Search Results (7,521)

In this study, a sustainable advanced manufacturing technology was developed using a holistic approach for finishing heat-treated 42CrMo4 steel cylindrical parts. The proposed technology is based on a hybrid combined process (HCP) involving cool-assisted dry hard turning and subsequent cool-assisted dry diamond burnishing (DB). A cold-air cooling (without lubrication) condition was achieved using a special device with a cold-air nozzle based on the principle of vortex tubes. The study was conducted in two stages. In the first stage, only the hard turning process was investigated using variance analysis to determine the significant governing factors (feed rate and cutting insert radius). The second stage involved studying and optimising the HCP. This approach incorporated the two significant turning process factors, along with three additional DB process factors: the radius of the diamond insert, burnishing force and feed rate. The selected objective functions were the average roughness, skewness, kurtosis, surface microhardness, residual surface axial stress and fatigue limit. The fatigue limit was determined using the accelerated Locati method. Mathematical models of the objective functions were obtained using experiments and regression analyses. Using multi-objective optimisation, the HCP was optimised based on two criteria: (1) maximum wear resistance under boundary lubrication conditions and (2) maximum fatigue limit. The optimisation tasks were solved by searching for the Pareto optimal solution approach using QStatLab and the NSGA II algorithm. The compromise optimal values of the governing factors, maximising the fatigue limit (690 MPa), are as follows: feed rate in turning and DB of 0.05 mm/rev, radius of the cutting insert of 0.8 mm, diamond insert radius of 2 mm, and burnishing force of 50 N. Experimental verification showed a good agreement with the optimised solutions for surface integrity and fatigue limit characteristics. Full article

Globally, states are incorporating various digital tools with the purpose of accelerating customs management of export and import goods. An example of this is the implementation of electronic single windows that seek to streamline the transmission of information on export and import operations by generating a single data entry point that can be accessed by different interconnected state and private entities. For customs clearance, some States require the collaboration of intermediaries known as customs agents: private actors who collaborate in the public function of customs control. Customs agents are licensed by the State to act as such, and are hired by exporters and importers to manage their operations with the customs authority. A digital reform of this type is a governance mechanism that involves coordination between institutions, interaction between the State and private actors, and the redefinition of the role of different actors. However, this can create tensions with actors who collaborate with the State. The objective of this study is to determine the perceptions of customs agents—as incumbent actors and collaborators of the State—regarding the implementation of new technologies by the State, through an analysis of the case of Chilean customs agents in the context of the implementation of a single window system for foreign trade by the Chilean customs authority. To this end, a qualitative study was conducted based on 15 semi-structured interviews with customs agents, including both current and former government officials. The data obtained were coded using MAXQDA software. The results show that the State has had to integrate customs agents into the implementation process, primarily due to their expert knowledge. On the other hand, some customs agents have viewed the implementation of the single window system with apprehension, while others have attempted to embrace the technological change since its inception, collaborating with the government. In order to protect their profession, the agents have sought opportunities for dialogue with the authorities and state agencies. Furthermore, customs agents and public officials agree that one of the system’s main weaknesses is the lack of participation from other government agencies in the operation of the single window system, thus failing to meet the State’s own established plans: the interconnection of different state agencies at a single point. This, in turn, reveals weaknesses in collaborative governance. In short, the implementation of a single window has been a major challenge for actors collaborating in the public customs function, producing tensions in its governance. Full article

Coffee is a major export earner for Uganda, raking in over USD 2 billion in 2025. The global price of coffee is tagged to the perceived quality in the cup which in turn is affected by the chemical composition of the green bean. Breeding for market-preferred Arabica coffee varieties is a major objective of coffee breeding programs. Determination of coffee bean chemical constituents is routinely done through expensive, slow and tedious laboratory procedures, making it unsustainable of resource-limited public sector coffee breeding programs. Here, we demonstrate the use of near-infrared spectroscopy (NIRS) and the machine learning algorithms partial least squares (PLS), random forest (RF) and support vector machine (SVM) for the prediction of caffeine, protein and trigonelline in Arabica coffee. NIRS provides a fast, accurate and reliable method of simultaneously predicting multiple sample constituents. Ripe coffee cherries were picked from 172 farmers’ fields, air dried in the laboratory at room temperature and processed to green beans. NIRS spectra were taken on the milled green bean at 400–2500 nm, with a 0.5 nanometer (nm) step. Reference data for caffeine, protein and trigonelline were collected on the same sample scanned with NIRS. A set of 12 spectral pretreatments were applied prior to making calibrations with the PLS, RF and SVM algorithms and 70% of the data as a training set and 30% as a test set. Caffeine content of reference samples ranged from 1.94–3.0 g/100 g, protein content ranged from 11.16–15.94% while trigonelline ranged from 0.94–1.23 g/100 g. The best calibrations for all algorithms and analytes were obtained using raw (untreated) spectra, which gave the same results as the Savitzky–Golay (SG) pretreatment. For caffeine, the best model (R²p = 0.89, RMSEP = 0.007, RPD = 3.34) was obtained with the SVM algorithm, while for protein, the best model (R²p = 0.98, RMSEP = 0.14, RPD = 6.92) was obtained using the PLS algorithm. Finally, for trigonelline, all three models had very high prediction accuracies (R²p = 0.98–0.99, RMSEP = 0.007–0.009, RPD = 8.53–10.52). Collectively, these results demonstrate the potential of using NIRS for rapid and simultaneous prediction of coffee green bean constituents to aid selection decisions. Full article

High-resolution remote sensing visual question answering (RS-VQA) requires models to identify question-relevant regions and reason over fine-grained visual evidence. However, existing vision–language models usually rely on fixed global image inputs, which may lose critical local details in ultra-high-resolution imagery and struggle with sparse informative regions, large object-scale variations, and complex spatial layouts. To address these challenges, this paper proposes a crop-tool-augmented active perception framework with reinforcement learning. The framework introduces structured tokens to explicitly organize the reasoning process into question understanding, cropping decision-making, local evidence acquisition and final answer generation. Based on this design, the model can actively determine whether a cropping operation is needed and select task-relevant regions for further inspection. To enable stable tool-use and multi-turn reasoning in a compact vision–language model, we construct teacher-guided cropping reasoning trajectories from high-resolution images, question–answer pairs, and annotated regions in the LRS-GRO dataset, and use them for cold-start supervised fine-tuning of Qwen2.5-VL-3B. Furthermore, we introduce Group Relative Policy Optimization to refine the model’s active perception policy. A region-aware reward function is designed by integrating output-format constraints, reference-region coverage, answer semantic consistency, and cropping penalties, which encourages compact and informative region selection while reducing redundant tool invocations. Experiments on VRSBench, MME-RealWorld-RS, XLRS-Bench, and LRS-VQA demonstrate that the proposed method achieves competitive overall performance compared with closed-source, open-source, and remote-sensing-specific vision–language models, and obtains the best or comparable results on most benchmarks. Ablation studies further verify the effectiveness of structured supervised fine-tuning, reinforcement learning optimization, and the proposed reward design. Full article

The Sumapaz páramo, the most extensive continuous páramo complex on a global scale and a strategic component in the hydrological regulation of central Colombia, is undergoing a progressive ecohydrological degradation whose integrated characterization through multitemporal biophysical indicators remains limited. To address this gap, the present study examines the transformation dynamics of the system by articulating four analytical components: precipitation modeling based on IDEAM stations (1995–2025), thermal trend analysis from WorldClim grids (2000–2024), multitemporal spectral analysis of four normalized difference indices (NDVI, NDWI, NDSI, NBR) derived from Landsat imagery (2000–2025), and Corine Land Cover cartography (2000–2018). The findings reveal a spatial decoupling between the precipitation distribution, which tends to shift toward lower altitudinal belts, and the storage areas in peat and Andosols of the páramo core, with a consequent reduction in effective recharge regardless of the total precipitated volume. Paradoxically, an almost complete contraction of surfaces with a positive water signal coexists with the expansion of photosynthetic activity, a phenomenon attributable to processes of shrub encroachment, thermophilization, and nutrient enrichment rather than to a functional recovery of the ecosystem. The cartographic analysis, in turn, confirms the advance of agricultural and livestock uses over native regulating covers. The convergence of these vectors configures a multivectorial degradation scenario that escapes monitoring based on a single index and that demands management strategies oriented simultaneously toward anthropic pressure, the spatial redistribution of precipitation, and the implementation of integrated ecohydrological surveillance systems. Full article

Stepper-motor-driven Selective Compliance Assembly Robot Arms are susceptible to motion control challenges under short-stroke and high-frequency start–stop conditions, including high sensitivity to pulse timing, difficulty in multi-joint coordination, and insufficient trajectory smoothness. To address these issues, this paper proposes an optimized motion control method for smooth execution of obstacle-avoidance trajectories, integrating path smoothing, asymmetric S-curve velocity planning, and pulse-frequency-based multi-axis synchronization. First, piecewise cubic Hermite interpolation, Gaussian smoothing, and end-effector-based equidistant resampling are applied to post-process Rapidly-exploring Random Tree-generated paths, thereby eliminating polyline turning points and improving uniformity of waypoint distribution. Second, an asymmetric S-curve velocity planning method with nonzero boundary velocity constraints is developed, and multi-axis synchronization is achieved based on the maximum segment duration principle. Finally, instantaneous reference velocities are converted into per-axis pulse frequency commands via proportional mapping, enabling real-time stepper motor drive control. Experimental results show that the proposed method reduces the obstacle-avoidance path length by 8.52% and significantly decreases the dispersion of trajectory step sizes. In single-segment dynamic simulations, the proposed method reduces the peak dynamic output force by 62%. In real robot experiments, the average motion time across three obstacle-avoidance tasks is reduced by approximately 55.21%, while end-effector trajectory continuity and inter-joint coordination are improved, suggesting the effectiveness and preliminary engineering feasibility of the proposed method under the tested conditions. Full article

In an Eastern Jewish community in 1467, there appeared a book in Judeo-Arabic entitled Kitāb maḥāsin al-ādāb [The Book of Excellent Conduct]. In its introduction, the author states that he is translating an earlier Hebrew work on ethics called Sefer musar [Book on Ethics]. That Hebrew work, broadly known as Mishle ha-ʿarav [The Sayings of the Arabs], is in turn a thirteenth-century Provençal translation of a still-earlier Arabic work. This article takes three chapters of the Judeo-Arabic version as a vantage point for a comparative cultural analysis: chapter 9 on speaking at the right time, chapter 10 on acting humbly, and chapter 28 on eating in moderation. As medieval translation often involved a process of adaptation and re-creation, I will (1) explore the translation strategies the author employed to transform a Jewish Hebrew musar work into a Jewish Judeo-Arabic adab work in terms of genre, form, and content, and (2) examine how the ethical values discussed in the three chapters were adapted for the new linguistic audience. The results will shed light on the understanding of how distant Jewish communities interpreted seemingly similar texts in different ways. Full article

Positive Leadership in Higher Education is a key process; however, its connection to the mental health and quality of life of women in leadership roles at universities has been explored only to a limited extent. This study aimed to analyze the structural relationships between positive leadership, positive mental health, and quality of life in women university leaders, and to explore the lived experiences and coping strategies that explain these associations. A mixed-methods explanatory sequential design (QUAN → qual) was employed. 45 women holding senior administrative positions in Peruvian universities completed three standardized scales. The quantitative results reveal that positive leadership significantly predicted positive mental health, which in turn strongly explained quality of life, confirming a partial mediation model. The qualitative results highlighted persistent psychosocial and structural challenges—gender bias, the glass ceiling, role overload, institutional pressure, impostor syndrome, and isolation—alongside coping strategies focused on self-care, emotional regulation, purpose-driven leadership, empowerment, sorority, and organizational transformation with a gender perspective. Positive leadership emerges as a key organizational resource that enhances women’s mental health and quality of life. Institutional cultures grounded in inclusion, care, and relational leadership are essential for sustainable and transformative women’s leadership in higher education. The WISE: Women Integrated for Sustainable Empowerment is presented as a practical action guide for positive female leadership. Full article

The common products of acidogenesis, the key stage in the process of anaerobic digestion, are lactate, butyrate, propionate, and acetate. They were decomposed in the Up-flow Anaerobic Sludge Blanket bioreactors working in continuous-flow open systems. A comprehensive analysis of variations in both isotopic ratios and concentrations of organic acids in the effluents was conducted to enhance comprehension of methanogenic processes. The analysis of carbon isotope fractionation in the CO₂-CH₄ system, as evidenced by the α¹³C_CO2-CH4 factor, has indicated that acetate decarboxylation has occurred. Furthermore, a decline in CO₂ levels was observed, accompanied by the predominance of butyrate and propionate, despite the presence of acetic acid in the effluents from all the bioreactors. Butyric acid demonstrated the greatest resistance to decomposition, resulting in ¹³C-enrichment of DIC. Lactic acid was utilised almost entirely. The observations presented above were subsequently validated through statistical analysis. A comparative analysis of the δ¹³C(CH₄) and δ¹³C(CO₂) values of our study with those of other natural substrates (detritic lignite, xylite, maize silage, and cattle manure) was undertaken, and it was found that isotope fractionation differs significantly in closed (potential thermodynamic processes) and open systems (expected Rayleigh processes). In the context of open systems, the isotope fractionation factor α¹³C_CO2-CH4 during methaneogenesis has been observed to attain values that are consistent with those observed in CH₄ oxidation. The study revealed that the presence of acetate in the substrate (i.e., the M4 bioreactor) led to the generation of CO₂ with a higher proportion of light carbon isotopes. This, in turn, resulted in a shift in the isotope fractionation factor (i.e., α¹³C_CO2-CH4) to values below 1.03. Our results suggest that methanogenic pathway signatures in open, continuous-flow systems may only be partially apparent. This is because substrate depletion drives Rayleigh-type isotope enrichment, while the dominance of a single substrate and its constant inflow stabilise pathway expression and shift control towards substrate dynamics rather than intrinsic microbial changes. Our finding suggests that isotope-based diagnostics could enhance process control in biogas plants by identifying substrate-driven limitations and facilitating more efficient and stable CH₄ production. Full article

The article presents a methodology for multi-objective compromise optimization of the process of turning on CNC machines with robotic loading by the methods of determining the optimum compromise area and carrying out optimization, using the generalized arithmetic mean utility function with weight coefficients. The methodology has been applied to determine the optimal cutting conditions, ensuring the best combination of technological parameters for the CNC turning process when machining parts made of 42CrMoS4 steel with cutting tools from different manufacturers. A complex study and modeling of the main technological parameters (production rate, cutting tool lifetime, and roughness of machined surfaces) in the CNC turning process have been performed depending on the conditions of cutting. By applying a genetic algorithm, the optimal conditions for implementing the process using both optimization methods have been determined, and a comparative analysis of the technological parameters has been made. Models have been created for predicting the number of machined parts, which exclude unplanned stops for tool changes, under the specified optimal conditions of cutting, taking into account the capacity of the tool magazine, the loading mechanism, and the volume of the production batch. Full article

Continuous cropping obstacles (CCOs) severely disrupt the soil physical structure, nutrient cycling, and microbial community balance, leading to decreased crop productivity. However, the effects of soil amendment interventions on bacterial, fungal, and archaeal communities in foxtail millet (Setaria italica (L.) P. Beauvois.) systems are not well comprehended. Selected physical, chemical, biological soil amendment and crop rotations were evaluated for their effects on rhizosphere soil microbial diversity, composition, network characteristics, community assembly processes, niche breadth, and multi-functionality. High-throughput sequencing of 16S rRNA and ITS regions demonstrated that earthworm castings significantly enhanced archaeal Chao1, Shannon diversity, and multi-functionality. Meanwhile, Bacillus mucilaginosus enhanced fungal diversity, and B. subtilis promoted bacterial network complexity. In continuous cropping soil alone, microbial communities exhibited low diversity and were predominantly governed by ecological drift. In contrast, soil amendment treatments shifted assembly toward deterministic processes, including homogeneous and heterogeneous selection. However, the analysis demonstrated greater complexity and niche width in bacterial communities than in fungal or archaeal communities, with keystone modules driven by Actinomycetota, Ascomycota, and Halobacteriota. Structural equation modeling indicated that soil physicochemical properties directly mediated rhizosphere soil microbial alpha diversity, which in turn positively influenced multi-functionality. Overall, organic amendments and microbial inoculants were associated with increases in microbial diversity, network stability, and functionality in this pot experiment, suggesting that such practices may help mitigate CCOs and sustainably improve foxtail millet productivity in dryland agricultural systems. Full article

The ultimate goal of integrating artificial intelligence into education is to ensure the long-term stability, quality, and sustainability of the educational process, turning it into a tool that consistently improves teaching and learning. Yet its sustainable and responsible integration depends largely on a positive mindset and the pedagogical willingness of future teachers. This study examines the attitudes and readiness of pre-service teachers, specializing in preschool, primary, and subject-specific science education, toward AI integration, with a specific focus on sustainable science education and Green Lab concepts. A mixed-methods study was conducted on a sample of 251 students from the University of Split. Data were analyzed using exploratory factor analysis, standard and Welch ANOVA with Tukey’s HSD and Games–Howell post hoc tests, and multiple linear regression in IBM SPSS 20, and qualitative content analysis. The findings reveal perceived usefulness as a primary driver of AI acceptance across all groups. Science students demonstrated the highest levels of ethical and critical sensitivity but provided the lowest ratings for AI’s practical application in sustainable science education, expressing cautious attitudes and distinct concerns about system reliability. However, no significant difference was found between students with and without a science background in regard to AI’s potential to facilitate sustainable scientific concepts. Furthermore, behavioral analysis demonstrated that even initial, occasional exposure to AI tools significantly boosted students’ perceptions of its utility and sustainable application compared to non-users, whereas increasing the frequency of use resulted in no additional gains. The transition toward sustainable science education requires moving beyond technical literacy toward a comprehensive framework that integrates pedagogical usefulness with ethical responsibility and sustainable scientific application. Future studies should explore potential models that combine the methodological creativity of pre-service educators and teachers with the analytical rigor of science students. Ultimately, this research underscores that an educational policy must integrate digital advancements while strictly maintaining ethical standards and the essential role of human supervision. Full article

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while the machine is in operation. These diagnostic methods depend on different signal processing techniques that are used to extract insulation-sensitive information from measured signals. This paper presents a review of the diagnostic signal processing techniques that have been applied to stator winding insulation condition monitoring, spanning time-domain, frequency-domain, time–frequency-domain and data-driven approaches. Where appropriate, the underlying mathematical formulation of the reviewed technique is presented, the physical basis for its sensitivity to insulation condition monitoring is discussed, and the key strengths and limitations are identified. A comparative analysis with summary tables is provided to highlight the trade-offs between detection sensitivity, computational cost, hardware requirements and practical deployment considerations. The review shows that time- and frequency-domain methods are simple to implement, while time–frequency and data-driven methods generally offer higher performance, but require greater computation and validation. Also, the comparison shows that turn-to-turn and groundwall insulation monitoring have received more research attention, while phase-to-phase remains less developed. This review concludes by identifying the challenges and future research directions needed to advance this field from laboratory demonstrations toward industrial adoption. Full article

Synthetic polymers are commonly incorporated into natural polymer nanofibers to enhance their overall performance. In this study, we investigated the effects of different poly(vinyl alcohol) (PVA) concentrations (0%, 2.5%, 5%, 7.5%, and 10% w/v) on the properties of gelatin/zein nanofibers. With increasing PVA concentration, fiber diameter significantly decreased from 976 ± 165 nm to 262 ± 60 nm, followed by a gradual increase to 396 ± 81 nm, indicating that PVA plays a crucial role in fiber diameter regulation. At higher concentrations (7.5% and 10% w/v), PVA became dominant, inducing protein aggregation and porous channel formation, which in turn increased the water vapor permeability of the composites. Rheological and mechanical analyses revealed that at these concentrations, the composites exhibited enhanced flexibility while maintaining network stability, demonstrating strong application potential. Furthermore, PVA incorporation induced a slight increase in the primary decomposition temperature (from 320.77 °C to 328.67 °C), indicating enhanced intermolecular compatibility and restricted segmental mobility within the protein–PVA network. Overall, these results establish a theoretical basis for tailoring fiber architecture and interfacial compatibility in natural–synthetic polymer composites. Further, the structural attributes of the resulting fibrous mats indicate their potential for food processing applications beyond conventional food packaging, including use as filtration media. Full article

Snake venom is a remarkably complex cocktail of bioactive molecules capable of hijacking diverse host physiological processes, yet how individual venom components drive these cellular responses remains a bit of a black box. To map these dynamics, we ran a comparative transcriptomic analysis on human osteoclastogenic cultures, exposing them continuously to crude Bothrops moojeni venom and its high (HMM) and low (LMM) molecular mass fractions throughout differentiation. This allowed us to capture the cumulative transcriptional shifts that unfold across the entire lifecycle of osteoclast development. The crude venom triggered a sweeping response, deeply impacting neuroimmune, extracellular matrix remodeling, inflammatory, and apoptotic pathways—reflecting a massive reshuffling of cellular regulatory networks. When we looked at the fractions, clear dividing lines emerged. The HMM fraction, packed with metalloproteinases and serine proteases, mostly drove pathways tied to cytoskeletal remodeling, intracellular trafficking, and osteoclast-associated signaling. In contrast, the LMM fraction—home to phospholipases A₂, disintegrins, and small peptides—steered a much more targeted course, influencing immune regulation, proliferative signaling, and metabolic homeostasis while noticeably turning down catalytic and binding functions. Interestingly, all venom-treated groups shared a drop-off in ATP-dependent and ligand-binding categories, pointing to a common disruption in core metabolic and signaling processes. Taken together, these findings offer a clearer mechanistic look at how different B. moojeni venom components target bone remodeling pathways, highlighting the power of transcriptomics for untangling complex venom–host interactions. Full article