Search Results (154)

Urban wetlands are transitional sub-ecosystems, which have an important part in connecting the city sources of heavy metal pollution with freshwater ecosystems, and numerous studies have studied the nature of heavy metal pollution, though only several have investigated the consequences of heavy metals on the health of city dwellers residing in urban wetlands. The Monte Carlo simulation-based method of assessing health risks was employed to calculate the health risks related to the population residing within the study site as one of the measures taken within the framework of the present research to identify the health risks faced by the population when using the sediments of Huixian Wetland Mudong Lake, Guilin City, to evaluate health outcomes. The results showed that Cd and As had the highest geoaccumulation index values and were the most seriously polluted metals. The northeastern and northwestern areas of the lake exhibited a strong level of ecological risk, likely due to their proximity to anthropogenic pollution sources and slower water exchange rates. Non-carcinogenic risk indices (HI) for both adults and children were below 1, with children facing higher risk than adults. For carcinogenic risk, As, Cd, Cr, and Pb posed greater risks to children than adults, with 99.96% of the total carcinogenic risk (TCR) values exceeding the USEPA threshold of 1.00 × 10⁻⁴, indicating an unacceptable risk to children. Sensitivity analysis revealed that the hand–oral intake rate (IRing), As, and Cr were the main factors affecting the human health risk. These findings provide clear guidance for targeted risk control; priority should be given to pollution control of Cd and As, as well as protective measures in high-risk zones, to reduce children’s exposure. The results of this study provide a scientific basis for precise risk control and remediation measures in the region. Full article

With the development of human–computer interaction technology, non-contact interaction based on gaze tracking and facial movements has become a research hotspot. Traditional mouse-and-keyboard methods pose challenges for people with disabilities or limited hand movements, while existing gaze-tracking systems often rely on expensive hardware or lack sufficient accuracy. This paper designs and implements a real-time system using ordinary cameras, achieving natural, efficient interaction via multimodal input combination. The system uses an improved MobileNetV2 backbone to construct GazeTrackNet for gaze estimation. It adopts MediaPipe Face Mesh to detect facial landmarks. Meanwhile, it applies geometric feature analysis, including eye aspect ratio and mouth aspect ratio, to identify actions such as blinking and mouth opening. It adopts a hybrid control strategy that combines gaze jumping and head fine-tuning, using mouth state as the main control switch. Key contributions include a lightweight gaze-tracking algorithm that enables stable and efficient gaze detection on consumer-grade hardware, a multimodal interaction strategy based on facial movement that improves system stability and ease of use, and a complete prototype system that achieves real-time performance on standard laptops. Experimental results show an average gaze average angle error of 3.0°, 97% eye state recognition accuracy, and end-to-end latency below 70 ms. The system can satisfy the requirements of daily desktop interaction under normal indoor lighting, and shows potential for future barrier-free interaction applications after further validation with target users. Existing gaze-tracking methods either suffer from low precision on lightweight devices or bring heavy computational overhead. Common facial recognition approaches also face frequent false trigger interference. Compared with them, our scheme achieves balanced accuracy and real-time performance via an attention-enhanced structure, and the designed dual anti-shake mechanism effectively suppresses misjudgment, delivering a more stable hands-free interaction experience. Full article

SMA bars, particularly those based on NiTi, exhibit superelastic and self-centering properties, providing damage-resistant, self-centering structural systems. However, their natural smoothness and low machinability pose a significant challenge to adequate mechanical anchorage. This paper experimentally measures the efficiency of two feasible wedge-type anchorage systems, wedge-and-barrel (WB) and spring anchor (SA), which are typically used in post-tensioning systems, and assesses their applicability for anchoring smooth-surfaced NiTi SMA bars. A total of 24 testing configurations were examined in this study. A complete monotonic tensile test regime was performed at steady loads with desired strain levels. The findings validate that both wedge-type anchorage systems were able to effectively anchor the SMA bars, although some performance differences were observed. The WB anchorage system showed increased stress capacity, improved load transfer efficiency, and less scatter across repeated tests, which can be attributed to its greater mechanical confinement and frictional interlock, exhibiting an increase of approximately 27% in stress capacity compared to the SA anchorage system. On the other hand, the SA system exhibited good anchorage performance. It showed a slightly lower stress response and greater variation at higher levels of deformation due to the spring’s compression mechanism. The results demonstrate the feasibility of using wedge-type anchorage systems to anchor SMA rebars for seismic applications and provide guidance for future anchorage design. Full article

Italian hand gestures constitute a distinctive and widely recognized form of nonverbal communication, deeply embedded in everyday interaction and cultural identity. Despite their prominence, these gestures are rarely formalized or systematically taught, posing challenges for foreign speakers and visitors seeking to interpret their meaning and pragmatic use. Moreover, their ephemeral and embodied nature complicates traditional preservation and transmission approaches, positioning them within the broader domain of intangible cultural heritage. This paper introduces a machine learning–based framework for recognizing iconic Italian hand gestures, designed to support cultural learning and engagement among foreign speakers and visitors. The approach combines RGB–D sensing with depth-enhanced geometric feature extraction, employing interpretable classification models trained on a purpose-built dataset. The recognition system is integrated into a non-immersive virtual reality application simulating an interactive digital totem conceived for public arrival spaces, providing tutorial content, real-time gesture recognition, and immediate feedback within a playful and accessible learning environment. Three supervised machine learning pipelines were evaluated, and Random Forest achieved the best overall performance. Its integration with an Isolation Forest module was further considered for deployment, achieving a macro-averaged accuracy and F1-score of 0.82 under a 5-fold cross-validation protocol. An experimental user study was conducted with 25 subjects to evaluate the proposed interactive system in terms of usability, user engagement, and learning effectiveness, obtaining favorable results and demonstrating its potential as a practical tool for cultural education and intercultural communication. Full article

With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual–behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. Full article

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article

In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic work environments. The system integrates a 6-Degrees of Freedom (DoF) collaborative robot (UR5e) with a hand-eye RGB-D vision system to achieve robust autonomy. The core technical contribution lies in a vision pipeline utilizing deep learning for object detection and point cloud processing for accurate 6D pose estimation, enabling advanced tasks such as human-aware object handover directly onto the operator’s hand. Crucially, an Automatic Speech Recognition (ASR) is incorporated, providing a Natural Language Understanding (NLU) layer that allows operators to issue real-time commands for task modification, error correction and object selection. Experimental results demonstrate that this multimodal approach offers a streamlined workflow aiming to improve operational flexibility compared to traditional HMIs, while enhancing the perceived naturalness of the collaborative task. The system establishes a framework for highly responsive and intuitive human–robot workspaces, advancing the state of the art in natural interaction for collaborative object manipulation. Full article

Increasing product complexity, shorter development cycles and cross-domain integration demands pose significant challenges for modern race car engineering teams. In Formula Student teams, heterogeneous toolchains, manual data exchange, late system integration, and high personnel turnover hinder efficient collaborative development and lead to repeated knowledge loss. This paper presents an integrated digital-engineering framework combining graph-based design languages (GBDL), model-to-text transformations, natural-language interactions via Large Language Models (LLMs), and Git-based version control to address these issues. By formalizing design knowledge and storing it in a centralized design graph, the framework ensures digital consistency of data and models, supports automated vehicle design variant generation, and enables seamless cross-domain integration. Through case studies of three Formula Student teams, the methodology demonstrates quantifiable reductions in design iteration time, enabling the evaluation of more than 10⁴ suspension variants within days instead of a few dozen manually created variants, while reducing hands-on engineering effort from minutes per variant to a largely unattended optimization process. The results indicate that the approach not only enhances efficiency and collaboration but also preserves design knowledge for long-term knowledge management and reuse. Looking forward, this methodology provides a scalable route toward further engineering automation, systematic variant-driven development, and early-stage design optimization supported by design languages and integrated downstream toolchains. Full article

The rising volume of sludge production poses significant environmental threats. Sludge has a high moisture content (MC), which increases its disposal and transport expenses. On the other hand, sludge has low dewaterability due to its high concentration of soluble organic compounds. To reduce sludge production, understanding and improving preconditioning and mechanical dewatering are crucial for breakthroughs in advanced sludge dewatering. The sludge samples used in this analysis were obtained from the Sarabium municipal wastewater treatment plant, with a moisture content of 97% and a specific filtration resistance (SRF) of 9.15463 × 10¹⁵ m/kg. Sludge dewatering was enhanced by treating the samples chemically with ferric chloride, aluminum sulfate, Moringa olifera, and cationic polyacrylamide CPAM and physically with wood chips, slag, rice husk, and wheat straw. The experiments examined the sludge’s initial characterization (specific resistance to filtration (SRF) and time to filtrate (TTF)). To verify the structural characteristics (density), elemental composition, and the presence of various functional groups, a characterization investigation was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and energy-dispersive X-ray spectroscopy (EDS). The results showed that chemical conditioning with ferric chloride is better than aluminum sulfate and Moringa. Wood chips also provide better results for physical conditioning than rice husk, wheat straw, and slag. The reaction occurred at the carbonyl group, where FTIR showed more activated sites during SEM analysis, as evidenced by the FTIR results. Still, when CPAM was added to conditioned sludge, there was no difference in sludge dewatering performance, and the activated sites remained unchanged. Hence, this research found that mechanical sludge dewatering was improved by conditioning with ferric chloride (pH of 6 and dose of 0.12 g/g of dry solid) and wood chips (dose of 1.5 g/g of dry solid), which reduced sludge volume after dewatering by 82.5% under low pressure, which in turn minimizes transportation, energy, and handling costs. This study supports SDG 3 and SDG 6 by improving sludge dewatering efficiency and promoting sustainable wastewater management using natural wood chips. Full article

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

Heracleum sosnowskyi Manden., or H. sosnowskyi, of the Apiaceae was first cultivated in the USSR in 1947 as a potential fodder plant. Due to the development of cold-resistant cultivars and the characteristics of H. sosnowskyi, it quickly became feral. As a result, H. sosnowskyi began to spread as an aggressive invasive species in the 1970s and 1980s. By the 90s it had become an ecological disaster. As well as forming monocultures and displacing native species, H. sosnowskyi contains furanocoumarins, photosensitizing compounds that increase skin sensitivity to ultraviolet rays and cause severe burns. In addition, furanocoumarins have cytotoxic, genotoxic, mutagenic and estrogenic effects. H. sosnowskyi also contains essential oils, which are particularly active during flowering and can irritate the mucous membranes of the eyes and respiratory tract, as well as cause allergic reactions in the form of bronchospasm in people with asthma and hypersensitivity. When released in high concentrations, these biologically active compounds have an allelopathic effect on native plant species, displacing them and reducing biodiversity. As H. sosnowskyi is not native; the biologically active compounds it secretes have a xenobiotic effect, causing serious damage to the ecosystems it occupies. However, in parallel with these negative properties, furanocoumarins have been found to be effective in the treatment of cancer and skin diseases. Furanocoumarins possess antimicrobial antioxidant osteo- and neuroprotective properties. Essential oils containing octyl acetate, carboxylic acid esters, and terpenes can be used in the pharmaceutical industry as antiseptic and anti-inflammatory agents. Additionally, essential oils can be used as biofumigants and natural herbicides. A comprehensive approach allows H. sosnowskyi to be viewed in two ways. On the one hand, it is an aggressive alien species that causes significant damage to ecosystems and poses a threat to human health. On the other hand, it is a potentially valuable natural resource whose biomass can be used within the principles of the circular economy. It is hoped that the use of H. sosnowskyi for economic interests can be a partial compensation for the problem of its aggressive invasion, which is of anthropogenic origin. Full article

As global climate change intensifies, research on low-carbon practices has become a critical component of sustainable tourism development. The carbon emission profile of ski tourism differs significantly from other tourism sectors. Ski resorts have a mountainous terrain and typically maintain relatively high levels of vegetation, endowing them with inherent advantages for pioneering low-carbon and sustainable tourism practices. However, the substantial energy demands associated with artificial snowmaking systems and advanced infrastructure pose significant challenges to reducing carbon emissions in ski resort operations. This study gathers first-hand data on sustainable tourism development in the Chongli ski resort—the region that hosted the 2022 Winter Olympics—through field investigations and interviews with key industry stakeholders. It develops a comprehensive framework accounting for carbon emissions in ski resorts by integrating input–output analysis with enterprise-level data, focusing on four core operational sectors: catering, skiing, wholesale and retail, and leasing and business services. Furthermore, this study examines the coupling relationship between carbon emissions and operating revenue. Using correlation and regression analyses, this study identifies the key drivers of carbon emissions across these operational departments within the ski tourism sector. The results indicate that carbon emissions from these four sectors in the Chongli ski resort exhibit periodic fluctuations with an overall upward trend year by year. Nevertheless, progress in low-carbon development is evident, suggesting that the resort is on a trajectory toward achieving peak carbon emissions and eventual carbon neutrality. The inclusion of natural endowments, market-scale effects, festival and special events, and capital investment in ski tourism collectively serve as crucial drivers for low-carbon sustainability in Chongli. Based on these findings, this study proposes targeted recommendations to support low-carbon sustainable development, offering scientific insights for similar Winter Olympics host cities. This study integrates top-down input–output analysis with bottom-up enterprise data, taking Chongli, the host city of the Winter Olympics, as a timely case study. It constructs a four-dimensional low-carbon development model based on the identification of key natural, social, and economic driving factors, and strengthens the reliability of the conclusion by relying on first-hand field research and operator interview data. Our study provides an analysis of methodological innovation, framework integrity, and solid empirical evidence that accounts for micro-scale carbon emissions in ski resorts. Full article

Despite the groundbreaking impact of currently approved CAR T-cell therapies, substantial unmet clinical needs remain. This highlights the need for CAR T treatments that are easier to tune, combine, and program with logic rules, in oncology and autoimmunity. Modular CAR T cells use a two-part system: the CAR on the T cell binds an adaptor molecule (AM), and that adaptor binds the tumour-associated antigen (TAA). This design separates recognition of the target antigen and activation of the T cells, resulting in a cellular therapy concept with better control, flexibility, and safety compared to established direct-targeting CAR T-cell systems. The key advantage of the system is the adaptor molecule, often an antibody-based reagent, that targets the TAA. Adaptors can be swapped or combined without re-engineering the T cells, enabling straightforward multiplexing and logic-gated control. The CAR itself is designed to recognise the AM via a unique tag on the adaptor. Only when the CAR, AM, and antigen-positive target cell assemble correctly is T-cell effector function activated, leading to cancer cell lysis. This two-component system has several features that need to be considered when designing a modular CAR: First, the architecture of the CAR, i.e., how the binding domain and the backbone are designed, can influence tonic signalling and activation/exhaustion parameters. Second, the affinity of CAR–AM and AM–TAA will mostly define the engagement kinetics of the system. Third, the valency of the AM has an impact on exhaustion and non-specific activation of CAR T cells. And lastly, the architecture of the AM, especially the size, defines the pharmacokinetics and, consequently, the dosing scheme of the AM. The research conducted on direct-targeting CAR T cells have generated in-depth knowledge of the advantages and disadvantages of the technology in its current form, with remarkable clinical success in relapsed/refractory disease and long-term survival in otherwise difficult-to-treat patient populations. On the other hand, CAR T-cell therapy poses the risk of severe adverse events and antigen loss coupled with antigen-negative relapse which remains the main reason for failed therapies. Addressing these issues in the traditional setting of one CAR targeting one antigen will always be difficult due to the heterogeneous nature of most oncologic diseases, but the flexibility to change target antigens and the modulation of CAR T response by dosing the AM in a modular CAR system might be pivotal to mitigate these hurdles of direct CAR T cells. Since the first conception of modular CARs in 2012, there have been more than 30 constructs published, and some of those have been translated into phase I/II clinical trials with early signs of success, but whether these will progress into a late-stage clinical trial and gain regulatory approval remains to be seen. Full article

Antimicrobial resistance (AMR), mostly resulting from the widespread use of antimicrobials in healthcare, veterinary, and agriculture, poses a significant challenge to global health. Healthcare facilities are hotspots of AMR due to high antibiotic consumption and the presence of highly susceptible populations. Moreover, there may be a dynamic exchange in AMR between healthcare infrastructures, human populations, animals, and the environment. To address these challenges, this review presents a One Health perspective, emphasizing the complex interconnections among many ecosystems. Furthermore, the development and dissemination of AMR in the healthcare environment, via surfaces and hands, have been critically investigated. Some of the neglected aspects that contribute to AMR, such as ventilation and wastewater, have also been addressed. The natural environment plays a crucial role as a reservoir for antimicrobial resistance genes (ARGs). The expected increase in AMR in the coming years will not only pose a challenge to public health but also to food security and environmental health. Hospitals should install advanced systems for treating wastewater to reduce the discharge of antimicrobials. Hospitals should also combine full water, sanitation, and hygiene (WASH) protocols with infection prevention and control (IPC) methods. These efforts are aimed at preventing infections and protecting public health and the environment. Other measures include advancing research to understand transmission pathways, increasing surveillance, reducing contamination in healthcare settings, implementing national plans for stewardship, and globally sharing resources and targets to reduce AMR. Full article

For human–robot gesture-driven control, this paper proposes a deep learning-based approach that employs both static and dynamic gestures to drive and control robots for object-grasping and delivery tasks. The method utilizes two-dimensional Convolutional Neural Networks (2D-CNNs) for static gesture recognition and a hybrid architecture combining three-dimensional Convolutional Neural Networks (3D-CNNs) and Long Short-Term Memory networks (3D-CNN+LSTM) for dynamic gesture recognition. Results on a custom gesture dataset demonstrate validation accuracies of 95.38% for static gestures and 93.18% for dynamic gestures, respectively. Then, in order to control and drive the robot to perform corresponding tasks, hand pose estimation was performed. The MediaPipe machine learning framework was first employed to extract hand feature points. These 2D feature points were then converted into 3D coordinates using a depth camera-based pose estimation method, followed by coordinate system transformation to obtain hand poses relative to the robot’s base coordinate system. Finally, an experimental platform for human–robot gesture-driven interaction was established, deploying both gesture recognition models. Four participants were invited to perform 100 trials each of gesture-driven object-grasping and delivery tasks under three lighting conditions: natural light, low light, and strong light. Experimental results show that the average success rates for completing tasks via static and dynamic gestures are no less than 96.88% and 94.63%, respectively, with task completion times consistently within 20 s. These findings demonstrate that the proposed approach enables robust vision-based robotic control through natural hand gestures, showing great prospects for human–robot collaboration applications. Full article