Search Results (42)

Cabbage (Brassica oleracea Linnaeus) is an important vegetable crop for food security and income generation for farmers in the Democratic Republic of Congo (DRC). However, production is severely undermined by a complex of insect pests. This study investigates farmers’ knowledge, perception, and pest management practices in key cabbage-growing areas surrounding Goma city in Eastern DRC. A total of 430 farmers were interviewed using a structured survey administered via the KoboToolbox platform. The diamondback moth (Plutella xylostella Linnaeus, 1758) and the cabbage aphid (Brevicoryne brassicae Linnaeus, 1758) were identified as the main pests, with peak incidences reported during the dry mid-season. Pest damages are most frequently observed at the post-transplanting and heading stages of cabbage. Although chemical control was the dominant strategy (69.4%), concerns arise due to the widespread use of moderately to highly hazardous insecticides, including pyrethroid, organophosphorus, and avermectin-based formulations. The insufficient use of personal protective equipment (PPE) and limited training on safe pesticide handling remain further challenges. While indigenous practices, such as crop rotation, handpicking of insects, and the use of botanical extracts, are employed to a lesser extent, awareness and implementation of biological control are almost nonexistent. The findings underscore the need to promote integrated pest management (IPM) approaches based on agroecological principles, including the safe use of (bio-)pesticides, training programs, and stakeholder engagement to enhance sustainable cabbage production. Full article

Background: Malnutrition is a serious health concern among older adults in residential aged care (RAC), and timely identification is critical for effective intervention. Recent advancements in transformer-based large language models (LLMs), such as RoBERTa, provide context-aware embeddings that improve predictive performance in clinical tasks. Fine-tuning these models on domain-specific corpora, like nursing progress notes, can further enhance their applicability in healthcare. Methodology: We developed a RAC domain-specific LLM by training RoBERTa on 500,000 nursing progress notes from RAC electronic health records (EHRs). The model’s embeddings were used for two downstream tasks: malnutrition note identification and malnutrition prediction. Long sequences were truncated and processed in segments of up to 1536 tokens to fit RoBERTa’s 512-token input limit. Performance was compared against Bag of Words, GloVe, baseline RoBERTa, BlueBERT, ClinicalBERT, BioClinicalBERT, and PubMed models. Results: Using 5-fold cross-validation, the RAC domain-specific LLM outperformed other models. For malnutrition note identification, it achieved an F1-score of 0.966, and for malnutrition prediction, it achieved an F1-score of 0.687. Conclusions: This approach demonstrates the feasibility of developing specialised LLMs for identifying and predicting malnutrition among older adults in RAC. Future work includes further optimisation of prediction performance and integration with clinical workflows to support early intervention. Full article

In recent decades, the River Ganges in India has been heavily contaminated with domestic waste and industrial toxins because of cultural activities, a lack of community awareness, an absence of sewage disposal facilities, and rapid population growth. Previous studies have focused separately on either the physical or social factors associated with River Ganges pollution but have not combined these elements in a single study. To fill this research gap, our study assesses the bio-physical and social vulnerability of the River Ganges by using a holistic approach. The following four sampling stations were selected: Rishikesh, Haridwar, Kanpur, and Varanasi. These locations were chosen to test the water quality in bio-physical aspects and to assess the social perceptions of river vulnerability among the residents and visitors. Perceptions of river water quality and likely sources of pollution were gathered via the distribution of over 1000 questionnaires. Data collection took place in the winter and summer of 2022 and 2023. The results showed that river water quality is not suitable for drinking purposes at any of the four cities without conventional treatment, and that the river is unsuitable for bathing at all locations, except upstream of Rishikesh. Nearly 50% of those questioned agreed that the river is polluted, whilst 74% agreed that pollution has increased in recent decades, particularly in the last 10 years. These compelling results are critical for policymakers and decision makers. They highlight the urgent need for novel strategies that address Ganges pollution while fostering community health education and environmental management. By dispelling myths surrounding river quality, this study strengthens the ongoing efforts to restore the Ganges, ensuring that it remains a vital lifeline for present and future generations. Full article

South Africa’s bioeconomy strategy identifies bio-innovation as a key driver of economic growth and social development, with plant breeding playing a central role in improving food security through the development of high-yielding, resilient, and high-quality crops. However, consumer perceptions of recent advances, particularly new breeding techniques (NBTs), remain underexplored. This study examines South African consumer attitudes towards plant breeding innovations, using a mixed-methods approach. The initial focus group interviews informed the development of a structured quantitative survey examining familiarity, perceptions, and acceptance of plant breeding technologies. Consumer awareness of plant breeding principles was found to be limited, with 67–68% of respondents unfamiliar with both conventional and modern plant breeding procedures. Despite this information gap, consumers expressed conditional support for modern breeding techniques, especially when associated with actual benefits like increased nutritional value, environmental sustainability, and crop resilience. When favourable effects were outlined, support for general investment in modern breeding practices climbed from 45% to 74%. Consumer purchase decisions emphasised price, product quality, and convenience over manufacturing techniques, with sustainability ranked last among the assessed factors. Trust in the sources of food safety information varied greatly, with medical experts and scientists being ranked highly, while government sources were viewed more sceptically. The results further suggest that targeted education could improve customer confidence, as there is a significant positive association (R² = 0.938) between familiarity and acceptance. These findings emphasise the significance of open communication strategies and focused consumer education in increasing the adoption of plant breeding breakthroughs. The study offers useful insights for policymakers, researchers, and industry stakeholders working on engagement strategies to facilitate the ethical growth and application of agricultural biotechnology in support of food security and quality in South Africa. This study contributes to a better understanding of South African consumers’ perceptions of plant breeding innovations and food safety. The research findings offer valuable insights for policymakers, researchers, and industry stakeholders in developing effective engagement and communication strategies that address consumer concerns and promote the adoption of products derived from diverse plant breeding technologies. Full article

Virtual machine (VM) placement in cloud datacenters is a complex multi-objective challenge involving trade-offs among energy efficiency, carbon emissions, and network performance. This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. The approach integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation. The experimental results show that NCRA-DP-ACO reduces power consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance. These results indicate the algorithm’s potential to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios. Full article

Despite remarkable advances in neural language models, a substantial gap remains in precisely interpreting the complex semantics of Electronic Medical Records (EMR). We propose Contrastive Representations Pre-Training (CRPT) to address this gap, replacing the conventional Next Sentence Prediction task’s cross-entropy loss with contrastive loss and incorporating whole-word masking to capture multi-token domain-specific terms better. We also introduce a carefully designed negative sampling strategy that balances intra-document and cross-document sentences, enhancing the model’s discriminative power. Implemented atop a BERT-based architecture and evaluated on the Biomedical Language Understanding Evaluation (BLUE) benchmark, our Discharge Summary CRPT model achieves significant performance gains, including a natural language inference precision of 0.825 and a sentence similarity score of 0.775. We further extend our approach through Bio+Discharge Summary CRPT, combining biomedical and clinical corpora to boost downstream performance across tasks. Our framework demonstrates robust interpretive capacity in clinical texts by emphasizing sentence-level semantics and domain-aware masking. These findings underscore CRPT’s potential for advancing semantic accuracy in healthcare applications and open new avenues for integrating larger negative sample sets, domain-specific masking techniques, and multi-task learning paradigms. Full article

Clinical education, traditionally linked to university training in health care, has found a new declination at the Fondazione Policlinico Universitario Campus Bio-Medico (FPUCBM) through a free public service aimed at patients, family members, and caregivers. This innovative approach aims to improve health self-management, promote empowerment, and foster the active involvement of patients in their own care pathway. Based on high-quality and safety certified standards (by the Joint Commission International), FPUCBM has launched structured initiatives such as “education box” events to provide clear and accessible information, addressing patients’ educational and emotional needs. The “health education service” integrates several activities, including single-topic educational events, the creation of information materials (brochures and video tutorials), and collaboration with patient associations. Since its launch in 2023, the service has reached more than 400 participants in 22 events, covering topics such as chronic disease management and prevention and the proper use of home devices. In total, 95 information brochures and 9 video tutorials have been produced to expand the available resources. Benefits include improved health awareness, increased confidence in care pathways, and a positive impact on the hospital’s reputation. In conclusion, it represents a replicable model of person-centered health care that combines human care and educational support to promote more effective and informed disease management while improving the overall patient experience. Full article

Data generated by Internet of Things devices enable the design of new business models and services, improving user experience and satisfaction. This data also serve as an essential information source for many fields, including disaster management, bio-surveillance, smart cities, and smart health. However, personal data are also collected in this context, introducing new challenges concerning data privacy protection, such as profiling, localization and tracking, linkage, and identification. This dilemma is further complicated by the “privacy paradox”, where users compromise privacy for service convenience. Hence, this paper reviews the literature on data privacy in the IoT, particularly emphasizing Personal Data Store (PDS)-based approaches as a promising class of user-centric solutions. PDS represents a user-centric approach to decentralizing data management, enhancing privacy by granting individuals control over their data. Addressing privacy solutions involves a triad of user privacy awareness, technology support, and ways to regulate data processing. Our discussion aims to advance the understanding of IoT privacy issues while emphasizing the potential of PDS to balance privacy protection and service delivery. Full article

Background/Objectives: Glaucoma is the second-leading cause of irreversible blindness globally. Retinal images such as color fundus photography have been widely used to detect glaucoma. However, little is known about the effectiveness of using raw clinical notes generated by glaucoma specialists in detecting glaucoma. This study aims to investigate the capability of deep learning approaches to detect glaucoma from clinical notes based on a real-world dataset including 10,000 patients. Different popular models are explored to predict the binary glaucomatous status defined from a comprehensive vision function assessment. Methods: We compared multiple deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and transformer-based models BERT and BioBERT. LSTM exploits temporal feature dependencies within the clinical notes, while CNNs focus on extracting local textual features, and transformer-based models leverage self-attention to capture rich contextual information and feature correlations. We also investigated the group disparities of deep learning for glaucoma detection in various demographic groups. Results: The experimental results indicate that the CNN model achieved an Overall AUC of 0.80, slightly outperforming LSTM by 0.01. Both models showed disparities and biases in performance across different racial groups. However, the CNN showed reduced group disparities compared to LSTM across Asian, Black, and White groups, meaning it has the advantage of achieving more equitable outcomes. Conclusions: This study demonstrates the potential of deep learning models to detect glaucoma from clinical notes and highlights the need for fairness-aware modeling to address health disparities Full article

Teleoperation enables robots to perform tasks in dangerous or hard-to-reach environments on behalf of humans, but most methods lack operator immersion and compliance during grasping. To significantly enhance the operator’s sense of immersion and achieve more compliant and adaptive grasping of objects, we introduce a novel teleoperation method for dexterous robotic hands. This method integrates finger-to-finger force and vibrotactile feedback based on the Fuzzy Logic-Dynamic Compliant Primitives (FL-DCP) controller. It employs fuzzy logic theory to identify the stiffness of the object being grasped, facilitating more effective manipulation during teleoperated tasks. Utilizing Dynamic Compliant Primitives, the robotic hand implements adaptive impedance control in torque mode based on stiffness identification. Then the immersive bilateral teleoperation system integrates finger-to-finger force and vibrotactile feedback, with real-time force information from the robotic hand continuously transmitted back to the operator to enhance situational awareness and operational judgment. This bidirectional feedback loop increases the success rate of teleoperation and reduces operator fatigue, improving overall performance. Experimental results show that this bio-inspired method outperforms existing approaches in compliance and adaptability during teleoperation grasping tasks. This method mirrors how human naturally modulate muscle stiffness when interacting with different objects, integrating human-like decision-making and precise robotic control to advance teleoperated systems and pave the way for broader applications in remote environments. Full article

Background: The organic sector is often suggested as a lever with a potential for contributing to the three dimensions of sustainability: social, environmental, and economic. This study aims to investigate selected organic initiatives and organic food sectors in different locations, such as capital cities, rural areas, and the bio-district in SysOrg project consortium, in the Warsaw municipality in Poland, North Hessia region in Germany, Cilento bio-district in Italy, Kenitra province in Morocco, and Copenhagen municipality in Denmark to uncover the diverse drivers, barriers, and entry points to enable a transformation process to resilient and sustainable organic food systems. Methods: Following the methodology of the SysOrg project, this study relied on the following mixed data collection methods: quantitative (a household survey distributed among citizens) and qualitative (semi-structured interviews with organized initiatives). Results: The results demonstrate that, despite being in different stages of development in the investigated territories, the organic sector is challenged by similar barriers (e.g., undeveloped market, regulatory/budgetary constraints, and lack of knowledge and awareness) and benefits from analogous drivers (e.g., awareness and education, community support, and incentives). Conclusions: Those similarities, but also analyses of their differences and origins, allowed us to establish critical entry points for the development of a sustainable organic food system, e.g., promoting organics through a top-down approach, providing training and education, reducing information delay, popularizing negative feedback, strengthening the effectiveness of a given incentives scheme by tailored nudging mechanisms, establishing country/regional specific traditional frames, making the system more inclusive, building organic communities, and awareness-building. Full article

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems. Full article

The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation. Full article

Mountain guides play an important role in the provision of nature-based tourism activities, such as mountaineering, in alpine environments around the world. However, these locales are uniquely sensitive to climate change, and despite extensive documentation of bio-geophysical changes, there are few studies evaluating the impacts of these changes on mountaineering routes and the livelihood of mountain guides. This constrains adaptation planning and limits awareness of potential loss and damage in the mountain tourism sector. In response, our study explored mountain guides’ lived experiences of working on Mt. Athabasca in Jasper National Park, Canada, to reveal the effects of climate change on mountaineering routes and implications for the mountain guiding community. To do this, we used a mixed methods approach that combined spatio-temporal trend analysis, repeat photography, and semi-structured interviews with mountain guides. We found that rising temperatures and changing precipitation regimes in the Mt. Athabasca area are driving glacial retreat and loss of semi-permanent snow and ice, which is impacting climbing conditions and objective hazards on mountaineering and guiding routes. Guides’ experiences of these changes varied according to socio-economic conditions (e.g., financial security, livelihood flexibility), with late-career guides tending to experience loss of guiding opportunities and early-career guides facing increased pressure to provide services in more challenging conditions. Our findings offer novel insights that identify salient issues and bolster support for actions in response to the concerns of the mountain guide community. This study also underscores the need for further research, as the underlying issues are likely present in mountaineering destinations globally. Full article

In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain. Full article