Search Results (104)

Fruit harvesting is labor-intensive and increasingly challenged by the shortage of agricultural labor. To address viewpoint planning under occlusion, this paper proposes a mapless picking guidance framework that directly predicts the next viewing direction and estimates fruit occlusion without relying on pre-built maps or candidate-viewpoint sampling. Unlike conventional active vision methods that enumerate and evaluate multiple candidate viewpoints, the proposed method generates feasible viewpoints by jointly leveraging occlusion estimation and global picking direction supervision, thereby reducing computational cost and alleviating local optimum bias. An adaptive approach strategy is further introduced to balance viewpoint exploration and target approach during planning. Simulation results show that the proposed method achieves average success rates of 80.46% on the in-distribution test set and 77.58% on the unseen-fruit test set, with corresponding occlusion reductions of 80.56% and 79.16%, respectively. These results demonstrate the effectiveness of the proposed framework for occlusion-aware fruit viewpoint planning in unstructured orchard environments. Full article

Recent advancements in Vision-Language Models (VLMs) have enabled robotic systems to leverage model-based understanding and reasoning over visual and linguistic inputs, offering a promising approach for interpreting user intent in human–robot interaction (HRI). In particular, deictic expressions commonly used in object handovers, such as “take this” and “give me that”, cannot be fully interpreted through language alone and require a comprehensive understanding of the speaker’s perspective and the environment. This study proposes a prompt-driven vision-language framework for deictic interpretation in human–robot handover. The system integrates a pre-trained VLM with a hierarchical prompt that decomposes reasoning into intent classification, spatio-temporal grounding, and output self-validation, enabling accurate identification of target objects and goal locations without model fine-tuning. Experimental results demonstrate 100% command interpretation accuracy across multiple interaction scenarios, including pick-and-place tasks, robot-to-human and human-to-robot handovers, and temporal deictic commands. Notably, the system operates under a prompt–command language mismatch, accurately interpreting Korean commands while being guided by English-based prompts. Analysis across progressive system configurations further demonstrates that structured prompting plays a critical role in reasoning performance. These results highlight the effectiveness of a prompt-driven approach for deictic interpretation and spatio-temporal grounding, providing a practical training-free framework for HRI. Full article

This paper presents a Python-based v3.12 framework for robotic object sorting in a virtual workcell, combining learning-from-demonstration with a comparative evaluation of classical machine learning classifiers. A user provides a minimal demonstration (e.g., one cube and one cylinder placed into two bins) from which a dynamic type-to-bin rule is inferred. In this study, learning-from-demonstration is implemented at the level of rule acquisition from minimal task examples rather than at the level of trajectory imitation or low-level motion teaching. This rule is used to relabel a larger dataset of pre-generated object positions, enabling training with a selectable number of file-based samples (2–1600) optionally augmented with manual samples. Five classifiers—decision tree, k-nearest neighbors, logistic regression, naive Bayes, and linear SVM—were trained and then used to drive autonomous pick-and-place execution while logging replication time and correctness (correct/incorrect moves and accuracy). Because the task reaches accuracy saturation under a deterministic rule, an additional offline inference benchmark was included to compare prediction throughput using 10,000 probes with repeated timing (median over 50 runs or mean ± standard deviation over 30 runs). To complement this nominal evaluation, the framework also included a perturbation-aware robustness protocol based on controlled positional perturbation, systematic bias, controlled shape corruption, repeated perturbation voting, and stability-aware scoring. This additional layer makes it possible to examine classifier behavior under controlled uncertainty, especially in reduced-data settings, without changing the compact simulator-based nature of the workflow. Results indicate identical sorting accuracy across models, while inference-time differences remain measurable, highlighting deployment-oriented trade-offs and confirming that end-to-end cycle time is dominated by robot motion rather than model computation. Full article

The Beikang Basin is a large petroliferous sedimentary basin in the southern South China Sea, and is regarded as an important frontier area for deepwater hydrocarbon exploration in the Nansha region. Previous exploration by foreign oil companies has demonstrated its significant hydrocarbon potential. However, no wells have yet been drilled by China in the Beikang Basin. In this underexplored, well-free setting, sandstone reservoir prediction is hindered by several key challenges, including low velocity-picking accuracy, difficulty in constructing reliable low-frequency models, and limited inversion accuracy. These limitations collectively reduce the reliability of reservoir identification and thus increase exploration risk. To address the challenge of accurately predicting sandstone reservoirs in underexplored areas without well control, this study proposes a reservoir prediction workflow that integrates horizon-controlled gridded tomographic velocity modeling with seismic multiple integration. The workflow includes (1) pre-stack gather optimization, (2) tomographic velocity inversion to construct a precise velocity field, (3) multiple integration to supplement missing medium- to low-frequency information, and (4) pre-stack amplitude-versus-offset (AVO) simultaneous inversion to derive P-wave and S-wave impedances. Combined with petrophysical analysis from adjacent areas to establish a lithology interpretation framework, this integrated approach enables quantitative prediction of sandstone reservoirs in the Beikang Basin. Application to the study area demonstrates that the predicted sandstone thickness distribution is consistent with the regional geological conditions and is supported by drilling results from surrounding areas. The proposed method, therefore, provides a reliable technical approach for reservoir evaluation in the Beikang Basin, and has practical value for sandstone reservoir prediction in other underexplored frontier basins with limited or no well control. Full article

Objective: To evaluate changes in emotion regulation, skin-picking disorder (SPD) severity, and repetitive thoughts and behaviors in adolescents with attention-deficit/hyperactivity disorder (ADHD) and comorbid SPD during methylphenidate treatment, and to examine the association between emotion regulation and SPD severity. Materials and Methods: This naturalistic follow-up study included 26 adolescents aged 11–17 years with ADHD and comorbid SPD. Participants received methylphenidate and were reassessed after three months. Emotion regulation, SPD severity, and repetitive thoughts and behaviors were assessed at baseline and follow-up using the Difficulties in Emotion Regulation Scale (DERS), Skin Picking Scale–Revised (SPS-R), and Repetitive Thoughts and Behaviors Scale–Child Form (RTBS-CF). Pre–post differences were analyzed using paired-sample tests, and associations were examined using correlation and linear regression analyses. Results: Significant reductions were observed in total DERS scores (p < 0.001, Cohen’s d = 1.35) and all subscales except non-acceptance (p = 0.686, Cohen’s d = 0.08). SPS-R and RTBS-CF scores decreased significantly (both p < 0.001, Cohen’s d = 1.79 and 0.91, respectively). Changes in DERS scores were moderately correlated with changes in SPS-R scores (r = 0.554, p = 0.003). Changes in emotion regulation were significantly associated with changes in SPD severity, accounting for approximately 31% of the variance in this sample. Conclusions: Methylphenidate treatment was associated with significant improvements in emotion regulation and concurrent reductions in skin-picking severity in adolescents with ADHD and comorbid SPD. Given the single-arm, pre–post naturalistic design, these findings should be interpreted as associative and exploratory rather than causal. Full article

Mechanical harvesting damage is a critical factor constraining potato quality and storage performance. Field curing is a commonly employed pre-treatment prior to mechanical picking of potatoes, which promotes skin suberization and reduces mechanical damage; however, the determination of optimal curing duration lacks a theoretical basis. This study investigated ‘Xisen No. 6’ potatoes at harvest maturity. Curing was performed by field sun-drying (open-air exposure) immediately after mechanical excavation, with five duration gradients (0, 1, 2, 3, and 4 h) established under the recorded meteorological conditions. Twenty-two physical–mechanical and damage parameters were measured, and principal component analysis (PCA) was employed for comprehensive evaluation. The results demonstrated that curing induced a transformation of tubers from “soft-elastic bodies” to “hard-brittle bodies”. This study first revealed the contradictory evolution pattern between skin abrasion damage and tissue impact damage, which exhibited a strong negative correlation (r = −0.89, p < 0.01). PCA indicated that a 3 h curing duration could effectively balance the control of both damage types. These findings provide a quantitative solution to the dilemma of reducing skin damage while controlling impact damage during mechanical potato harvesting, offering significant guidance for optimizing harvesting process parameters and reducing postharvest losses. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

Clinical diagnostic laboratories continue to face growing pressure from rising test volumes, increasingly complex testing menus, significant workforce shortages, and expectations for faster turnaround times at sustainable cost. Total laboratory automation (TLA) has become a central strategy for improving efficiency in high-volume laboratories, where integrated systems from Abbott, Roche, Siemens Healthineers, and Beckman Coulter have demonstrated substantial reductions in turnaround time, error rates, and labor requirements. Evidence across multiple health systems shows that TLA improves performance and stabilizes laboratory operations even during workload peaks. Despite these gains, large segments of pre-analytical and post-analytical workflows remain manual, especially tasks related to specimen transportation, bench-level manipulation, instrument tending, and troubleshooting. Recent progress in collaborative robotics (cobots), autonomous mobile robots (AMRs), and hospital service robots demonstrates that these technologies can complement TLA by addressing not only the logistical and dexterous tasks that fixed automation lines cannot reach but also enabling robots that can work safely right alongside humans in a shared space. Cobots have shown sub-millimeter precision in colony picking and other fine-motor tasks, though typically at lower throughputs than dedicated track modules, and AMRs have demonstrated reliable transport of pathology carts and medical supplies through large clinical environments. Meanwhile, humanoid-capable mobile manipulators, like Moxi from Diligent Robotics, deployed in hospitals are already completing hundreds of thousands of supply deliveries, indicating real-world significance. Here, we integrate technical, regulatory, operational, and business perspectives on TLA, collaborative robotics, and mobile platforms. We discuss real-world efficiency gains, regulatory expectations under the CLIA and United States FDA, and the emerging case for hybrid automation ecosystems that combine TLA islands, cobotic workcells, AMRs, and AI-enabled orchestration. We argue that the next decade of laboratory automation will move beyond monolithic tracks with robots toward flexible, modular robotic systems designed to operate safely together with humans and to augment the increasingly strained laboratory workforce. This not only allows clinical staff to dedicate more time to patient care but also ensures greater reliability and scalability for essential services throughout demanding hospital environments. Full article

The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa–Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems. Full article

This study examines the logistical challenges arising in omni-channel retailing, where the interaction between traditional stores and online channels requires flexible and efficient transportation planning. In particular, the growth of Buy-Online-and-Pick-up-in-Store (BOPS) services has intensified the need to manage both forward deliveries and customer returns, the latter being a costly component of reverse logistics. To address these challenges, this study introduces the Shared Capacity Vehicle Routing Problem with Simultaneous Pickup and Delivery (SCVRP-SPD), which minimizes total operational cost by considering both transportation costs and the additional transfer costs incurred when reallocating store visits to more efficient delivery paths. In the SCVRP-SPD, stores are designed to serve a dual role as both pickup and return points, and a shared-capacity mechanism is incorporated to utilize leftover capacity in pre-planned trips, improving efficiency while reducing overall logistics cost. A mixed-integer programming model is developed for the problem, and solutions are obtained using GUROBI (version 11.0) and a newly designed Modified Differential Evolution (MDE) algorithm. Numerical experiments are conducted to evaluate the performance of the proposed MDE algorithm and to generate managerial insights, showing that the SCVRP-SPD is a promising strategy for omni-channel retailers seeking to reduce transportation costs, streamline reverse logistics, and better utilize resources. Full article

Hydraulic pre-fracturing is a rock-weakening technique applied in hard-rock excavation. To investigate the effects of hydraulic pre-fracturing on crack propagation in excavation roadwalls and on the cutting loads experienced by excavation machine picks when cutting precracked rock, a two-way fluid–solid couplinproach (CFD–DEM) was employed to simulate the three-dimensional crack propagation process of a rock face under hydraulic fracturing. The results indicate that crack propagation under hydraulic fracturing evolves through four stages: (1) initiation of the primary crack; (2) further development of the primary crack, accompanied by the emergence of fine subsidiary cracks; (3) retardation of the primary crack growth, concurrent with propagation of secondary cracks; and (4) further expansion of secondary cracks. The influences of borehole aperture and injection pressure on crack propagation were analyzed; within the investigated ranges, increasing either aperture or injection pressure produced a nonlinear increase in crack development. When the hydraulic-fracture borehole diameter increased from 85 mm to 100 mm, the number of broken bonds increased by 56.2%; when the injection pressure increased from 25 MPa to 40 MPa, the number of broken bonds increased by 153.9%. The cutting force experienced by picks when cutting precracked rock decreased by 9.05% compared with cutting intact (non-precracked) rock; after precracking, the mean forces in the Z and Y directions decreased by 11.46% and 7.20%, respectively, whereas the mean force in the X direction increased by 5.49%. The findings provide reference data for the practical implementation of hydraulic pre-fracturing in hard-rock excavation. Full article

In this paper we will be concerned with zeta-symmetry—the functional equation for the (Riemann) zeta-function (equivalents to which are called modular relations)—and reveal the reason why so many results are intrinsic to PFE (Partial Fraction Expansion) for the cotangent function. The hidden reason is that the cotangent function (as a function in the upper half-plane, say) is the polylogarithm function of order 0 (with complex exponential argument), and therefore it shares properties intrinsic to the Lerch zeta-function of order 0. Here we view the Lerch zeta-function defined in the unit circle as a zeta-function in a wider sense, as a function defined in the upper and lower half-planes. As evidence, we give a plausibly most natural proof of Ramanujan’s formula, including the eta transformation formula as a consequence of the modular relation via the cotangent function, speculating the reason why Ramanujan had been led to such a formula. Other evidence includes the pre-Poisson summation formula as the pick-up principle (which in turn is a generalization of the argument principle). Full article

Established hairpin stators for electric traction motors are made up of a large number of so-called hairpins. To produce these stators, the individual hairpins must first be pre-assembled into an auxiliary device in order to achieve the desired winding scheme. The resulting hairpin basket must then be picked up and transported to the lamination stack. Automated solutions for both processes are characterized by a high degree of complexity and low flexibility. Manual assembly, however, is prone to errors. The new approach presented in this paper is therefore based on the collaborative assembly of the hairpins and a flexible hairpin basket gripper. A cobot hands the hairpins in the correct sequence to the operator. The correct positioning of the hairpins in the auxiliary device is ensured by the use of a monitor located under it. The creation of the correct assembly sequence is partly automated by a collision detection program. In addition, a new and flexible hairpin basket gripping concept is presented. Tests show that the cycle times of both new processes are slow due to hardware limitations. This restricts their use to specific applications, such as complex winding patterns or very small quantities. Full article

Herein we report the very first experiments which were conducted in an attempt to demonstrate the ability of low-field (LF), compact benchtop NMR spectrometers to provide spectral profiles of whole human biofluids, which took place in September–November 2014, and this paper represents a 10-year (decennial) anniversary of this work. LF ¹H NMR analysis was performed on ²H₂O-reconstituted lyophilizates of urine samples (pH 7.00) collected from untreated Niemann-Pick type C1 (NPC1) disease patients and their heterozygous carrier controls (n = 3 in each case). ¹H NMR spectra were acquired on a 60 MHz Oxford Instruments Pulsar compact benchtop spectrometer with spectral filter widths of 5000 Hz, using 1000–1600 scans, and relaxation delays of 15 or 30 s. Further, 400 MHz spectra were also obtained on these samples. Following parameter optimisation, the benchtop system generated reasonable quality urinary ¹H NMR profiles containing ca. 30 signals. Benchtop ¹H NMR analysis confirmed the abnormal urinary metabolic signature of NPC1 disease, and also revealed a gastric permeability disorder in one patient (detection of upregulated urinary sucrose, verified by 400 MHz NMR analysis). Early LF NMR experiments also demonstrated that glucose was trackable in control urine samples pre-spiked with this metabolite. This paper continues with further developments made on LF NMR-based metabolomics technologies, which are systematically discussed for related investigations conducted since 2014. In conclusion, such ‘first-time’ bioanalytical information regarding spectral quality served to pave the way forward for benchtop NMR-based metabolomics investigations of biofluids, which could provide invaluable disease-engendered ‘snapshots’ of disturbances to metabolic pathways and activities, along with those of any co-linked or unlinked comorbidities. Full article

In vitro maturation (IVM) of oocytes retrieved from ovum pick-up (OPU) or ovarian tissue (OT) is a standard approach for patients with specific conditions where prior hormonal stimulation is contraindicated. However, the developmental competence of oocytes matured in vitro is still inferior to that of oocytes matured in vivo. Capacitation IVM (CAPA-IVM) includes an extra step of pre-maturation culture (PMC) with c-type natriuretic peptide (CNP) as a meiotic arrestor to better synchronize cytoplasmic and nuclear maturity in oocytes by allowing the cytoplasm additional time to acquire essential components critical for optimal competency. This study aims to evaluate the effect of CAPA-IVM on equine oocyte quality and developmental competence. Immature cumulus–oocyte complexes (COCs) were retrieved from slaughterhouse ovaries and matured in vitro either in CAPA-IVM (short 6 h, long 24 h pre-maturation) or standard IVM. Mature oocytes from each group were analyzed for calcium-releasing potential (n = 52) and single-oocyte proteomics (n = 44), and embryo development (n = 229) was assessed after fertilization with piezo-drilled intracytoplasmic sperm injection (ICSI). Genetic analysis of developed blastocysts (n = 41) was performed to detect chromosomal aberrations. Our findings demonstrate that CAPA-IVM of equine COCs yields significantly higher maturation rates than controls. Moreover, short CAPA-IVM with six hours pre-maturation culture showed substantially higher embryo development potential than the control group (20/69 vs. 9/63, respectively). Genetic analysis revealed a high euploidy rate in equine blastocysts regardless of the maturation conditions. Live calcium imaging of the fertilized oocytes demonstrated that the majority of oocytes displayed non-continuous calcium oscillation patterns, irrespective of maturation conditions. Single-oocyte proteomics reveals a comparable proteomic landscape between mature oocytes subjected to short CAPA-IVM and standard IVM. However, we identified four enriched gene sets with positive enrichment scores after short CAPA-IVM, related to cytoskeleton regulation, ribosomal function, and cytosolic components. Our findings indicate that CAPA-IVM holds the potential to improve oocyte quality and competence in horses. However, further fine-tuning of culture conditions would benefit the effective use of these IVM systems. Moreover, given that the mare serves as an excellent model for human reproduction, the molecular trends identified in this study could provide valuable insights for advancing human artificial reproductive technologies. Full article