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Invasive alien plant species (IAPS) may cause threats to native biodiversity in ecosystems. Researchers have been investigating all the possible ways that they can be used effectively for other purposes. Since IAPS are capable of forming cellulose fibre nets, in this research, papers were made from three different types of IAPS (Japanese knotweed, giant goldenrod, and black locust). This research examined these IAPS papers and their effectiveness when used as printing substrates. In comparison to commercial office paper, the differences in basic, surface, optical, and microscopic properties were measured. As a widely used technology, inkjet printing was applied. We tested a commonly used sans-serif typeface (which has been established as being more legible than other typefaces in previous research) in three different type sizes (i.e., 8, 10, and 12 pt). According to the results, paper made from IAPS could offer some usable properties and acceptable legibility, especially when printing typefaces with specific attributes, such as moderate counter size, higher x-height, and minimal differences in the letter stroke width, are used. An appropriate typographic tonal density should be achieved in combination with an adequate letter size, e.g., 10 pt type size when a sans-serif typeface is used.

This review explores circular economy principles in regard to ethanol production from paper-mill sludge. Environmental sustainability and renewability over fossil fuels make second generation ethanol an attractive energy source in a rapidly growing population and consumption world. Paper sludge (PS), a by-product of the pulp and paper (P&P) industry, can no longer be recycled for paper production and is mainly disposed of in landfills. Therefore, it poses a major environmental challenge. However, it has shown potential as a valuable raw material for ethanol production, along with other pulp and paper products, due to its abundant availability and high cellulosic content. This waste-to-energy (WtE) technology for ethanol production is proposed as an alternative, aligning with circular economy concepts to maximize resource efficiency and minimize waste. This review underlines the circular economy aspects of bioethanol production within paper mill sludge management systems. Circular economy principles applied to ethanol production from PS offer a promising avenue for sustainable biofuel development that not only addresses waste management challenges but also enhances the overall environmental performance of biofuel production. Furthermore, economic benefits are described, highlighting the potential for job creation and community development.

Expansive soil exhibits significant swellings and shrinkages, which may result in severe damage or the collapse of structures built upon it. Calcium-based admixtures, such as lime, are commonly used to improve this problematic soil. However, traditional chemical additions can increase significant environmental stress. This paper proposes a sustainable solution, namely, the use of lignin fiber (LF) from the paper industry to partially replace lime as an amendment for expansive soils. Both the macroscopic and microscopic characteristics of the lignin fiber-treated expansive soil are extensively studied. The results show that the mechanical properties of expansive soil are improved by using lignin fiber alone. Under the condition of an optimal dosage of 8%, the compressive strength of lignin fiber-modified soil can reach 193 kPa, the shear strength is increased by 40% compared with the untreated soil, and the water conductivity is also improved with the increase in dosage. In addition, compared with 2% lime-modified soil, the compressive strength of 8% lignin fiber- and 2% lime composite-treated expansive soil increased by 50%, the cohesion increased by 12%, and the water conductivity decreased significantly. The microstructure analysis shows that at an 8% lignin fiber content, lignin fibers interweave into a network in the soil, which effectively enhances the strength and stability of the improved soil. Simultaneously, the fibers can form bridges across the adjacent micropores, leading to the merging of pores and transforming fine, dispersed micropores into larger, connected macropores. Lime promotes the flocculation of soil particles, forming larger aggregates and thus resulting in larger pores. The addition of fibers exerts an inhibitory effect on the flocculation reaction in the composite-improved soil. In conclusion, lignin fibers are an effective addition used to partially replace calcium admixture for the treatment of expansive soil, which provides a sustainable and environmentally friendly treatment scheme for reducing industrial waste.

Nanoparticles have been in use for centuries [...]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research.

For linear viscoelastic materials, this paper proposes a finite element analysis method based on an integral constitutive relationship that can simultaneously consider the relaxation behavior of the elastic modulus and the creep Poisson’s ratio. Firstly, the generalized Maxwell model is employed to depict the relaxation characteristics of the elastic modulus, while the generalized Kelvin model is used to represent the creep Poisson’s ratio. Subsequently, the element relaxation stiffness matrix is established, thereby forming a convolutional finite element equation. Furthermore, the recursive calculation of the convolutional integral is derived, and the calculation steps of the finite element for viscoelasticity considering the time-dependent nature of both the elastic modulus and Poisson’s ratio are established. Finally, the accuracy of the proposed algorithm is verified through two numerical examples with linear viscoelastic material. The results indicate that the proposed variable stiffness method for the finite element analysis of linear viscoelastic materials can simultaneously consider the changes in the elastic modulus and Poisson’s ratio over time, thereby establishing a new path and idea for the more accurate simulation of viscoelastic materials’ mechanical properties. Compared with the initial strain method for linear viscoelastic materials, the variable stiffness method proposed in this paper effectively avoids the assumption of constant stress during the micro time interval, thus significantly enhancing the finite element calculation accuracy of linear viscoelastic materials. The proposed method establishes a simulation algorithm that matches existing commercial software with viscoelastic material experiments by considering the elastic modulus and Poisson’s ratio as material parameters.

As a transitional vehicle between fuel and electric vehicles, hybrid vehicles achieve energy savings and emission reductions without range anxiety. Regenerative braking has a direct impact on the fuel consumption of the whole vehicle; however, the current regenerative braking strategy for commercial vehicles is not yet perfect and has a poor adaptability in terms of working conditions and whole-vehicle load changes. Therefore, this paper proposes a regenerative braking strategy based on the identification of working conditions, by considering the influence of the vehicle load state and driving conditions on braking. Firstly, historical driving data of commercial vehicles were obtained from GPS data, driving conditions were classified using principal component analysis (PCA) and K-means, and a working condition recogniser was constructed using a back propagation neural network (BPNN) optimised with the Coati optimisation algorithm (COA). The recognition accuracy of the COA-BPNN was 7.6% better than that of the BPNN. Secondly, front and rear axle braking force distribution strategies are proposed, according to the braking intensity magnitude and load state under empty-, half-, and full-load conditions. Finally, a genetic algorithm (GA) was used to find the optimal control parameters for each category of working conditions, and the COA-BPNN condition recogniser identified the current category of working conditions needed to retrieve the corresponding optimal control parameters in the offline parameter library. The simulation results under C-WTVC and synthetic conditions show that the energy recovery rate of the proposed strategy in this paper reached up to 69.65%, which is at most 206.3% higher than that of the fixed-ratio strategy and at most 37.4% higher than that of the fuzzy control strategy.

Problems related to ventral hernia repairs (VHR) are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study is based upon data from 3339 patients from different European countries observed during the last 12 years (2012–2023), which were collected by specialists in hernia surgery. Most patients underwent standard surgical procedures, with a growing trend towards laparoscopic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair. Appropriate mathematical methods are employed to extract and classify the selected features, with emphasis on computational and machine-learning techniques. The paper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods. The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patients in the database. The accuracy of separating GHR mesh surgery with and without postoperative complications reached 74.4% using a two-layer neural network classification. Robotic surgeries represent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests both an interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting.

In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices.

This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables comparing various data, showing their similarities and homogeneity. Simultaneously, CPA aims at measuring the control loop quality, supporting decision-making about their tuning and maintenance. This paper shows that control system quality can be efficiently visualized using LMRDs. The method was analyzed using simulations and further validated at a real chemical engineering industrial ammonia synthesis plant.