Search Results (439)

The automated detection of persimmons in orchards is hindered by two primary factors: fruits of the same cultivar possess nearly identical color, causing adjacent instances to be easily merged, and heavy occlusion by leaves and stems hides large portions of the fruit surface. These challenges often co-occur and severely degrade detection performance. To address them jointly, we propose PersimmonDet, a collaborative framework that integrates a dual-path modulation fusion module (MFM) for color–occlusion decoupling, a modulation-guided dynamic upsampling operator (M-DySample) that preserves boundaries under occlusion, a scale-aware lightweight detection head (MB_Head), and a repulsion-enhanced Wise-IoU loss. The key innovation is an implicit coordination mechanism: the loss function generates strong gradients based on difficult samples, and these gradients flow back to train the fusion and upsampling modules, forming a closed optimization loop. Experiments on a self-built persimmon dataset show that PersimmonDet achieves an mAP@0.5 of 96.13% and mAP@0.5:0.95 of 84.42%, outperforming multiple lightweight detectors. The largest gains occur based on heavily occluded and densely clustered fruits. Ablation studies reveal a clear improvement when all four components are combined, confirming their complementary contributions. The same architecture, retrained on a winter jujube dataset without modification, delivers consistent advantages, demonstrating cross-crop transferability. These results indicate that combining problem-aware modulation, boundary-preserving upsampling, and difficulty-focused training is a promising approach for robust fruit detection in orchard environments. Full article

Project-based learning (PBL) is increasingly used in higher education to promote authentic, collaborative, and applied learning experiences. However, limited evidence exists regarding how students with varying levels of quantitative preparedness and ADHD experience PBL in introductory analytics courses. This study examined students’ perceptions of PBL in introductory statistics and data mining courses taught within non-STEM undergraduate programs. Survey data were collected from 414 students and analyzed using exploratory PCA, reliability analysis, descriptive statistics, and two-way multivariate analysis of variance. Subsequent analyses focused on two theoretically derived composite dimensions: Engagement and Value, and Autonomy. Quantitative preparedness was significantly associated with students’ perceptions of PBL, whereas ADHD status alone was not associated with either dimension. Students with lower quantitative preparedness reported higher levels of engagement and perceived value than their more highly prepared peers. An exploratory interaction between quantitative preparedness and ADHD status was observed for the Engagement and Value dimension. The findings suggest that structured project milestones, authentic datasets, collaboration, and instructor guidance may help foster more inclusive analytics learning environments and support meaningful participation among diverse learners. These findings should be interpreted cautiously because they are based on students’ self-reported perceptions within a single institution. Full article

Plant surfaces form a critical biomechanical and ecological interface between plants and insects. Beyond chemical cues and rewards, the physical micro- and nanostructure of stems can decisively shape insect attachment, locomotion, and visitation patterns. This perspective paper synthesizes a body of work centered on studies by Gorb, Gorb, and collaborators (2017–2024), together with the broader literature cited therein, to develop a framework for understanding stem surface-mediated plant–insect interactions. We integrate comparative, experimental, and biomechanical evidence demonstrating that epicuticular waxes, pubescence, epidermal cell geometry, and surface roughness act each and in combination as a mechanical filter for crawling insects, particularly ants. We propose a unifying conceptual model, in which plant stem surfaces function as scale-dependent mechanical barriers that modulate insect access, balancing ecological trade-offs between attraction and exclusion. The paper further highlights ecological and evolutionary implications as well as methodological advances and discusses opportunities for bioinspired applications. Full article

The sex ratio of the rice stem borer, Chilo suppressalis (Walker), is important for assessing reproductive potential and outbreak risk, but automated sex classification in field trap images is hindered by small targets, variable postures, subtle morphological differences and complex backgrounds. An edge–cloud collaborative system was developed for fine-grained sex classification of the rice stem borer. The edge terminal performs image acquisition, target detection, ROI extraction, and foreground enhancement, while the cloud platform conducts sex classification and result management. To improve small-target localization, YOLO-CSNet was constructed by integrating a global attention mechanism and adaptive spatial feature fusion into YOLO11n. Within detection-constrained ROIs, Haar-like features and an AdaBoost discriminator were used to suppress tray textures, shadows and non-target regions. For sex classification, DRS-ViT combines convolutional patch embedding, a discriminative-region soft-weighting module and a KANsformer encoder to represent weak sex-related cues and model global structural relationships. YOLO-CSNet achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 94.36%, 93.19%, 95.32%, and 73.95%, respectively. DRS-ViT achieved accuracy, precision, recall, and F1-score values of 93.93%, 94.68%, 93.29%, and 93.91%, respectively. In a temporally independent field deployment conducted at one Shandong site from May to June 2026, the system achieved an image-upload success rate of 91.7% and an end-to-end accuracy of 87.9%. The field results support the feasibility of the workflow under the tested conditions. Future work will extend field validation across multiple sites and seasons to evaluate system generalizability. Full article

Despite growing curricular emphasis on computational thinking (CT) and engineering design practice (EDP), preparing elementary teacher candidates (ETCs) for integrated, practice-based instruction remains challenging. Grounded in Communities of Practice (CoP) and design-based implementation research (DBIR) frameworks, this study examines how ETCs (pre-survey: n = 23, post-survey: n = 13) develop learning-to-teach practices for integrating coding and robotics into PreK–5 STEAM learning (STEM with Arts integration) to foster these competencies. The study is situated within an interdisciplinary, community-partnered course, Toying with Technology (TwT), where ETCs collaborate in Critical Friends teams to design, facilitate, and reflect on community partners’ need-oriented, empathy-centered STEAM experiences. Utilizing PreK–5 learning technologies and field-based insights from STEM/STEAM professionals, ETCs developed 5E lesson plans integrating science, literacy, mathematics, and robotics, culminating in both an in-person and a virtual STEAM Family Night. Data sources include pre- and post-surveys measuring ETCs’ technology integration perceptions, post-field exit tickets, family feedback, community partner reflections, and course artifacts. Pre-survey results indicated limited prior experience and neutral confidence regarding technology integration. In contrary, post-survey findings demonstrated significant increases in self-reported confidence, conceptual understanding of CT and EDP, and readiness to facilitate empathy-centered, technology-rich learning. Qualitative analysis further revealed growth in collaboration, problem-solving, and pedagogical reasoning, while parent and partner feedback highlighted high levels of engagement and increased STEAM interest. Overall, this study provides initial empirical evidence, within a community-partnered, empathy-oriented course design, showing how community-engaged teacher education, informed by CoP and DBIR frameworks, prepares ETCs to teach CT, EDP, and critical problem-solving through integrated, authentic STEAM integration. Full article

Insect herbivory plays a critical role in shaping plant growth, fitness, and population dynamics, yet its effects across African ecosystems remain poorly understood due to fragmented, methodologically diverse, and geographically biassed research. To address this gap, we conducted an integrated bibliometric and systematic review of peer-reviewed studies on insect herbivory and plant performance in Africa. Searches of Web of Science and Scopus identified 1642 records, of which 312 met relevance criteria and 157 satisfied quality thresholds, including clear herbivory metrics and robust experimental or observational designs. Bibliometric analyses revealed modest but increasing publication rates, with research concentrated in Southern and Eastern Africa and primarily focused on savannas and agroecosystems. Montane forests, wetlands, and arid ecosystems remain underrepresented. Most studies examined Lepidopteran leaf herbivory, while fewer than 15% addressed root, stem, or seed damage, and research collaborations were largely regional. Systematic synthesis showed that insect herbivory generally reduces seedling survival, height, and biomass, particularly at early life stages, although some species display tolerance or compensatory growth depending on environmental conditions and plant traits. Reported effect sizes were highly variable (Hedges’ g = 2.1–3.4), reflecting differences in ecological context, herbivore guilds, and study design. Few studies incorporated multiple life stages, trophic interactions, or climate gradients, limiting inference on long-term population and ecosystem responses. We emphasise the need for harmonised methodologies, trait-based and guild-specific approaches, long-term manipulative studies, and expanded research across underrepresented ecosystems to better inform conservation and restoration under environmental change. Full article

As educators are increasingly expected to integrate technology into meaningful learning experiences, higher education institutions face growing demands to prepare digitally competent STEM teachers. This study examines an interdisciplinary IoT-enhanced project-based training involving pre-service teachers from the Computer Education and Instructional Technology (CEIT) and Science Education programmes, focusing on pre–post changes in computational thinking, collaborative processes, project development experiences, and participants’ perceptions of how the training contributed to their professional development. The study involved 36 pre-service teachers from CEIT and Science Education programmes and employed an embedded mixed-methods design combining computational thinking assessments with qualitative analyses of project and collaboration processes. Results showed statistically significant pre–post increases in decomposition in both groups and in algorithmic thinking among Science Education pre-service teachers. The overall Computational Thinking Skills Test (CTS Test) score did not change significantly in the CEIT group, whereas the corresponding change in the Science Education group was interpreted cautiously because it was at the conventional significance threshold. No statistically significant changes were found in pattern recognition or abstraction in either group, and the subdimension findings were treated as exploratory given the small sample and the absence of correction for multiple testing. Furthermore, active participation in collaborative meetings was associated with stronger teamwork practices, while limited engagement was associated with reported implementation challenges. Because attendance was self-selected, these associations cannot be interpreted causally. Participants perceived their IoT learning experience as making a positive contribution to their professional development and future teaching practice. Taken together, the findings suggest that interdisciplinary IoT-supported project-based learning (PBL) may provide a promising framework for supporting selected dimensions of computational thinking and collaboration in pre-service teacher education, while also offering an accessible instructional model that supports participation among learners with different levels of prior technical expertise. Full article

The transition towards active-learning pedagogies represents an important challenge in STEM (Science, Technology, Engineering, and Mathematics) education, particularly in Agricultural Engineering courses characterised by mathematically demanding content and complex engineering problem solving. Although gamification and Student Response Systems (SRSs) have shown promising educational potential, empirical evidence regarding their combined application in Agricultural Engineering remains limited. This quasi-experimental repeated-measures study evaluated an Educational Innovation Project integrating the Wooclap Student Response System with structured gamification activities—including educational escape rooms, forensic engineering simulations, collaborative challenges, and peer discussion—in two undergraduate Agricultural Engineering courses at the University of Almería. Academic performance was assessed through short-term (one week) and medium-term (one month) knowledge-retention tests, while students’ perceptions were explored using repeated questionnaire administrations throughout the intervention. Quantitative analyses included descriptive statistics, assessment of normality, paired-samples Student’s t-tests, 95% confidence intervals, and Cohen’s d effect sizes. The intervention was associated with statistically significant improvements in short-term and medium-term academic performance, with medium to large effect sizes, while students also reported high levels of engagement, perceived knowledge retention, technological usability, and reduced academic stress. These findings suggest that integrating a Student Response System with structured gamification may support active learning and knowledge retention in technically demanding Agricultural Engineering courses. Nevertheless, because of the quasi-experimental repeated-measures design and the absence of a parallel control group, the findings should be interpreted as evidence of an association between the instructional approach and the observed educational outcomes rather than as proof of causal effectiveness. Further controlled studies involving larger and more diverse engineering cohorts are warranted. Full article

Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by profound biological heterogeneity and highly dynamic treatment trajectories that conventional, static prognostic systems incompletely capture. Cellular therapies, such as chimeric antigen receptor T-cell therapy and hematopoietic stem cell transplantation, offer potentially curative options for relapsed or refractory disease, yet outcomes remain highly variable, and management decisions regarding conditioning intensity, lymphodepletion, immunosuppression, and toxicity surveillance continue to be largely protocol-driven rather than individually adapted. Artificial intelligence (AI) and machine learning (ML) have demonstrated substantial promise in diagnostic support, prognostic stratification, and multimodal data integration across hematologic malignancies, but existing models remain predominantly static and related to pre-treatment in orientation, limiting their utility for real-time clinical guidance. This review summarizes current AI applications in hematologic oncology; critically compares the strengths, limitations, and clinical applicability of major AI model classes, including traditional machine learning, deep learning, multimodal integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue that the central challenge facing the field is no longer whether AI can predict outcomes, but whether it can actively guide real-time therapeutic decisions, and that achieving this transition will require interdisciplinary collaboration, prospective validation, and governance frameworks capable of ensuring interpretability, equity, and clinical trustworthiness. Full article

Human–robot collaboration (HRC) significantly enhances manufacturing flexibility and productivity, yet it faces persistent operational challenges stemming from the fundamental capability asymmetry between humans and robots. Task scheduling is a critical factor impacting the productivity of HRC systems, particularly when human worker fatigue is taken into account. Related task scheduling studies predominantly assume fixed subtask processing times, yet the inherent variability of human workers necessitates non-fixed durations. This paper addresses the HRC task scheduling problem with non-fixed human execution times, explicitly modelling workers’ temporal uncertainty using fuzzy triangular numbers. We proposed a new probability density function for fuzzy triangular numbers in Monte Carlo simulation, which is constructed based on a proportional-to-area principle. Through 5000 random simulations, the resulting distribution outperforms the existing method. Meanwhile, an improved decomposition-based multi-objective evolutionary algorithm is proposed to solve the multi-objective scheduling problem, simultaneously considering productivity and human fatigue. The numerical experiments indicate that the proposed algorithm outperforms the comparison methods. Full article

Hispanic-Serving Institutions (HSIs) are vital to the STEM pathways of Latine/x and other minoritized students, and experiential learning (ExL) is a high-impact practice shown to support postsecondary STEM success. The purpose of this paper is to understand how these intersect by conducting a systematic literature review on servingness in STEM ExL at HSIs, highlighting a growing body of research and policy efforts emphasizing intentionality in serving Latine/x students. Key findings center on the types of ExL HSI STEM students engage in along with structures for serving, including (1) intentional recruitment and engagement; (2) culturally responsive and sustaining STEM practices; (3) institutional and external support and resources; (4) facilitating interactions and engagement with faculty, staff, and industry professionals; (5) and institutional collaborations. We also identified important academic and non-academic outcomes associated with involvement in STEM ExL initiatives. Insights from this review underscore existing structures within HSIs that support more inclusive STEM ExL, while identifying potential opportunities for transforming STEM education within HSI organizations and systems through institutional, industry, and community capacity-building partnerships. Implications for future work on Latine/x servingness and intentionality in HSI STEM ExL are discussed. Full article

UAV infrared small-object detection is challenging because targets occupy few pixels, provide weak thermal contrast, and are easily confused with cluttered backgrounds. Although convolutional and Transformer-based detectors improve local representation and global context modeling, their predominantly spatial processing pipelines do not explicitly prevent fragile target responses from being attenuated during early encoding and multi-scale aggregation. To address this gap, we propose SFCD-Det, a spatial–frequency collaborative detector organized around a progressive preservation–purification–coordination methodology. A Wavelet-Transform Stem preserves low-frequency structures and localized high-frequency details before backbone encoding. A Feature Purification Layer regulates adjacent-scale interactions and suppresses clutter-dominated responses before aggregation, while a Spatial–Frequency Coordinated Feature Pyramid reconstructs multi-scale features using shallow spatial anchors, purified intermediate responses, and deep spatial–frequency priors. Experiments on four benchmarks show that SFCD-Det consistently outperforms the DEIM-N baseline. It achieves 62.6% mAP and 95.1% mAP₅₀ on HIT-UAV, 41.4% and 86.4% on IRSTD-1K, 16.8% and 46.8% on RGBTDronePerson, and 34.1% and 88.0% on USOD, respectively. These results demonstrate that SFCD-Det strengthens weak-target representation in UAV infrared imagery. Its additional gains on USOD suggest that the frequency mechanism may also benefit weak and spatially localized responses in visible imagery under low illumination or shadow. Full article

Breast reconstruction after mastectomy or trauma poses considerable clinical and aesthetic challenges that traditional methods, such as silicone implants and autologous tissue flaps, often insufficiently address. This review analyzes the evolution of biomaterials for breast and soft tissue reconstruction, encompassing conventional ECM-derived scaffolds and acellular dermal matrices, as well as advanced hybrid constructs, injectable smart hydrogels, functionalized biomaterials, and cell-integrated systems incorporating adipose-derived stem cells. Emerging fabrication technologies, including 3D bioprinting, electrospinning, and computational imaging-guided design, are examined for their ability to create patient-specific, vascularized scaffolds with adjustable mechanical and biological characteristics. This review analyzes the scientific basis of soft tissue regeneration, focusing on adipogenesis, angiogenesis, immunomodulation, and extracellular matrix remodeling. The paper further overviews the efficacy of both well-known and new biomaterials, providing a detailed discussion of the translational challenges associated with their clinical application. Despite the substantial recent advancements in the field, comprehensive regenerative breast reconstruction requires ongoing interdisciplinary collaboration and innovation to translate promising preclinical results into safe, effective, and accessible clinical solutions for patients. Full article

Industrial chili peppers are specialized varieties primarily used for the extraction of capsaicinoids and paprika red. Their post-harvest processing level directly affects product quality and industrial economic benefits. Most existing studies have focused on a single unit operation or on edible chili peppers, and a systematic review of the entire post-harvest processing chain for industrial chili peppers is still lacking. Taking the standardized post-harvest processing workflow of industrial chili peppers as its core theme, this paper systematically reviews the current research approaches and application status of industrial chili pepper post-harvest processing technologies across six core unit operations, namely cleaning and impurity removal, grading and sorting, drying, stem and seed removal, crushing and grinding, and extraction of bioactive compounds. The analysis indicates that the field currently faces four common challenges: the lack of standardized processing parameters for classified processing, relatively low drying energy efficiency, insufficient online sensing and intelligent collaborative control, and a scarcity of industrial-scale validation for emerging technologies. This paper further constructs a technical route and technology evaluation framework for the entire post-harvest processing chain of industrial chili peppers, clarifies the applicable boundaries and scale suitability of different processing technologies, and provides a theoretical basis for industrial technological upgrading and process selection. Full article

Informal science, technology, engineering, and mathematics (STEM) learning settings can provide early and accessible opportunities for students who have been historically underrepresented in computing to engage with cybersecurity concepts and emerging technologies. This one-group pretest-posttest mixed-methods study examined a week-long, GenAI-integrated informal cybersecurity summer camp for 33 underrepresented and underserved racial and ethnic minority (UUREM) middle school girls. The study investigated pre-post patterns in perceived cybersecurity knowledge, domain-specific self-efficacy, interest, utility value, career aspirations, and selected AI-literacy practices. Quantitative findings showed the strongest support for increases in perceived cybersecurity knowledge and cyber threat identification self-efficacy, both of which remained significant after Holm adjustment for multiple comparisons. Networking and web management self-efficacy showed preliminary unadjusted increases, whereas overall cybersecurity interest, enjoyment and intent to pursue, utility value, career aspirations, and the remaining self-efficacy domains did not show statistically significant differences. Qualitative observations documented activity-specific engagement, collaborative discussion of cybersecurity ideas, and mentor-mediated GenAI practices such as prompt development, output comparison, and script revision. These findings suggest that informal GenAI-integrated cybersecurity programs may support selected aspects of perceived learning and cybersecurity self-efficacy, while highlighting the need for more rigorous designs, validated AI-literacy measures, and longer-term follow-up. Full article