Search Results (45)

Rice (Oryza sativa L.) is a vital staple crop, and the environmental risk assessment of transgenic varieties is crucial for formulating biosafety policies. Rice pollen grains are spherical, with an average diameter of 40.03 ± 2.75 μm. This study established a standardized protocol for in vitro pollen germination by first optimizing key culture conditions. A single-factor experimental design identified the optimal medium composition as 150 g/L sucrose, 40 mg/L boric acid, 20 mg/L calcium chloride, 10 mg/L monopotassium phosphate, and 10 mg/L magnesium sulfate. The ideal germination temperature was determined to be 31 ± 1 °C, with no germination observed below 16 °C or above 40 °C. Pollen germination rates declined significantly within 5 min post-isolation and ceased completely after 30 min. Building on this optimized protocol, a standardized evaluation method was developed, defining key assessment conditions at temperatures of 25/31/37 °C and post-isolation times of 0/5/15 min. Under these defined conditions, the pollen viability of glyphosate-resistant transgenic rice G2-6 was compared to its non-transgenic recipient ZH11. No significant differences were found at any tested time–temperature combination (p > 0.05). This work establishes a practical and reproducible standard for transgenic rice pollen assessment, offering a scientific basis for evidence-based biosafety regulation and policy-making. Full article

Participation in cancer prevention programs is suboptimal. Socioeconomic backgrounds play a role in cancer awareness and prevention programs. We conducted a narrative review, summarizing the evidence on the integration of cancer prevention extended to non-occupational risk factors at the workplace. Cancer prevention programs include screenings (colonoscopy, mammography, Pap-test), vaccinations (anti-HPV, anti-HBV), and interventions focused on lifestyle changes. Such strategies may face several barriers related to individual or environmental factors. The workplace is potentially an ideal setting for implementing extended cancer prevention strategies because (i) occupational health surveillance (OHS) targets adults, including hard-to-reach subgroups; (ii) it is structured, with health records and exams for risk assessment; (iii) it offers a key chance to promote cancer awareness and prevention through direct worker–physician interaction. Such an innovative approach requires a coordinated effort to build professional networks and manage high-risk workers. Its successful implementation depends on financial support and the active involvement of physicians, employers, and workers. Occupational-based cancer prevention represents a novel and promising strategy, though its feasibility and cost-effectiveness need to be assessed through large-scale studies. Full article

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution. Full article

Difference in Differences (DiD) is a useful statistical technique employed by researchers to estimate the effects of exogenous events on the outcome of some response variables in random samples of treated units (i.e., units exposed to the event) ideally drawn from an infinite population. The term “effect” should be understood as the discrepancy between the post-event realisation of the response and the hypothetical realisation of that same outcome for the same treated units in the absence of the event. This theoretical discrepancy is clearly unobservable. To circumvent the implicit missing variable problem, DiD methods utilise the realisations of the response variable observed in comparable random samples of untreated units. The latter are samples of units drawn from the same population, but they are not exposed to the event under investigation. They function as the control or comparison group and serve as proxies for the non-existent untreated realisations of the responses in treated units during post-treatment periods. In summary, the DiD model posits that, in the absence of intervention and under specific conditions, treated units would exhibit behaviours that are indistinguishable from those of control or untreated units during the post-treatment periods. For the purpose of estimation, the method employs a combination of before–after and treatment–control group comparisons. The event that affects the response variables is referred to as “treatment.” However, it could also be referred to as “causal factor” to emphasise that, in the DiD approach, the objective is not to estimate a mere statistical association among variables. This review introduces the DiD techniques for researchers in economics, public policy, health research, management, environmental analysis, and other fields. It commences with the rudimentary methods employed to estimate the so-called Average Treatment Effect upon Treated (ATET) in a two-period and two-group case and subsequently addresses numerous issues that arise in a multi-unit and multi-period context. A particular focus is placed on the statistical assumptions necessary for a precise delineation of the identification process of the cause–effect relationship in the multi-period case. These assumptions include the parallel trend hypothesis, the no-anticipation assumption, and the SUTVA assumption. In the multi-period case, both the homogeneous and heterogeneous scenarios are taken into consideration. The homogeneous scenario refers to the situation in which the treated units are initially treated in the same periods. In contrast, the heterogeneous scenario involves the treatment of treated units in different periods. A portion of the presentation will be allocated to the developments associated with the DiD techniques that can be employed in the context of data clustering or spatio-temporal dependence. The present review includes a concise exposition of some policy-oriented papers that incorporate applications of DiD. The areas of focus encompass income taxation, migration, regulation, and environmental management. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising photovoltaic candidates. A dataset of 1053 double perovskites was extracted from the Materials Project database, with 50 feature descriptors generated. Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. Four algorithms, including gradient-boosting regression (GBR), random forest regression (RFR), LightGBM, and XGBoost, were evaluated, with XGBoost demonstrating the best performance (R² = 0.934 for bandgap, R² = 0.959 for formation energy; MAE = 0.211 eV and 0.013 eV/atom). The SHAP (Shapley Additive Explanations) analysis revealed that the X-site electron affinity positively influences the bandgap, while the B″-site first and third ionization energies exhibit strong negative effects. Formation energy is primarily governed by the X-site first ionization energy and the electronegativities of the B′ and B″ sites. To identify optimal photovoltaic materials, 4573 charge-neutral double perovskites were generated via elemental substitution, with 2054 structurally stable candidates selected using tolerance and octahedral factors. The XGBoost model predicted bandgaps, yielding 99 lead-free double perovskites with ideal bandgaps (1.3~1.4 eV). Among them, four candidates are known compounds according to the Materials Project database, namely Ca₂NbFeO₆, Ca₂FeTaO₆, La₂CrFeO₆, and Cs₂YAgBr₆, while the remaining 95 candidate perovskites are unknown compounds. Notably, X-site elements (Se, S, O, C) and B″-site elements (Pd, Ir, Fe, Ta, Pt, Cu) favor narrow bandgap formation. These findings provide valuable guidance for designing high-performance, non-toxic photovoltaic materials. Full article

Due to external environmental factors, the layout of compound channels in estuarine ports is restricted. Moreover, with the opposing distribution of anchorages and terminals within the port, vessels navigating between these areas must cross the channel, severely affecting channel navigation safety and efficiency. In order to improve the efficiency of vessel scheduling, we analyze the layout characteristics of an estuarine port and its compound channel, summarize vessel navigation modes and traffic flow conflicts, and identify five key conflict areas. On this basis, we develop a multi-objective optimization model aimed at minimizing vessel waiting times and the total channel occupancy time ratio. This model incorporates constraints such as navigation rules, traffic flow conflicts, tidal effects, and traffic control. To solve the model, we propose an adaptive non-dominated sorting genetic algorithm, ANSGA-NS-SA, which integrates neighborhood search (NS) and Simulated Annealing (SA). The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. In terms of port scheduling performance, the proposed model and algorithm outperform the traditional First-Come-First-Served (FCFS) strategy and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), reducing total vessel waiting time by 33.8% and improving total channel occupancy ratio by 8.8%. This study provides a practical and effective decision support tool for estuarine port scheduling, enhancing overall port operational efficiency. Full article

As the cornerstone of the modern information industry, designing a high-performance circuit is crucial. Due to the influence of external environmental and asymmetric arrangements, non-ideal factors in analog integrated circuits (ICs) cannot be ignored, which makes the design process heavily reliant on human experience, and the design efficiency is low. Recently, scholars have conducted extensive research on optimization design methods for analog ICs by combining artificial intelligence and optimization algorithms. In this article, the developments and perspectives on optimization design methods for analog ICs are reviewed. In traditional design methods, particle swarm optimization (PSO), the genetic algorithm (GA), and reinforcement learning (RL) have been applied with different computer-aided design (CAD) tools. A variety of circuit simulation software have been developed, such as Cadence, Ngspice, Pspice, etc. Due to its high precision, comprehensive functionality, and full-process simulation, Cadence has been widely used in the design of analog ICs. These methods can improve the design efficiency to a certain extent. In the iterative process, running the simulation software to obtain performance metrics can waste a lot of time. Thus, efficient optimization design methods have been proposed to improve the design efficiency by establishing a proxy model of the circuit, which can replace simulation software. Accordingly, three research directions in this field are proposed. In summary, this article can aid scholars in quickly understanding the current status of optimization design methods for analog ICs and provide guidance for future research. Full article

Despite the growing influence of social media on both professional and personal lives, there is a noticeable lack of research on media literacy related to the perception of body image. This study aims to fill that gap by exploring well-being and the factors that support it. The sample included 520 students from both STEM (science, technology, engineering, and mathematics) and non-STEM fields. We utilized adapted psychometric scales to measure attitudes towards physical appearance (SATAQ), body shape dissatisfaction (BSQ), and psychological well-being (PWB). The procedure involved assessing well-being and media exposure, and completing questionnaires designed to measure the impact of media exposure. The findings revealed that both STEM and non-STEM students with higher well-being reported significantly lower negative effects from exposure to idealized body images compared to those with lower well-being. Additionally, greater self-acceptance, a stronger sense of purpose in life, and better environmental mastery were associated with a lower negative impact on body image. Comparing the groups, STEM students exhibited higher resilience to the negative effects of idealized body images. These insights highlight key protective factors critical for developing interventions and strategies for student resilience. Full article

Food waste presents a critical environmental and economic challenge across Europe. In the Mediterranean region, the agricultural industry generates considerable quantities of citrus fruits, leading to significant byproduct waste, which remains underutilized. To help address this, this study explored the valorization of orange peel waste using non-thermal ultrasonic-assisted extraction (UAE) and a one-factor-at-a-time experimental design to investigate the effects of nine chemical and physical UAE parameters. The goal was to identify ideal operational ranges for each parameter using several responses (bioactive compound recovery, antioxidant activity, and radical scavenging activity), thus elucidating the most influential UAE factors and their role in co-extracting various classes of natural compounds. The key findings revealed that the polarity and ionic potential of the extraction medium, tuned through ethanol:water or pH, significantly influenced both the chemical profile and bioactivity of the extracts. Notably, citric acid and citrates appeared to stabilize co-extracted compounds. Lower solid-to-liquid ratios increased yields, while particle sizes between 1400 and 710 µm enhanced phenolic recovery by approximately 150 mg/L GAE. In contrast, increases in pulse, probe diameter, immersion depth, and extraction time led to degradation of bioactive compounds, whereas the maximal amplitude improved phenolic acid recovery by up to 2-fold. Collectively, these insights provide a foundation for optimizing non-thermal UAE to valorize orange peel waste. Full article

In the field of direction of arrival (DOA) estimation, a common assumption is that array noise follows a uniform Gaussian white noise model. However, practical systems often encounter non-ideal noise conditions, such as non-uniform or colored noise, due to sensor characteristics and external environmental factors. Traditional DOA estimation techniques experience significant performance degradation in the presence of colored noise, necessitating the exploration of specialized DOA estimation methods for such environments. This study introduces a DOA estimation method for acoustic vector arrays based on a spectrum attention mechanism (SAM). By employing SAM as an adaptive filter and constructing a double-branch model combining a convolutional neural network (CNN) and long short-term memory (LSTM), the method extracts the spatial and temporal features of signals, and effectively reduces the frequency components of colored noise, enhancing DOA estimation accuracy in colored noise scenarios. At an SNR of −5 dB, it achieves an accuracy rate of 85% while maintaining a low RMSE of only 2.03°. Full article

This work evaluates the renewable energy development potential of Polish voivodeships based on the TOPSIS method and spatial autocorrelation analysis. Data were obtained from the Polish Local Data Bank, covering 22 indicators in the field of economic, social, environmental, and energy related to renewable energy initiatives. The TOPSIS method was applied to construct a synthetic indicator for each voivodeship, facilitating a hierarchical ranking based on their proximity to an ideal solution representing optimal conditions. The results indicate that the Mazowiecki voivodeship leads the list in terms of renewable energy development potential, followed by Małopolskie i Pomorskie, and that this is mainly due to good economic conditions and large investments in renewable energy projects. Spatial autocorrelation analysis yielded a Moran’s I value of –0.1137 with a Z score of 0.303 and a p value of 0.752, suggesting a weak negative spatial autocorrelation that is not statistically significant. This implies that the distribution of renewable energy potential across voivodeships is largely random and is not influenced by spatial proximity. The study concludes that non-spatial factors play a more significant role in renewable energy development potential, offering valuable insights for policymakers and stakeholders to allow them to focus on economic and social variables when promoting renewable energy initiatives in Poland. Full article

Soil erosion is one of the most significant problems in global environmental development. Assigning, analyzing, and quantifying the main physical characteristics of drainage basins are powerful keys in identifying regions where there is a higher risk of soil erosion and where prompt mitigation actions are needed. Drainage basins and their drainage systems are ideally defined using the analysis morphometric parameters and their quantitative description. The present study aims to analyze morphometric parameters to prioritize drainage basins that are prone to erosion. Topographic sheets and remotely sensed digital elevation model (DEM) datasets have been prepared and analyzed using geospatial techniques to delineate drainage basins of different sizes and extract different ordered drainage systems. Based on the analysis of morphometric parameters, the Rabigh area was divided into 12 drainage basins, which significantly contribute to determining soil erosion priority levels. The present study selected and applied the most effective morphometric parameters to rank and prioritize the drainage basins of the study area after considering the crucial quantitative characteristics, such as linear, relief, and areal aspects. For each single basin, the compound factor was assigned from several morphometric parameters and applied to rank the Rabigh area. The results confirm that Basins 1, 4, 11, and 12 require a high level of soil erosion priority over an area of 2107 km²; however, Basins 3, 8, 9, and 10 have little degradation and a low level of soil erosion priority. Therefore, in the regions where high soil erosion is a factor, mitigation techniques such as terracing, filter strips, contouring, and other effective and useful structural and non-structural methods should be applied. Full article

Slovenia, influenced by Slavic, Mediterranean, and Balkan cultures, along with Austro-Hungarian traditions and strong environmental concerns, is an ideal case study for understanding consumer perceptions of edible insects and increasing their acceptance as an alternative protein source. A survey conducted in Slovenian and English with 537 participants examined existing perceptions and acceptance of edible insects as food and livestock feed. Findings show moderate interest in insects, particularly in non-visible, integrated forms, despite most participants not having tried whole insects. Young, educated individuals and those residing in rural areas have tried insects more often than other sociodemographic groups. Men showed more interest in entomophagy compared to women. Crickets, grasshoppers, and locusts were most acceptable, while cockroaches were least favored. Economic factors are crucial, with a preference for insect-based products priced similarly to conventional foods. The majority also support using insects as livestock feed. These results can aid policymakers at regional and national levels, help businesses market these products, and contribute to the literature on consumer responses in different European regions regarding edible insects as a sustainable food source. Full article