The expansion of food production is becoming more important due to a rising world population, which is relying on food security on regional and local scales. Intensive food production systems exert a negative impact on the regional ecosystem because of agrochemical pollution and nutrient-rich water discharging into nearby rivers. Furthermore, these systems highly depend on regional water resources, causing water scarcity and soil erosion due to the overexploitation of natural resources in general. The objective of this article is to review the water usage in the two most water-intensive food production systems, agriculture and aquaculture, showing lacking areas like system management and climate change, which must be considered in the implementation of a sustainable water footprint. In addition, the review includes an analysis of the combination of both production systems in aquaponic food production and the possibilities of water saving. There are a variety of analyses related to water usage for crop and aquatic animal production, but in these analyses, there is a lack of information about system management in general, which includes cleaning processes, water substitution, pond removal, water evaporation, and, especially in aquaculture, the water usage required for industrially elaborated fish feed. Full article

A doppler radar holds promising prospects in the field of indoor target localization. However, traditional Doppler radar systems suffer from high power consumption, large size, and noticeable radio frequency interference issues when transmitting carriers of different frequencies. Therefore, an ISABO-based (improved subtraction-average-based optimizer) target localization algorithm for a single-frequency Doppler radar is proposed in this paper. Firstly, a mathematical model for target localization is established according to the spatial geometric relationships during the target movement and the Doppler frequency-shift information in the single-frequency echo signal. Then, the optimization function is constructed with the target motion error as the optimization goal. Finally, an improved subtraction-average-based optimizer algorithm is proposed to solve the optimal parameters and realize the target positioning. The experimental results show that the proposed method can achieve centimeter-level localization accuracy with a cost-effective system structure. Full article

This study aims to determine the effectiveness of administering 80 ppm nitric oxide in reducing kidney injury, mitochondrial dysfunction and regulated cell death in kidneys during experimental perfusion. Twenty-four sheep were randomized into four groups: two groups received 80 ppm NO conditioning with 90 min of cardiopulmonary bypass (CPB + NO) or 90 min of CPB and hypothermic circulatory arrest (CPB + CA + NO), while two groups received sham protocols (CPB and CPB + CA). Kidney injury was assessed using laboratory (neutrophil gelatinase-associated lipocalin, an acute kidney injury biomarker) and morphological methods (morphometric histological changes in kidney biopsy specimens). A kidney biopsy was performed 60 min after weaning from mechanical perfusion. NO did not increase the concentrations of inhaled NO₂ and methemoglobin significantly. The NO-conditioning groups showed less severe kidney injury and mitochondrial dysfunction, with statistical significance in the CPB + NO group and reduced tumor necrosis factor-α expression as a trigger of apoptosis and necroptosis in renal tissue in the CPB + CA + NO group compared to the CPB + CA group. The severity of mitochondrial dysfunction in renal tissue was insignificantly lower in the NO-conditioning groups. We conclude that NO administration is safe and effective at reducing kidney injury, mitochondrial dysfunction and regulated cell death in kidneys during experimental CPB. Full article

Ecosystems are undergoing continuous degradation due to the dual perturbation of global climate change and human activities, posing unprecedented threats and challenges to the ecosystem services they provide. To gain a deeper understanding of the spatio-temporal evolution of ecosystem service value (ESV), it is essential to accurately capture the characteristics of its spatial and temporal changes and its influencing factors. However, traditional spatio-temporal statistical methods are limited to analyzing the heterogeneity of ESV in a single temporal or spatial dimension, which fails to meet the comprehensive analysis needs for spatio-temporal heterogeneity over an extended continuum. Therefore, this paper constructs a Bayesian spatio-temporal hierarchical model to analyze the ESV heterogeneity in both temporal and spatial dimensions in Northeast China from 2000 to 2020 to accurately identify the regions with unstable fluctuations in ESV and analyze the influencing factors behind them. It aims to comprehensively and systematically reveal the intrinsic laws of spatio-temporal evolution of ESV, and provide a scientific basis for relevant decision-making. The study found a continuous fluctuating downward trend of ESV in Northeast China from 2000 to 2020, with significant spatial and temporal heterogeneity. Notably, the distribution of hot and cold spots is regularly concentrated, especially in the transition zone from low hills to plains, which forms an “unstable zone” of spatial and temporal fluctuations of ESV. Natural factors such as NDVI and NPP exhibit a significant positive correlation with ESV, while social factors like population density and GDP show a strong negative correlation. Compared to traditional statistical methods, the Bayesian spatio-temporal hierarchical model, with its outstanding flexibility and accuracy, provides a new perspective and way of thinking for analyzing classical spatio-temporal problems. Firstly, the model examines time and space as a whole and fully accounts for the influence of spatio-temporal interactions on ESV. Secondly, the Bayesian spatio-temporal hierarchical model meets the needs of long-term continuous ESV outcome detection, which provides us with solid support for a deeper understanding of the evolution of ESV. Full article

Breast tumor segmentation of ultrasound images provides valuable tumor information for early detection and diagnosis. However, speckle noise and blurred boundaries in breast ultrasound images present challenges for tumor segmentation, especially for malignant tumors with irregular shapes. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Nevertheless, they are often dominated by features of large patterns and lack the ability to recognize negative information in ultrasound images, which leads to the loss of breast tumor details (e.g., boundaries and small objects). In this paper, we propose a novel noise suppression and boundary refinement network, NSBR-Net, to simultaneously alleviate speckle noise interference and blurred boundary problems of breast tumor segmentation. Specifically, we propose two innovative designs, namely, the Noise Suppression Module (NSM) and the Boundary Refinement Module (BRM). The NSM filters noise information from the coarse-grained feature maps, while the BRM progressively refines the boundaries of significant lesion objects. Our method demonstrates superior accuracy over state-of-the-art deep learning models, achieving significant improvements of 3.67% on Dataset B and 2.30% on the BUSI dataset in mDice for testing malignant tumors. Full article

Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system. Full article

The dairy sector faces increasing pressure to adopt sustainable practices. Various tools have been developed to evaluate sustainability of the dairy supply chain. This paper provides an overview of these tools, highlighting their strengths and limitations regarding sustainability dimensions, indicators, and system boundaries. A systematic literature search identified 27 tools that were then categorized into a typology based on dimensions of sustainability, geographical applicability, and accessibility. In-depth analysis was conducted on six tools: Sustainability Assessment of Food and Agriculture (SAFA), Sustainability Monitoring and Assessment RouTine (SMART), Response-Inducing Sustainability Evaluation (RISE) 3.0 version, Swiss Agricultural Life Cycle Assessment (SALCAsustain), MOnitoring Tool for Integrated Farm Sustainability (MOTIFS), and Technology Impact and Policy Impact CALculations (TIPICAL). Assessment focused on the relevance of covered sustainability dimensions to the dairy sector, level of supply chain coverage, type of indicators, accessibility, and practicability. The review identified tools which integrate multiple sustainability aspects in a comprehensive way (SAFA and SMART) and tools offering accurate quantification of the impact on sustainability dimensions of the production system (SALCAsustain, RISE, MOTIFS, TIPICAL). Only two tools extend assessment past the farm gate (SAFA, SMART). Future users should select tools based on the specific objectives of measuring sustainability in dairy systems. This review contributes to the literature by addressing various aspects of sustainability assessment tools, by addressing the need for an integrated and comprehensive view, and by considering the entire dairy supply chain. Full article

A mRNA vaccine is a type of vaccine that induces an immune response. Antigen-encoding mRNA is delivered via vaccine carriers into the immune cells, which are produced because of antigen-encoding mRNA translation, a protein. For example, COVID-19 mRNA vaccines produce the spike protein of the COVID-19 virus, whereas for influenza virus, mRNA vaccines target the haemagglutinin protein to treat the flu, and it requires modifications depending on the pandemic or seasonal viruses as it is capable of adapting the immune response, which makes the development of vaccines arduous. The protein molecule promotes an adaptive immune response that eliminates and terminates the corresponding virus or pathogen. There are many challenges to delivering an mRNA vaccine into the body; hence, the encapsulation of the mRNA (usually within lipid nanoparticles) is necessary to protect the mRNA from the body’s surrounding environment. In this review article, we focus mainly on the production, formulation, and stabilization of mRNA vaccines in general, elaborating more on and focusing more on SARS-CoV-2, or COVID-19, and influenza viruses, which have become a major concern as these viruses have turned into life-threatening diseases. Full article

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa. Full article

Chinese cities are pursuing an energy transition to decouple energy-related carbon emissions (ERCEs) from economic growth. Despite numerous studies focusing on the factors influencing carbon emissions, few have quantitatively analyzed their respective contribution rates, thus leaving a gap in effectively guiding policies. This study took 16 cities in the Yangtze River Delta (YRD) as the study area. The decoupling between ERCEs and economic growth was analyzed during 2000–2020, and the contribution rates of different factors were explored. The results showed that the total ERCEs increased from 413.40 million to 1265.86 million tons during 2000–2020, increasing by over three times. Coal and oil were the dominant energy sources in most cities, but natural gas consumption increased from 0.15% to 5.96%. Moreover, 14 cities showed a decoupling status, indicating a certain win–win situation between economic growth and ERCE reduction. Economic growth greatly increased ERCEs, with its contribution rate ranging from 114.65% to 493.27% during 2000–2020. On the contrary, energy structure and energy intensity both contributed to reducing ERCEs in most cities, and their maximum contribution rates reached −32.29% and −449.13%, respectively, which were the main forces for the win–win situation. Finally, carbon reduction proposals are put forward, which provide theoretical support for achieving the “Double Carbon” goal in the YRD. Full article

Geary’s c is a prominent measure of spatial autocorrelation in univariate spatial data. It uses a weighted sum of squared differences. This paper develops Geary’s c for multivariate spatial data. It can describe the similarity/discrepancy between vectors of observations at different vertices/spatial units by a weighted sum of the squared Euclidean norm of the vector differences. It is thus a natural extension of the univariate Geary’s c. This paper also develops a local version of it. We then establish their properties. Full article

Objective: Ethical climates and their influence on emotional health have been the subject of intense debates. However, Personal Ethics as a potential resource that can mitigate Burnout syndrome has gone unnoticed. Therefore, the main objective of this study is to examine the effect of Personal Ethics on the three dimensions that constitute Burnout, considering the moderating influence of Affective Commitment. Design/methodology: A model consisting of three simple moderations is used to solve this question. The sample includes 448 professionals from the Colombian electricity sector with university-qualified education. Findings: Personal Ethics mitigates Emotional Exhaustion and Depersonalization, but it is not related to Personal Realization. Affective Commitment, unexpectedly, has an inverse moderating effect. In other words, as this type of commitment intensifies, the positive impact of Personal Ethics on Burnout and Depersonalization decreases until it disappears. Furthermore, Affective Commitment does not influence the dynamic between Personal Ethics and self-realization. Research limitations/implications: A longitudinal study would strengthen the causal relationships established in this research. Practical implications: Alignment of values between the individual and the organization is crucial. In fact, integration between the organization and its personnel through organic, open and connected structures increases psychological well-being through values linked to benevolence and understanding. Social implications: Employees’ emotional health is transcendental beyond the organizational level, as it has a significant impact on personal and family interactions beyond the workplace. Originality/value: The potential adverse repercussion of Affective Commitment has been barely examined. Additionally, Personal Ethics, when intensified by high Affective Commitment, can lead to extra-role behaviors that transform what is voluntary into a moral imperative. This situation could generate emotional fractures and a decrease in achievement. This perspective, compared to previous research, introduces an innovative element. Full article

With the continuously growing requirement for encryption in network environments, web browsers are increasingly employing hypertext transfer protocol security. Despite the increase in encrypted malicious network traffic, the encryption itself limits the data accessible for analyzing such behavior. To mitigate this, several studies have examined encrypted network traffic by analyzing metadata and payload bytes. Recent studies have furthered this approach, utilizing graph neural networks to analyze the structural data patterns within malicious encrypted traffic. This study proposed an enhanced encrypted traffic analysis leveraging graph neural networks which can model the symmetric or asymmetric spatial relations between nodes in the traffic network and optimized feature dimensionality reduction. It classified malicious network traffic by leveraging key features, including the IP address, port, CipherSuite, MessageLen, and JA3 features within the transport-layer-security session data, and then analyzed the correlation between normal and malicious network traffic data. The proposed approach outperformed previous models in terms of efficiency, using fewer features while maintaining a high accuracy rate of 99.5%. This demonstrates its research value as it can classify malicious network traffic with a high accuracy based on fewer features. Full article

In the preceding decades, rapid technological advancements and increasing democratisation of historical records have been coupled with scientific data from DNA testing, which has revolutionised the family history industry. Going beyond the traditional archives and databases, DNA profiles present nuanced confirmations, puzzles, and contradictions generated through this biological lens. Family history researchers seek iterative engagements with their familial pasts and, in the process, amplify their contemporary identities. This specialised group of historians illuminates their families’ travels through the broader historical landscape, constructing micro-narratives using a broad range of investigative modalities. This article reports on the findings of a large international study (n = 1016) that investigated family history researchers’ motivations for undertaking DNA testing, their experiences, and its impact on their perceptions of individual, national, and global identities using Berzonsky’s socio-cultural model of identity construction (2003, 2011) as an analytic frame. Using a survey methodology, it was concluded that DNA testing can expand and disrupt long-held notions of identity and has the power to shift perceptions and understandings of the self while simultaneously providing a new era of opportunity to reconceptualise national and international affiliations. It suggests further investigative avenues to assess the potential of DNA testing, which may promote social cohesion, inclusiveness, and global citizenship. Full article

Background: Brown seaweed is promising for the treatment of type 2 diabetes mellitus (T2DM). Its bioactive constituents can positively affect plasma glucose homeostasis in healthy humans. We investigated the effect of the brown seaweeds Sargassum (S.) fusiforme and Fucus (F.) vesiculosus in their natural form on glucose regulation in patients with T2DM. Methods: We conducted a randomized, double-blind, placebo-controlled pilot trial. Thirty-six participants with T2DM received, on a daily basis, either 5 g of dried S. fusiforme, 5 g of dried F. vesiculosus, or 0.5 g of dried Porphyra (control) for 5 weeks, alongside regular treatment. The primary outcome was the between-group difference in the change in weekly average blood glucose levels (continuous glucose monitoring). The secondary outcomes were the changes in anthropometrics, plasma lipid levels, and dietary intake. The data were analyzed using a linear mixed-effects model. Results: The change in weekly average glucose levels was 8.2 ± 2.1 to 9.0 ± 0.7 mmol/L (p = 0.2) in the S. fusiforme group (n = 12) and 10.1 ± 3.3 to 9.2 ± 0.7 mmol/L (p = 0.9) in the F. vesiculosus group (n = 10). The between-group difference was non-significant. Similarly, no between-group differences were observed for the changes in the secondary outcomes. Discussion: A daily intake of 5 g of fresh, dried S. fusiforme or F. vesiculosus alongside regular treatment had no differential effect on weekly average blood glucose levels in T2DM. Full article

When utilizing high-dimensional chaotic signals for frequency modulation, achieving a uniformly distributed power spectrum is a challenging task. This paper addresses this challenge by proposing a power spectrum homogenization strategy based on distribution transformation. The strategy transforms the task of achieving a uniformly distributed power spectrum in frequency modulation of high-dimensional chaotic signals to solve and equalize the probability density function of the chaotic signals, thereby further enhancing the ability of high-dimensional chaotic signals to suppress electromagnetic interference. Firstly, the difficulty of obtaining a smooth probability density function of chaotic modulation signals is solved using the kernel density estimation algorithm. Then, a distribution transformation algorithm is proposed to convert non-uniformly distributed chaotic modulation signals into uniformly distributed chaotic modulation signals. By using uniformly distributed chaotic modulation signals for frequency modulation, the objective of power spectrum equalization is achieved. Finally, taking the Chua’s chaotic signal as an example, the effectiveness of the proposed strategy is verified using an experimental platform based on a digital signal processor-controlled active clamping flyback converter. Full article

This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, focusing on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region’s hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study’s insights into soil thermal dynamics and system design optimization contribute to advancing sustainable urban infrastructure development. Full article

Fibre lasers are distinct in that their optical train is decoupled from the environment, especially in the all-fibre format. The attractive side of this decoupling is the simplicity of maintenance (no need to align the cavity or keep the optical elements clean), but the flip side of this is the difficulty one encounters when trying to control the output parameters. The components used in all-fibre laser cavities are usually different from those of free-space laser cavities and require new approaches to control. Essentially, an important task emerges, i.e., research and development of all-fibre laser components able to adjust their parameters (ideally by electronic means) in order to tune key parameters of the output radiation—wavelength, output power, and so on. The present review analyses the existing methods of control over the output parameters of mode-locked all-fibre lasers. It is further noted that a method relying on several independently pumped active media may be promising in this regard. Full article

Turmeric (Curcuma longa) contains curcumin, demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC). Nevertheless, curcumin is the most researched active ingredient for its numerous pharmacological effects. We investigated the impact of these curcuminoids found in Ryudai gold, an approved cultivar of Curcuma longa, on wound healing, inflammation, and diabetes. Sub-planter injections of carrageenan induced acute paw inflammation in rats. The wound-healing ability of 1% curcuminoids was examined by making a 6 mm round wound on the shaved dorsum of the mice with a biopsy punch. A single intraperitoneal injection of streptozotocin (50 mg/kg) was used to induce diabetes in mice. Curcuminoids at a dose rate of 100 mg/kg body weight were used with feed and as a gastric gavage to treat diabetes and inflammation in experimental animals. Paw thickness was measured at 1, 3, and 6 h following carrageenan injection. After three hours, mean paw volume was 58% in carrageenan-injected mice, which was 35%, 37%, and 31% in the curcumin, DMC, and BDMC groups, respectively. Histopathology of the paw tissue demonstrated severe infiltration of inflammatory cells and thickening of the dermis, which were remarkably improved by the curcuminoids. The wound-healing abilities were significantly higher in the curcumin- (95.0%), DMC- (93.17%), and BDMC-treated (89.0%) groups, in comparison to that of the control (65.09%) group at day nine. There were no significant differences in wound-healing activity among the groups treated with 1% curcuminoids throughout the study. Streptozotocin-induced diabetes was characterized by an increased blood glucose (552.2 mg/dL) and decreased body weight (31.2 g), compared to that of the control rats (145.6 mg/dL and 46.8 g blood glucose and body weight, respectively). It also caused an increase in serum alanine aminotransferase (ALT; 44.2 U/L) and aspartate aminotransferase (AST; 55.8 U/L) compared to that of the control group (18.6 U/L and 20.1 U/L, respectively). Histopathological examination of the liver showed that diabetes caused hepatic cellular necrosis, congestion of the central vein, and parenchymatous degeneration. However, all three curcuminoids significantly decreased blood glucose levels, ALT, and AST and improved the histopathological score of the liver. These results evidenced that not only curcumin but also DMC and BDMC have potent anti-inflammatory, wound healing, and anti-diabetic efficacy, and the Ryudai gold variety of turmeric could be used as a functional food supplement. Full article

The accuracy of estimating changes in terrestrial water storage (TWS) using Gravity Recovery and Climate Experiment (GRACE) level-2 products is limited by the leakage effect resulting from post-processing and the weak signal magnitude in adjacent areas. The TWS anomaly from 2003 to 2016 in the Dnieper River basin, with characteristics of medium scale and an adjacent weak TWS anomaly area, are estimated in this work. Two categories of leakage error repair approaches (including forward modeling, data-driven, single, and multiple scaling factor approaches) are employed. Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the efficiency of approaches. The TWS anomaly inverted by the forward modeling approach (FM) is more accurate in terms of RMSE 3.04 and NSE 0.796. We compared single and multiple scaling approaches for the TWS anomaly and found that leakage signals mostly come from semi-annual terms. From the recovered results demonstrated in the spatial domain, the South of Dnieper River basin is more sensitive to the leakage effect because of it is adjacent to a weak hydrological signal region near the Black Sea. Further, comprehensive climate insights and physical mechanisms behind the TWS anomaly were confirmed. The temperate continental climate of this river basin is shown according to the variation in TWS anomaly in the spatial domain. Snowmelt plays a significant role in the TWS anomaly of the Dnieper River basin, following the precipitation record and the 14-year temperature spatial distribution for February. We compared single and multiple scaling approaches for the TWS anomaly and found that leakage signals mostly come from semi-annual terms. Full article

It is essential to determine the most suitable machining method for magnesite-reinforced Aluminium 6061 composites, which possess excellent mechanical properties, especially notable tensile strength and hardness. The composites were produced using a stir-casting technique, incorporating reinforcements of lightly-calcined magnesite, dead burnt magnesite, and waste magnesite in weight fractions of 2.5%, 5%, and 7.5% within an aluminium 6061 matrix. Wire electrical discharge machining was employed to investigate the machining characteristics of these composites, using controllable process parameters such as cutting speed, pulse-on and pulse-off times, and the weight fraction of magnesites. Two performance indicators such as surface roughness and material removal rate were tested for various parameter combinations by central composite design. To comprehend the impact of the study parameters, contour charts were drawn. MRR increases at a high cutting speed of 2 mm/min when the pulse-on time changes from 120 μs to 125 μs. SR increases when the pulse-on times above 120 μs at all cutting speeds. High cutting speeds make high MRR irrespective of the weight fractions of reinforcement. High pulse-on times make the material melt more, which increases the material removal rate. Because specimen surface material erodes quickly and forms microcracks, high pulse-on time also results in high surface roughness. To optimize the WEDM machining conditions for each composite, hybrid SSO-DF and DFO-DF optimizers were developed by combining the desirability function with Salp-swarm optimization and Dragonfly optimization algorithms. The cutting speed of 2 mm/min and the pulse-on time of 114 μs produce the best performances on the composites. Full article

Solid lipid nanoparticles (SLNs) represent promising nanostructures for drug delivery systems. This study successfully synthesized SLNs containing different proportions of babassu oil (BBS) and copaiba oleoresin (COPA) via the emulsification–ultrasonication method. Before SLN synthesis, the identification and quantification of methyl esters, such as lauric acid and β-caryophyllene, were performed via GC-MS analysis. These methyl esters were used as chemical markers and assisted in encapsulation efficiency experiments. A 2² factorial design with a center point was employed to assess the impact of stearic acid and Tween 80 on particle hydrodynamic diameter (HD) and polydispersity index (PDI). Additionally, the effects of temperature (8 ± 0.5 °C and 25 ± 1.0 °C) and time (0, 7, 15, 30, 40, and 60 days) on HD and PDI values were investigated. Zeta potential (ZP) measurements were utilized to evaluate nanoparticle stability, while transmission electron microscopy provided insights into the morphology and nanometric dimensions of the SLNs. The in vitro cytotoxic activity of the SLNs (10 µg/mL, 30 µg/mL, 40 µg/mL, and 80 µg/mL) was evaluated using the MTT assay with PC-3 and DU-145 prostate cancer cell lines. Results demonstrated that SLNs containing BBS and COPA in a 1:1 ratio exhibited a promising cytotoxic effect against prostate cancer cells, with a percentage of viable cells of 68.5% for PC-3 at a concentration of 30 µg/mL and 48% for DU-145 at a concentration of 80 µg/mL. These findings underscore the potential therapeutic applications of SLNs loaded with BBS and COPA for prostate cancer treatment. Full article