Search Results (312)

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application. Full article

High-density polyethylene (HDPE) is a widely used polymer known for its excellent mechanical properties and chemical resistance. This study investigated the impact of incorporating varying percentages of nano-graphene particles (NGP) into HDPE on its thermal, mechanical, and tensile properties. Differential scanning calorimetry (DSC) analysis revealed that the addition of NGP enhanced the thermal stability and crystallization behavior of HDPE, with optimal performance observed at a 5% NGP concentration. Mechanical property evaluations indicated that small additions of NGP initially reduced zero-shear viscosity from 114,667 Pa·s to 44,045 Pa·s at 1% NGP, but higher concentrations improved the material’s rigidity and strength, with the best results at 3% NGP, where the flexural modulus reached 980 MPa. Tensile tests showed that while small amounts of NGP may decrease tensile strength from 26.4 MPa to 23.5 MPa at 1% NGP, higher concentrations significantly enhanced these properties, with tensile strength at break reaching 27 MPa and tensile elongation peaking at 20.8% at 7% NGP. The findings highlight the potential of NGP to enhance the performance of HDPE composites, making them suitable for a wide range of industrial applications. These enhanced composites are particularly important for the bottling industry, where improved material properties can lead to lighter, stronger, and more efficient packaging solutions. Full article

This study investigates the influence of key fused deposition modeling (FDM) process parameters, namely, print speed, infill percentage, layer thickness, and layer width, on the tensile properties of PLA specimens produced using 3D printing technology. A Taguchi L9 orthogonal array was employed to design the experiments efficiently, enabling the systematic evaluation of parameter effects with fewer tests. Tensile strength and elongation at break were measured for each parameter combination, and statistical analyses, including the signal-to-noise (S/N) ratio and analysis of variance (ANOVA), were conducted to identify the most significant factors. The results showed that infill percentage significantly affected tensile strength, while layer thickness was the dominant factor influencing elongation. The highest tensile strength (47.84 MPa) was achieved with the parameter combination of 600 mm/s print speed, 100% infill percentage, 0.4 mm layer thickness, and 0.4 mm layer width. A linear regression model was developed to predict tensile strength with an R² value of 83.14%, and probability plots confirmed the normal distribution of the experimental data. This study provides practical insights into optimizing FDM process parameters to enhance the mechanical performance of PLA components, supporting their use in structural and functional applications. Full article

With the ongoing development of lightweight automobiles, research on new aluminum alloys and welding technology has gained significant attention. Friction stir welding (FSW) is a solid-state joining technique for welding aluminum alloys without melting. In this study, novel squeeze-cast Al-Si-Mg-Fe-Zn alloys with different Zn contents (0, 3.4, 6.5, and 8.3 wt%) were friction stir welded (FSWed) at a translational speed of 200 mm/min and a rotational speed of 800 rpm. These parameters were chosen based on the observations of visually sound welds, defect-free and fine-grained microstructures, homogeneous secondary phase distribution, and low roughness. Zn can affect the microstructure of Al-Si-Mg-Fe-Zn alloys, including the grain size and the content of secondary phases, leading to different mechanical and corrosion behavior. Adding different Zn contents with Mg forms the various amount of MgZn₂, which has a significant strengthening effect on the alloys. Softening observed in the weld zones of the alloys with 0, 3.4, and 6.5 wt% Zn is primarily attributed to the reduction in Kernel Average Misorientation (KAM) and a decrease in the Si phase and MgZn₂. Consequently, the mechanical strengths of the FSWed joints are lower as compared to the base material. Conversely, the FSWed alloy with 8.3 wt% Zn exhibited enhanced mechanical properties, with hardness of 116.3 HV_0.2, yield strength (YS) of 184.4 MPa, ultimate tensile strength (UTS) of 226.9 MP, percent elongation (EL%) of 1.78%, and a strength coefficient exceeding 100%, indicating that the joint retains the strength of the as-cast one, due to refined grains and more uniformly dispersed secondary phases. The highest corrosion resistance of the FSWed alloy with 6.5%Zn is due to the smallest grain size and KAM, without MgZn₂ and the highest percentage of {111} texture (24.8%). Full article

Fanconi anemia (FA) is a DNA repair deficiency disorder associated with genomic and chromosomal instability and a high cancer risk. In a small percentage of cases, FA is caused by biallelic pathogenic variants (PVs) in the BRCA2/FANCD1 gene, defining the FA-D1 subtype. Experimental and epidemiologic data indicate that the complete absence of BRCA2 is incompatible with viability. Therefore, cells from individuals affected with FA caused by biallelic BRCA2 PVs must have a residual BRCA2 function. This activity may be maintained through hypomorphic missense mutations, translation termination–reinitiation associated with a translational stop mutation, or other non-canonical or uncommon translation initiation and elongation events. In some cases, however, residual BRCA2 function is provided by alternatively or aberrantly spliced BRCA2 transcripts. Here, we review and debate aspects of the contribution of splicing in the 5′ segment to BRCA2 functions in the context of PVs affecting this largely intrinsically disordered protein region, with a focus on recent findings in individuals with FA-D1. In this Perspective, we also discuss some of the broader biological implications and open questions that arise from considering 5′-terminal BRCA2 splicing in light of old and new findings from FA-D1 patients and beyond. Full article

Polystyrene (PS), a thermoplastic polymer, is used in many applications due to its mechanical performance, good chemical inertness, and excellent processability. However, it is doped with different nanomaterials for reasons such as improving its electrical conductivity and mechanical properties. In this study, carbon nanotube (CNT)-added PS composites were produced with the aim of combining the properties of CNTs, such as their low weight and high tensile strength and Young’s modulus, with the versatility, processability, and mechanical properties of PS. In this study, multi-walled carbon nanotube (MWCNT)-reinforced polystyrene (PS) composites with different percentage ratios (0.1, 0.2, and 0.3 wt%) were prepared by a plastic injection molding method. The mechanical, microstructural, and thermal properties of the fabricated PS/MWCNT composites were characterized by Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) Spectroscopy, Atomic Force Microscopy (AFM) and Thermogravimetric Analysis (TGA) techniques. AFM analyses were carried out to investigate the surface properties of MWCNT-reinforced composite materials by evaluating the root mean square (RMS) values. These analyses show that the RMS value for MWCNT-reinforced composite materials decreases as the weight percentage of MWCNTs increases. The TGA results show that there is no change in the degradation temperature of the 0.1%- and 0.2%-doped MWCNT composites compared to pure polystyrene, but the degradation of the 0.3%-doped MWCNT composite is almost complete at a temperature of 539 °C. Among the PS/MWCNT composites, the 0.3%-doped MWCNT composite exhibits more thermal stability than pure PS and other composites. Similarly, the values of the percentage elongation and tensile strength of 0.3% MWCNT-doped composites was obtained as 1.91% and 12.174% mm², respectively. These values are higher than the values of 0.1% and 0.2% MWCNT-doped composite materials. In conclusion, the mechanical and thermal properties of MWCNT-reinforced PS polymers provide promising results for researchers working in this field. Full article

Synthetic plastic pollution represents a major global concern, driving the search for sustainable and biodegradable packaging alternatives. However, many biodegradable plastics suffer from inadequate mechanical performance. This study aimed to develop a biodegradable film based on cassava starch, incorporating onion peel powder (OPP), a byproduct rich in quercetin derivatives, as a reinforcing agent and plasticized with crude glycerol. A Central Composite Design (CCD), implemented using Minitab 19, was employed to investigate the effects of starch (60–80%) and OPP (0–40%) content on the mechanical properties and biodegradability of the resulting bioplastics. Three optimized formulations were identified according to specific performance criteria. The first formulation, containing 72.07% starch and 21.06% OPP, was optimized for maximum tensile strength while maintaining target values for elongation and biodegradability. The second, composed of 77.28% starch and 37.69% OPP, was optimized to enhance tensile strength and biodegradability while minimizing elongation. The third formulation, with 84.56% starch and 27.74% OPP, aimed to achieve a balanced optimization of tensile strength, elongation, and biodegradability. After a 30-day soil burial test, these formulations exhibited weight loss percentages of 31.86%, 29.12%, and 29.02%, respectively, confirming their biodegradability. This study optimized the mechanical and biodegradability properties of cassava starch-based bioplastics using statistical modeling. The optimized formulations show potential for application in sustainable food packaging. Full article

This study investigated the application of neural network techniques to predict the mechanical properties of low-carbon hot-rolled steel strips using industrial data. A feedforward neural network (FFNN) model was developed to predict the yield strength (YS), ultimate tensile strength (UTS), and elongation (%EL) based on the chemical composition and processing parameters. For the low-carbon hot-rolled steel strip (C: 0.02–0.06%, Mn: 0.17–0.38%), 435 datasets were utilized with 17 input parameters, including 15 composition elements, finish rolling temperature (FRT), and coil target temperature (CTT). The model was trained using 335 datasets and tested using 100 randomly selected datasets. The optimum network architecture consisted of two hidden layers with 34 neurons each, achieving a mean squared error of 0.014 after 200,000 iterations. The model predictions showed excellent agreement with the actual values, with mean percentage errors of 4.44%, 3.54%, and 4.84% for the YS, UTS, and %EL, respectively. The study further examined the influence of FRT and CTT on mechanical properties, demonstrating that FRT has more complex effects on mechanical properties than CTT. The model successfully predicted property variations with different processing parameters, thereby providing a valuable tool for alloy design and process optimization in steel manufacturing. Full article

Pollination is a determining factor in achieving economic yield in hazelnut cultivation, and together with variable climate conditions, this requires the use of artificial pollination. This study evaluated the efficiency of artificial pollination performed with a manual sprayer using pollen from three pollinizer cultivars on the ‘Tonda Francescana^®’ commercial orchard and the effect of different pollen sources on nuts. Dry pollens were applied by a Pollen Blower machine twice during female blooming. The pollen of ‘Nocchione’ determined the highest fruit set and yield per tree, even if it did not determine the highest blank seed percentage. The open pollinizers exhibited a lower sphericity and shape index (NSI), ‘Camponica’ pollen was associated with the biggest nut and kernel; ‘San Giovanni’ pollen showed higher nut elongation. Artificial pollination turned out to be a good tool to increase yield, but its efficiency is strongly influenced by the pollen used. Full article

The flesh fly, Wohlfahrtia nuba (Wiedemann) (Diptera: Sarcophagidae), is one of the first necrophagous insects to arrive on a cadaver and is vital for understanding decomposition. Environmental factors, especially temperature, influence insect development, which is crucial for estimating postmortem interval (PMI) in forensic entomology. This study explored how seasonal temperature variations affect the survival and development of W. nuba’s immature stages. The W. nuba colony was reared in the laboratory for four seasons from 3 October 2023 to 30 September 2024. The duration of the larval and pupal phases, the percentage of survival and mortality of the larvae and pupae, the larval growth rate, the percentage of emergence, fecundity, the sex ratio, and the pre-larviposition period were among the many life cycle characteristics that were documented during the study. Research indicates that seasonal changes affect development, shortening the growth period as temperatures rise. Flies raised at an average temperature of 38.3 °C grew faster but experienced higher larval mortality and lower survival rates. The average duration of larval and pupal stages was reduced, with an optimal development temperature of 27.9 °C showing higher survival rates, maximum body weight, and fecundity. The largest mortality rate occurred during winter at an average temperature of 18.5 °C, with males and females showing significant pupal elongation. The findings could help forensic entomologists working on legal investigations to ascertain PMI. Full article

This investigation explores the application of supervised machine learning regression approaches to predict various responses, including penetration, bead width, bead height, hardness, ultimate tensile strength, and percentage elongation in autogenous TIG-, A-TIG-, and TIG-welded joints of SS304H, which is considered as an advanced high-temperature resistant material. The machine learning (ML) models were constructed based on the data gathered from 50 experimental runs, considering eight key input variables: gas flow rate, torch angle, filler material, welding pass, flux application, root gap, arc gap, and heat input. A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. Among these, the XGBoost model has demonstrated the highest predictive capability, achieving R² scores of 0.886 for penetration, 0.926 for width, 0.915 for weld bead height, 0.868 for hardness, 0.906 for ultimate tensile strength, and 0.926 for percentage elongation, along with the lowest values of RMSE, MAE, and MSE across all responses. The outcomes establish that machine learning models, particularly XGBoost, can accurately predict welding characteristics, marking a significant advancement in the optimization of TIG welding parameters. Consequently, integrating such predictive models can substantially enhance the precision, reliability, and overall efficiency of welding processes. Full article

This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior. Full article

Background/Objectives: Globally, antibiotic contamination has become an emerging issue in agricultural lands. The presence of antibiotic residues in farmlands, especially through the application of manure fertilizers containing veterinary antibiotics, e.g., tetracycline (TC), can cause severe toxicity, which inhibits crop growth and performance, subsequently threatening human health via consumption of contaminated products. This study was conducted to evaluate the phytotoxicity of TC on Kimchi cabbage (Brassica campestris L.) during seed germination, seedling, and vegetative growth stages, along with its physiological responses and bioaccumulation under TC stress. Methods: The responses of cabbage plants to TC stress were assessed through a germination test and a pot experiment, conducted for three days and six weeks, respectively, under different doses of TC (0, 5, 10, 25, and 50 mg/L). Results: As a result of the germination test, higher TC doses (25 and 50 mg/L) tended to delay seed germination, but all treatments achieved a 100% germination percentage by Day 3 after sowing. Eight days after sowing, the length of shoots and roots of seedlings exhibited a TC dose-dependent decline, specifically under 50 mg TC/L, showing a considerable decrease of 24% and 77%, respectively, compared to control. Similar results were observed in the plants transitioning from the seedling to vegetative stages in the pot experiment. Four and six weeks after sowing, the 50 mg TC/L dose showed the strongest phytotoxicity in cabbage plants with physiological parameters, such as the maximum photosystem II quantum yield (F_v/F_m), pigment content (chlorophyll and carotenoid), biomass, and leaf number, significantly reduced by 26 to 60% compared to control. Interestingly, at lower TC doses (5 and 10 mg/L), a hormesis effect was observed in the phenotype and biomass of the plants. In addition, the degree of TC accumulation in the plants was highly dose-dependent at Week 4 and Week 6, but a temporal decline in TC accumulation was noted between these time points in all TC treatments. This phenomenon might affect the value of the bio-concentration factor (BCF) as an indicator of the plant’s tendency to uptake TC. That is, in Week 6, the dose-dependent reduction in BCF for TC in the plants was likely attributed to a dilution effect caused by plant biomass increase or a degradation mechanism within the plant. Conclusions: Overall, our findings suggest that tetracycline toxicity induces seed germination delay and influences seedling elongation and photosynthetic functions, ultimately impairing crop growth and performance. Also, the antibiotic dynamics related to accumulation and degradation in plants were identified. These results will not only suggest the toxicity threshold of TC for cabbage but also provide insights into effective soil management strategies for food production safety and agroecosystem sustainability in antibiotic-contaminated soils. Full article

In desert plants, outcrossing is frequently limited by pollinator scarcity, leading to a certain percentage of self-fertilization. However, research on the ecological adaptations of self-fertilized seeds remains limited. Gymnocarpos przewalskii Maxim, a Tertiary relict plant in the arid deserts of Northwest China, exhibits pronounced self-pollination. In this study, the population of G. przewalskii from the fifth regiment of Alar City was selected to explore its self-pollination types, self-pollination percentages, and ecological adaptations. We found that artificially cross-pollinated G. przewalskii produced heavier seeds, faster germination, seedlings with greater biomass, and stronger environmental adaptability than self-pollination. However, the frequency of insect visits per flower was less than one. The fruit setting rate of natural pollination was 6.90%, while that of self-pollination was 4.43%, accounting for 64.20% of the natural fruit setting rate. Additionally, G. pzewalskii’s filaments elongated rapidly to make the anthers and stigma at the same height before flowering. These characteristics suggest that G. przewalskii is capable of autonomous self-pollination and is prior selfing. Gymnocarpos przewalskii likely produces a high proportion of the selfing merely to ensure population survival. These findings offer valuable insights into the adaptation of desert plants to the scarcity of pollinators. Full article

The continuous provision of nitrogen (N) to the crop is critical for optimal cotton production; however, the constant and excessive application of synthetic fertilizers causes adverse impacts on soil, plants, animals, and human health. The current study focused on the short-term effects (one-year study) of adding different rates of clinoptilolite zeolite, as part of an integrated nutrient management plan, and different rates of inorganic N fertilizer to improve soil and crop performance of cotton in three locations (ATH, MES, and KAR) in Greece. Each experiment was set up according to a split-plot design with three replications, three main plots (zeolite application at rates of 0, 5, and 7.5 t ha⁻¹), and four sub-plots (N fertilization regimes at rates of 0, 100, 150, and 200 kg N ha⁻¹). The results of this study indicated that increasing rates of the examined factors increased cotton yields (seed cotton yield, lint yield, and lint percentage), with the greatest lint yield recorded under the highest rates of zeolite (7.5 t ha⁻¹: 1808, 1723, and 1847 kg ha⁻¹ in ATH, MES, and KAR, respectively) and N fertilization (200 kg N ha⁻¹: 1804, 1768, and 1911 kg ha⁻¹ in ATH, MES, and KAR, respectively). From the evaluated parameters, most soil parameters (soil organic matter, soil total nitrogen, and total porosity), root and shoot development (root length density, plant height, leaf area index, and dry weight), fiber maturity traits (micronaire, maturity, fiber strength, and elongation), fiber length traits (upper half mean length, uniformity index, and short fiber index), as well as color (reflectance and spinning consistency index) and trash traits (trash area and trash grade), were positively impacted by the increasing rates of the evaluated factors. In conclusion, the results of the present research suggest that increasing zeolite and N fertilization rates to 7.5 t ha⁻¹ and 200 kg N ha⁻¹, respectively, improved soil properties (except mean weight diameter), stimulated crop development, and enhanced cotton and lint yield, as well as improved the fiber maturity, length, and color parameters of cotton grown in clay-loam soils in the Mediterranean region. Full article