deficiencies in environmental management. These serious issues must be addressed, and appropriate solutions must be implemented to reduce disaster risks and promote the growth of a better and more sustainable country. Since Islam is the main religion for the Indonesian people, an Islamic approach will be encouraged to solve this problem. Previous researchers requested that waqf is the most important Islamic legal entity that can be used for this purpose. Sustainable and integrated waste management or ISWM/Integrated Sustainable Waste Management focuses on waste management as a multi-actor. Waqf can contribute to long-term management that is used to manage and manage waste. Waqf institutions and others have pioneered some of these innovative schemes, but they have been massively implemented. So, this research will examine the concept of Waqf-Based Waste Management.

The Lived Experiences of Saudi Nursing Students in Digital Clinical Experience: A Phenomenological Study.

Background Novel digital methods of simulation are gaining popularity in nursing education in light of the limited access to clinical placement and expensive high-fidelity simulation technologies. Aim The aim of this study is to explore the lived experiences of Saudi nursing students in digital clinical experiences (DCEs). Methods A qualitative phenomenological research design, grounded in Husserlian phenomenology, was employed. Purposive sampling was utilized to select 21 participants actively involved in DCEs. In-depth interviews were conducted to collect rich, narrative data. Results The thematic analysis has yielded four themes, namely, "comfort and safety", "critical thinking and problem solving", "appraisal of knowledge", and "transition to practice." Conclusions The findings contribute to ongoing discussions about leveraging technology in nursing education, emphasizing the need for educators and policymakers to integrate digital tools that enhance the learning experiences of nursing students.

GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

AbstractCode clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.

ENDEAVORING THE ROLE OF OBESITY IN EXTRACELLULAR MATRIX DEGRADATION LEADING TO METASTASIS

One of the significant causes of death globally is cancer ( http://www.who.org/ ). Another critical problem is obesity, which is associated with an increased cancer threat. This work provides insight into how obesity contributes to cancer progression and metastasis. We developed a diffusive obesity-cancer model consisting of cancer cells, normal cells, fat cells, macrophages, and an extracellular matrix (ECM) for this aim. We have directed the formed model’s global existence and non-negativity. Equilibrium points for the related ODE are calculated, and its existence and stability study is also done. We present a traveling wave analysis of the obesity-cancer model and have calculated the minimum wave speed. Using a combination of analytic and numerical results of traveling waves, we conjecture that the minimal wave speed depends on fat cells’ diffusive rate and haptotaxis coefficient. We followed the theory of the Partial Rank Correlation Coefficient (PRCC) to carry out a global sensitivity analysis to evaluate the most sensitive parameters reliable for cancer progression. We delivered a comprehensive numerical analysis of our deterministic and diffusive models (in 1D and 2D) and analogized the result. Numerical simulation of corresponding spatially explicit systems conveys complex spatio-temporal dynamics, resulting in the appearance of patterns. It also discloses that cancer spread increases with increased haptotaxis coefficient and growth rate of obese cells. Our simulation confirms that the degradation of the ECM increases cancer spread and density.

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

A study of a critical hypoelliptic problem in a stratified Lie group

In this paper, the existence of at least one solution to the problem (P) { − L M u = λf ( u ) e α | u | M ′ + μ | u | p − 2 u in Ω u = 0 on ∂Ω is established, where L M u := div G ⋅ ( | ∇ G u | M − 2 ∇ G u ) is the M-sub-Laplacian, 2 ≤ p < M , }0 $ ]]> α > 0 , M ′ = M M − 1 , Ω is a bounded subset of the N-dimensional stratified Lie group G with N ≥ 3 (N = 1, 2 has been avoided since they do not produce a purely stratified Lie group) and M is the homogeneous dimension of G . We will also show that in fact, the problem admits infinitely many solutions with some restrictions on the parameters involved.

Research Center and Olympics as a strategy for science education in the centers Educates More of Maranhão

This article addresses the implementation of the Research and Olympics Center (NPO) in the Educa Mais Centers in the State of Maranhão, part of the Full-Time Public Education Networks. The main objective was to analyze the results obtained by students and teachers after the introduction of the Center, focusing on participation and awards in knowledge olympiads and scientific events. The data collected revealed a significant increase in the engagement of Educa Mais Centers in challenging academic activities after the implementation of the NPO. The significant participation of students and teachers in Olympiads and scientific events indicated a clear interest in deepening knowledge and expanding educational horizons. Also noteworthy is the considerable increase in the number of awards, indicating not only recognition of merit, but also encouraging participation in other activities, with different methodologies. This positive scenario suggests that the Research and Olympics Center not only introduces an innovative approach to teaching, but also promotes a school culture that values academic excellence. The focus on research and participation in Olympiads not only challenges students intellectually, but also develops fundamental skills and competencies such as critical thinking, problem solving, networking and teamwork. When considering the longevity and effectiveness of this implementation, constant monitoring of results is essential, identifying areas for improvement and necessary adjustments. The dissemination of these positive results not only celebrates the success of the initiative, but also serves as an inspiring model for other educational institutions seeking to improve educational standards and encourage student participation in enriching activities beyond the conventional curriculum. This study highlights the importance of innovative pedagogical strategies to promote the academic and scientific development of students, contributing to the formation of a more capable generation to face future challenges.

A novel extreme adaptive GRU for multivariate time series forecasting

Multivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly enhanced the ability to tackle such problems. However, a primary challenge in time series forecasting comes from the imbalanced time series data that include extreme events. Despite being a small fraction of the data instances, extreme events can have a negative impact on forecasting as they deviate from the majority. However, many recent time series forecasting methods neglect this issue, leading to suboptimal performance. To address these challenges, we introduce a novel model, the Extreme Event Adaptive Gated Recurrent Unit (eGRU), tailored explicitly for forecasting tasks. The eGRU is designed to effectively learn both normal and extreme event patterns within time series data. Furthermore, we introduce a time series data segmentation technique that divides the input sequence into segments, each comprising multiple time steps. This segmentation empowers the eGRU to capture data patterns at different time step resolutions while simultaneously reducing the overall input length. We conducted comprehensive experiments on four real-world benchmark datasets to evaluate the eGRU’s performance. Our results showcase its superiority over vanilla RNNs, LSTMs, GRUs, and other state-of-the-art RNN variants in multivariate time series forecasting. Additionally, we conducted ablation studies to demonstrate the consistently superior performance of eGRU in generating accurate forecasts while incorporating a diverse range of labeling results.

Disentangling Photodoping, Photoconductivity, and Photosuperconductivity in the Cuprates.

The normal-state conductivity and superconducting critical temperature of oxygen-deficient YBa_{2}Cu_{3}O_{7-δ} can be persistently enhanced by illumination. Strongly debated for years, the origin of those effects-termed persistent photoconductivity and photosuperconductivity (PPS)-has remained an unsolved critical problem, whose comprehension may provide key insights to harness the origin of high-temperature superconductivity itself. Here, we make essential steps toward understanding PPS. While the models proposed so far assume that it is caused by a carrier-density increase (photodoping) observed concomitantly, our experiments contradict such conventional belief: we demonstrate that it is instead linked to a photo-induced decrease of the electronic scattering rate. Furthermore, we find that the latter effect and photodoping are completely disconnected and originate from different microscopic mechanisms, since they present different wavelength and oxygen-content dependences as well as strikingly different relaxation dynamics. Besides helping disentangle photodoping, persistent photoconductivity, and PPS, our results provide new evidence for the intimate relation between critical temperature and scattering rate, a key ingredient in modern theories on high-temperature superconductivity.

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

AbstractThe integration of machine learning (ML) with algae‐derived biopolymers in 3D printing is a burgeoning area with the potential to revolutionize various industries. This review article delves into the challenges and advancements in this field, starting with the critical problem it addresses the need for sustainable and efficient additive manufacturing processes. Algae‐based biopolymers, such as alginate and carrageenan, are explored for their viability in 3D printing, highlighting their environmental benefits and technical challenges. The role of ML in enhancing material selection, predictive modeling, and quality control is examined, showcasing how this synergy leads to significant improvements in 3D printing processes. Key findings include the enhanced mechanical properties of algae‐based biopolymers and the optimization of printing parameters through ML algorithms. Examples like the use of Spirulina in creating a range of materials and the application of carrageenan in bone tissue engineering are discussed. The conclusion underscores the transformative impact of combining ML with algae‐based biopolymers in 3D printing, paving the way for innovative, sustainable solutions in additive manufacturing. Despite existing challenges, this integration holds promise for a future of advanced, eco‐friendly manufacturing techniques.

Turbulence spreading effects on the ELM size and SOL width

BOUT++ turbulence simulations were performed to investigate the impact of turbulence spreading on the edge localized mode (ELM) size and divertor heat flux width $({\lambda _q})$ broadening in small ELM regimes. This study is motivated by EAST experiments. BOUT++ linear simulations of a pedestal radial electric field (Er) scan show that the dominant toroidal number mode (n) shifts from high-n to low-n, with a narrow mode spectrum, and the maximum linear growth rate increases as the pedestal Er well deepens. The nonlinear simulations show that as the net E × B pedestal flow increases, the pressure fluctuation level and its inward penetration beyond the top of the pedestal both increase. This leads to a transition from small ELMs to large ELMs. Both inward and outward turbulence spreading are sensitive to the scrape-off-layer (SOL) plasma profiles. The inward turbulence spreading increases for the steep SOL profiles, leading to increasing pedestal energy loss in the small ELM regime. The SOL width $({\lambda _q})$ is significantly broadened progressing from the ELM-free to small ELM regime, due to the onset of strong radial turbulent transport. The extent of the SOL width $({\lambda _q})$ broadening depends strongly on outward turbulence spreading. The fluctuation energy intensity flux ${\varGamma _\varepsilon }$ at the separatrix can be enhanced by increasing either pedestal Er flow shear or local SOL pressure gradient. The ${\lambda _q}$ is broadened as the fluctuation energy intensity flux ${\varGamma _\varepsilon }$ at the last close flux surface (LCFS) increases. Local SOL E × B flow shear will restrain outward turbulence spreading and the associated heat flux width broadening. Operating in H-mode with small ELMs has the potential to solve two critical problems: reducing the ELM size and broadening the SOL width.

Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference From High-Dimensional Biosignals

Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this paper proposes two PLS-based algorithms, Pseudo-Sparse PLS (PS-PLS) and iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3D trajectory from Electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the Electromyography (EMG) powers from recorded Magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the High-Density surface Electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches.

Analysis of a Large 19-Year Database in Vascular Surgery in Southern Iran: Evaluation of Trends and Limitations

IntroductionLarge databases with data elements of clinical interest are essential for carrying out high-quality observational studies. Such databases have become increasingly popular for clinical research in fields like Vascular Surgery. Our goal is to create a solid and reliable database of the patients who have been admitted and undergone different vascular-surgery procedures over 19 years and to provide surgeons with the current trends and limitations in managing patients with vascular disease. MethodThe database of patients operated in Namazi Hospital, the referral center for Vascular Surgery in southern Iran, from 2001 to 2019, was retrieved and patients undergoing vascular procedures were parted. Demographic and perioperative data were evaluated and patients were categorized into subgroups based on the type and cause of operation. All data were analyzed with SPSS version 26.0 (IBM. NY. USA). ResultsDuring the period of our study, a total of 226,051 operations were performed at the Namazi Hospital. Among these operations, 6,386 (2.82%) Vascular Surgery-related operations were entered into our study. The average age of the patients in our study was 53.22 ± 18.92 years (range: 1 day old – 97 years) and 4,061 (63.6%) were male. Furthermore, 147 (2.3%) were operated by multiple surgeons. Moreover, 798 (12.5%) of the patients were admitted postoperatively to the intensive care unit (ICU), while the rest (5,588; 87.5%) in the common surgery ward. The cause of operation in 609 (9.5%) of the cases was trauma. Based on wound categorization, 5,132 (80.4%) were type I (clean). The most frequent operation performed in our center was arterial reconstruction and limb revascularization (31.4%), followed by hemodialysis access (31.3%). The most frequent surgery in the age group of under 18 years was fasciotomy, in the 19 to 40 years group was tumor (56.8%) and varicose veins (52.9%), in the 41 to 60 years group was implantation of ventral venous port catheter (47%). Only carotid and thoracic outlet syndrome surgeries were significantly higher in females. The remaining operations were all significantly higher among male patients. Finally, carotid body tumor surgery was the most frequent operation requiring ICU monitoring. ConclusionWe demonstrated, for the first time, an overview of Vascular Surgeries performed in a referral tertiary center in Southwest Iran. There is an increase in the number of surgical procedures in the field of Vascular Surgery, and large databases will be a valuable tool for addressing critical problems in this field and also the healthcare system.

Assessment of the Risk of the Loss of Supply, the Recycling Rate and the Degree of Substitutability of Elements in the NdFeB Magnets of a Small Wind Farm Generator

Existing shortages on the market of critical and strategic raw materials and problems with the supply chain of selected raw materials, mainly those from China, indicate the need to develop recycling technologies for devices containing the above-mentioned materials. The aim of this work should be to ensure a sustainable supply of raw materials for industry in the EU. One potential source of raw materials may be small-power wind turbine generators equipped with permanent magnets (NdFeB). This research allowed for the development of a methodology for disassembling a generator containing NdFeB magnets in order to determine the shares of individual elements. Due to the significant shares of critical and strategic elements in permanent magnets, a detailed economic analysis was carried out. Based on the research results, economic benefits resulting from the recovery of valuable elements were indicated. The significant market value of critical and strategic raw materials contained in neodymium magnets was demonstrated. The risk of the loss of supplies, the recycling rate and the possibility of replacing the elements in permanent magnets were assessed.

Employment of the population as a factor of ensuring social security

The purpose of the article is to determine the role of employment in ensuring social security. The essence of the "social security" concept has been clarified, and its content has been defined. It was concluded that security is a particular state of the system, determined by its ability to resist dangers, which involves not only the preservation of the existing situation but also the creation of opportunities for development under conditions of uncertainty and risk. It has been proven that the social security of society and the state begins with the social security of the individual - the guaranteed protection of a person from internal and external threats that may violate his vital interests, rights, and freedoms. This state includes ensuring normal life activities, improving material well-being, and creating conditions for development and self-realization. The role of employment in ensuring the individual's social security is considered the formation of income for the realization of material, social, and status needs and the need for self-development. The impact of employment on economic development, social stability, and the well-being of society is analyzed. It has been proven that solving employment problems is necessary for ensuring society's social security through the successful construction of a socially oriented market economy. The critical problems of employment and social security in Ukraine during the war are identified. Among them, the destruction and damage to the infrastructure of individual enterprises, the occupation of territories, general economic instability, which leads to a reduction in production and the deterioration of the financial condition of enterprises, a high level of unemployment with an insufficient number of highly qualified specialists, significant resettlement of the population, including refugees and internally displaced persons, which leads to disparities in labor markets in different regions. It has been proven that the issues of employment and socio-economic recovery in the conditions of a military conflict are highly complex and require a complex approach from the authorities, the public, and international organizations. Particular attention should be paid to developing support programs, training, and retraining to provide the population with opportunities to restore stability and everyday functioning in the labor market, which will positively affect social security. Keywords: social security, security factors, working population, employment, unemployment.

Nonlocal critical growth elliptic problems with jumping nonlinearities

In this paper we study a nonlocal critical growth elliptic problem driven by the fractional Laplacian in the presence of a jumping nonlinearity. By using variational and topological methods and applying some new linking theorems recently proved by Perera and Sportelli in [19], we prove the existence of a nontrivial solution for the problem under consideration.The results we obtain here are the nonlocal counterparts of the ones obtained in [19] in the context of a local equation. Due to the nonlocal nature of our problem, some additional difficulties arise, and the arguments employed in the local setting need to be improved or reconceived. In fact, the proofs of our main theorems require some refined techniques and new regularity results for weak solutions of nonlocal problems that are of independent interest.We would like to point out that our results are specifically for a nonlocal problem with the fractional operator in integral form. However, we do not exclude the possibility that our results may have a counterpart for the spectral operator studied in [27]. Since nonlocal operators in integral form are being widely investigated in the current literature, especially in connection with geometric problems, we have restricted ourselves to elliptic equations driven by a fractional operator in integral form here.

Mathematics Learning Model Based on Computational Thinking: Preparing elementary School Students to be Disciplined, Independent, and Dignified

Objective: The research aims to evaluate the feasibility of a computational thinking-based mathematics learning model. Explore strategies for preparing disciplined, independent, and dignified students by applying the developed model in elementary schools.&#x0D; &#x0D; Theoritical Framework: Computational Thinking (CT) is becoming a fundamental part of the way people think and understand the world. Mathematics offers fruitful opportunities for CT integration. Integrating mathematics with CT, allows students to acquire essential critical thinking and problem olving skills.&#x0D; &#x0D; Method: The type of research in this article is qualitative evaluative. The research subjects were the principal, math teachers, and students of Klaten Elementary School in Central Java. Data collection methods were observation, interview, and documentation. Data validity with source and method triangulation. Data analysis techniques are flow model and constant comparison.&#x0D; &#x0D; Results and Conclusions: The results of the research, the CT-based mathematics learning model is feasible based on the assessment of material, design, and language experts, and can improve learning activities and outcomes. Preparing students to be disciplined, independent, and dignified, based on strong religion, core character, lifestyle character, character respect for others. This means that CT-based mathematics learning is feasible to implement in elementary schools and can prepare students to be disciplined, independent, and dignified.&#x0D; &#x0D; Significance: The research findings have a significant impact on primary schools. The impact is to improve the quality of character, learning process, thinking, and learning achievement. This means that the research results have a broad role in the development of primary school students.

A unified bond–based peridynamic model without limitation of Poisson's ratio

The issue of fixed Poisson's ratio is a critical problem plaguing bond–based peridynamic (BB–PD) models. The popular approach of introducing the bond's tangential stiffness cannot completely remove Poisson's ratio limitation, but instead leads to some additional troubles, such as negative tangential stiffness and bond force density misalignment in finite rigid body rotation problems. To address the issue of fixed Poisson's ratio thoroughly, a unified bond–based peridynamics (UPD) was originally proposed. In the proposed model, a novelty pairwise force density function has been developed, in which dilatation and distortion are distinguished, and the model assigns different stiffnesses to the two deformation components. Arbitrary and reasonable values of Poisson's ratio can be achieved by adjusting the PD parameters that control the bulk and shear moduli of the material. The theoretical range of Poisson's ratio in the proposed model is (-1, 0.5], which is consistent with those in continuum mechanics. In addition, the absence of tangential stiffness prevents the proposed model from misalignment of bond force density due to rigid body rotation. Several numerical examples, including four two–dimensional and three–dimensional deformation analyses and three quasi–static fracture analyses, were implemented to verify the accuracy and reliability of the proposed model. The results show that the proposed model can accurately simulate the elastic properties of the material and obtain the deformation with high accuracy. The numerical examples also demonstrate the fracture prediction capability of the proposed model.

Metagenomic insights into the mechanism for the rapid enrichment and high stability of Candidatus Brocadia facilitated by Fe(Ⅲ)

The rapid enrichment of anammox bacteria and its fragile resistance to adverse environment are the critical problems facing of anammox processes. As an abundant component in anammox bacteria, iron has been proved to promote the activity and growth of anammox bacteria in the mature anammox systems, but the functional and metabolic profiles in Fe(III) enhanced emerging anammox systems have not been evaluated. Results indicated that the relative abundance of functional genes involved in oxidative phosphorylation, nitrogen metabolism, cofactors synthesis, and extracellular polymers synthesis pathways was significantly promoted in the system added with 5 mg/L Fe(III) (R5). These enhanced pathways were crucial to energy generation, nitrogen removal, cell activity and proliferation, and microbial self-defense, thereby accelerating the enrichment of anammox bacteria Ca. Brocadia and facilitating their resistance to adverse environments. Microbial community analysis showed that the proportion of Ca. Brocadia in R5 also increased to 64.42 %. Hence, R5 could adapt rapidly to the increased nitrogen loading rate and increase the nitrogen removal rate by 108 % compared to the system without Fe(III) addition. However, the addition of 10 and 20 mg/L Fe(III) showed inhibitory effects on the growth and activity of anammox bacteria, which exhibited the lower relative abundance of Ca. Brocadia and unstable or even collapsed nitrogen removal performance. This study not only clarified the concentration range of Fe(III) that promoted and inhibited the enrichment of anammox bacteria, but also deepened our understanding of the functional and metabolic mechanisms underlying enhanced enrichment of anammox bacteria by Fe(III), providing a potential strategy to hasten the start-up of anammox from conventional activated sludge.

Advances in the Sustainable Development of Biobased Materials Using Plant and Animal Waste as Raw Materials: A Review

There is substantial concern about critical environmental problems related to waste in production sectors such as textile, construction, and packaging. The materials ascribed to the sector’s unsustainability are primarily fabrics, plastic, and hazardous solvents, making developing new biobased materials imperative. As such, various strategies have been investigated to convert and recycle waste and give them commercial value via the manufacture of biobased materials. This review discusses the various types of raw materials as sources to develop new biobased materials that could promote the transition toward sustainability. According to the literature, the functional qualities of biobased materials are comparable to those of synthetic materials. Raw material sources such as biomass, derived from plant and animal-based waste, are attractive due to their low cost, abundance, and biodegradability. The manufacture of biomaterials, as well as their characterization and performance, are also discussed. Further, this review will offer a comprehensive view of the potential applicability and current commercial applications of the developed biobased materials in relevant areas such as packaging, construction, textile, and wastewater remediation. This could be a potential field of research to address the environmental challenges posed by the continuous growth of the global population.