Search Results (535)

The likely increase in the frequency and severity of droughts with climate warming will pose an enormous challenge for the conservation of forest biodiversity. Documenting the response of species to recent droughts can inform future conservation actions. Mammals that breed and mature slowly may be especially vulnerable to drought-induced disruption to breeding. The yellow-bellied glider (Petaurus australis, Shaw) is a threatened low-density, arboreal marsupial of eastern Australia. Following a severe drought in 2019, one population had declined by 48% by 2021. The present study investigated whether this population had recovered 3–4 years (2022 and 2023) after that drought. Audio surveys of this highly vocal species were conducted at 42 sites, sampling > 1000 h per year, and producing recordings of 2038–2856 call sequences. The probability of occupancy varied little across the two survey years (0.92–0.97). Local abundance in 2023 had returned to pre-drought levels (45% of occupied sites had ≥3 individuals compared to 6% in 2021). These findings show a recovery from a drought-induced decline required at least 3 years, in keeping with the slow life history traits of this species. This study highlights the importance of considering a species’ life history strategy when evaluating its sensitivity to drought. Full article

Introduction: Radiomics is the extraction of non-invasive and reproducible quantitative imaging features, which may yield mineable information for clinical practice implementation. Quantification of lung function through radiomics could play a role in the management of patients with pulmonary lesions. The aim of this study is to test the capability of radiomic features to predict pulmonary function parameters, focusing on the diffusing capacity of lungs to carbon monoxide (DL_CO). Methods: Retrospective data were retrieved from electronical medical records of patients treated with Stereotactic Body Radiation Therapy (SBRT) at a single institution. Inclusion criteria were as follows: (1) SBRT treatment performed for primary early-stage non-small cell lung cancer (ES-NSCLC) or oligometastatic lung nodules, (2) availability of simulation four-dimensional computed tomography (4DCT) scan, (3) baseline spirometry data availability, (4) availability of baseline clinical data, and (5) written informed consent for the anonymized use of data. The gross tumor volume (GTV) was segmented on 4DCT reconstructed phases representing the moment of maximum inhalation and maximum exhalation (Phase 0 and Phase 50, respectively), and radiomic features were extracted from the lung parenchyma subtracting the lesion/s. An iterative algorithm was clustered based on correlation, while keeping only those most associated with baseline and post-treatment DL_CO. Three models were built to predict DL_CO abnormality: the clinical model—containing clinical information; the radiomic model—containing the radiomic score; the clinical-radiomic model—containing clinical information and the radiomic score. For the models just described, the following were constructed: Model 1 based on the features in Phase 0; Model 2 based on the features in Phase 50; Model 3 based on the difference between the two phases. The AUC was used to compare their performances. Results: A total of 98 patients met the inclusion criteria. The Charlson Comorbidity Index (CCI) scored as the clinical variable most associated with baseline DL_CO (p = 0.014), while the most associated features were mainly texture features and similar among the two phases. Clinical-radiomic models were the best at predicting both baseline and post-treatment abnormal DL_CO. In particular, the performances for the three clinical-radiomic models at predicting baseline abnormal DL_CO were AUC₁ = 0.72, AUC₂ = 0.72, and AUC₃ = 0.75, for Model 1, Model 2, and Model 3, respectively. Regarding the prediction of post-treatment abnormal DL_CO, the performances of the three clinical-radiomic models were AUC₁ = 0.91, AUC₂ = 0.91, and AUC₃ = 0.95, for Model 1, Model 2, and Model 3, respectively. Conclusions: This study demonstrates that radiomic features extracted from healthy lung parenchyma on a 4DCT scan are associated with baseline pulmonary function parameters, showing that radiomics can add a layer of information in surrogate models for lung function assessment. Preliminary results suggest the potential applicability of these models for predicting post-SBRT lung function, warranting validation in larger, prospective cohorts. Full article

Identifying and eliminating critical links in multi-hop networks is essential for enhancing overall network resilience. In this study, we propose a novel algorithm to detect links that significantly impact the pairwise connectivity of multi-hop networks. We formulate the critical link detection problem as minimizing pairwise connectivity subject to a total edge weight constraint c. The proposed method first computes the maximum flow between neighboring nodes to evaluate strong connections, and then progressively contracts these nodes to expose weaker connections. Throughout this iterative process, the algorithm records previously identified flows to minimize redundant flow computations. At each step, it also keeps track of the cut sets that reduce the network’s pairwise connectivity. Ultimately, it selects the subset of these cut sets whose removal minimizes pairwise connectivity while satisfying the total weight constraint c. This approach consistently identifies fewer yet more impactful critical edges than traditional Min-Cut or Greedy strategies. We evaluate the performance of our method against existing algorithms across various network sizes and node degrees. Experimental results show that the proposed method consistently discovers more influential edges and achieves a 34–38% reduction in pairwise connectivity, outperforming Greedy (22–24%), Min-Cut (24–32%), and Degree-based (12–19%) methods. Full article

Combining a large collection of patient data and advanced technology, healthcare organizations can excel in medical research and increase the quality of patient care. At the same time, health records present serious privacy and security challenges because they are confidential and can be breached through networks. Even traditional methods with federated learning are used to share data, patient information might still be at risk of interference while updating the model. This paper proposes the Privacy-Preserving Federated Learning with Homomorphic Encryption (PPFLHE) framework, which strongly supports secure cooperation in healthcare and at the same time providing symmetric privacy protection among participating institutions. Everyone in the collaboration used the same EfficientNet-B0 architecture and training conditions and keeping the model symmetrical throughout the network to achieve a balanced learning process and fairness. All the institutions used CKKS encryption symmetrically for their models to keep data concealed and stop any attempts at inference. Our federated learning process uses FedAvg on the server to symmetrically aggregate encrypted model updates and decrease any delays in our server communication. We attained a classification accuracy of 83.19% and 81.27% when using the APTOS 2019 Blindness Detection dataset and MosMedData CT scan dataset, respectively. Such findings confirm that the PPFLHE framework is generalizable among the broad range of medical imaging methods. In this way, patient data are kept secure while encouraging medical research and treatment to move forward, helping healthcare systems cooperate more effectively. Full article

The recent advancements and emergence of rapidly evolving AI models, such as large language models (LLMs), have sparked interest among researchers and professionals. These models are ubiquitously being fine-tuned and applied across various fields such as healthcare, customer service and support, education, automated driving, and smart factories. This often leads to an increased level of complexity and challenges concerning the trustworthiness of these models, such as the generation of toxic content and hallucinations with high confidence leading to serious consequences. The European Union Artificial Intelligence Act (AI Act) is a regulation concerning artificial intelligence. The EU AI Act has proposed a comprehensive set of guidelines to ensure the responsible usage and development of general-purpose AI systems (such as LLMs) that may pose potential risks. The need arises for strengthened efforts to ensure that these high-performing LLMs adhere to the seven trustworthiness aspects (data governance, record-keeping, transparency, human-oversight, accuracy, robustness, and cybersecurity) recommended by the AI Act. Our study systematically maps research, focusing on identifying the key trends in developing LLMs across different application domains to address the aspects of AI Act-based trustworthiness. Our study reveals the recent trends that indicate a growing interest in emerging models such as LLaMa and BARD, reflecting a shift in research priorities. GPT and BERT remain the most studied models, and newer alternatives like Mistral and Claude remain underexplored. Trustworthiness aspects like accuracy and transparency dominate the research landscape, while cybersecurity and record-keeping remain significantly underexamined. Our findings highlight the urgent need for a more balanced, interdisciplinary research approach to ensure LLM trustworthiness across diverse applications. Expanding studies into underexplored, high-risk domains and fostering cross-sector collaboration can bridge existing gaps. Furthermore, this study also reveals domains (like telecommunication) which are underrepresented, presenting considerable research gaps and indicating a potential direction for the way forward. Full article

The popularity of small-scale and local fruit and vegetable production has increased in recent years due to perceived economic, environmental, and social benefits. However, these operations face contamination risks that both consumers and small-scale producers may underestimate. The present study aimed to assess the microbiological and chemical hazards on fruit, vegetables, soil, and water samples from small-scale farms in north-western Portugal during pre-harvest activities. Additionally, the study investigated farmers’ non-compliance with food safety regulations and good agricultural practices (GAPs), exploring how their behaviour might contribute to the identified hazards. A before-and-after analysis of non-compliant behaviours was conducted to determine the impact of training on improving food safety practices. The analysis identified the presence of pathogenic bacteria, pesticides, flame retardant residues, nitrates, and heavy metals. Lead (Pb) concentrations exceeded EU limits in organic carrots from one producer (0.156 ± 0.043 mg/kg) and in chard from another (0.450 ± 0.126 mg/kg). Cadmium (Cd) levels were also above regulatory thresholds in bell peppers (0.023 ± 0.009 mg/kg) and organic tomatoes (0.026 ± 0.015 mg/kg) from two different producers. Elevated levels of heavy metals were detected in irrigation water from two sites, with zinc (Zn) at 0.2503 ± 0.0075 mg/L and Pb at 0.0218 ± 0.0073 mg/L. Among food samples, the most prevalent microorganisms were Pseudomonas spp. (88.2%), Bacillus cereus (76.5%), and aerobic mesophilic bacteria (100%). Phosphorus flame retardants (PFRs), particularly tris(2-butoxyethyl) phosphate (TBEP), were detected in all food and soil samples. Some EU-banned pesticides were detected in food and soil samples, but at levels below the maximum residue limits (MRLs). Chlorpyrifos (35.3%) and p,p’-DDD (23.5%) were the most detected pesticides in food samples. After the training, GAP behaviour improved, particularly that related to hygiene. However, issues related to record-keeping and soil and water analyses persisted, indicating ongoing challenges in achieving full compliance. Full article

Gesture recognition based on conventional machine learning is the main control approach for advanced prosthetic hand systems. Its primary limitation is the need for feature extraction, which must meet real-time control requirements. On the other hand, deep learning models could potentially overfit when trained on small datasets. For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. Within this framework, 1D-CNN filters are trained to generate latent representation that closely approximates Fisher’s (LDA’s) discriminant subspace, constructed from handcrafted features. Under the train-one-test-all evaluation scheme, our proposed hybrid framework consistently outperformed the 1D-CNN trained with cross-entropy loss only, showing improvements from 4% to 11% across two public datasets featuring hand gestures recorded under various limb positions and arm muscle contraction levels. Furthermore, our framework exhibited advantages in terms of induced spectral regularization, which led to a state-of-the-art recognition error of 22.79% with the extended 23 feature set when tested on the multi-limb position dataset. The main novelty of our hybrid framework is that it decouples feature extraction in regard to the inference time, enabling the future incorporation of a more extensive set of features, while keeping the inference computation time minimal. Full article

This Technical Note presents the design and implementation of a low-cost modular growth chamber developed to keep mother plants under controlled environmental conditions for vegetative propagation. The system was conceived as an accessible alternative to expensive commercial equipment, offering reproducibility and adaptability for small-scale and research-based cultivation. The proposed chamber integrates thermal insulation, LED lighting, forced ventilation through the implementation of extractors, a recirculating irrigation system with double filtration, and a sensor-based environmental monitoring platform operated via an Arduino UNO microcontroller. The design features a removable tray that serves as a support for the mother plant, an observation window covered by a movable dark acrylic that prevents the passage of external light, and a vertical structure that facilitates optimal space utilization and ergonomic access. Functionality was conducted using a Stevia rebaudiana Bertoni mother plant maintained for 30 days under monitored conditions. Environmental parameters—temperature, relative humidity, and illuminance—were recorded continuously. The plant showed vegetative development through new shoot emergence and the growth in height of the plant, and despite a loss in foliage expansion, it confirmed the chamber’s capacity to support sustained growth. Although no statistical replication or control group was included in this preliminary evaluation, the system demonstrates technical feasibility and practical utility. This chamber provides a replicable platform for future experimentation and propagation studies. Complete technical specifications, schematics, and component lists are provided to enable replication and further development by other researchers. The growth chamber design aligns with the goals of open-source agricultural innovation and supports knowledge transfer in controlled-environment plant propagation technologies. Full article

Background: Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions of people worldwide. PD results in motor and cognitive dysfunction. While there is no proven cure for PD, it is widely agreed that aerobic exercises and occupations can help slow the progression of the disease and keep some motor-related symptoms from developing. The most effective forms of exercise to slow the progression of motor symptoms in Parkinson’s disease have also been studied. Research Question: This research article aims to compare the differences in outcomes of exercise on cognitive outcomes in Parkinson’s Disease, as evaluated by meta-analysis. Methods: Key terms were Parkinson’s Disease and exercise terms. These search terms were then entered to electronic databases—Ovid MEDLINE, SCOPUS, and CINAHL—from March 2018 to May 2023. An ancestral bibliography was also performed. Results: Two reviewers screened the title and abstract records (n = 528) found in the initial search. Our review identified 18 studies which met inclusion criteria for meta-analysis. The meta-analysis found an effect of exercise on cognition of patients with PD (d = −0.03) which was not significant (CI95% of −0.13 < µ < 0.08; p > 0.05, as the CI includes zero). Additionally, the homogeneity analysis was not significant (Q (17) = 2.83; p > 0.05). Full article

Background/Objectives: Home non-invasive ventilation use is the primary treatment for improving respiratory function in people with chronic hypercapnic respiratory failure. Non-invasive ventilation has also been studied to understand users’ perspectives. However, no studies have been conducted on how users adapt to non-invasive ventilation in their homes from the early phase of their diagnosis as a long-term utility. Methods: The study employed a descriptive qualitative design guided by Roy’s adaptation model. A purposive sample was used. People with chronic hypercapnic respiratory failure who had used NIV at home for a minimum of six months would be eligible. They were interviewed at a sleep and assisted ventilation centre. Their interviews were audio recorded before proceeding with transcription. Each transcript was thematically analysed. Results: Twenty participants were included in the study, from which six themes emerged. They experienced a common transition, from denying the need for non-invasive ventilation to integrating them into their daily lives at home. Throughout this process, they had emotional turmoil, faced difficulties in keeping their masks on, and improved sleep quality. They also adjusted their social interactions before fully accepting the use of non-invasive ventilation. Their coping strategies in their role functions at home and social interaction were also narrated. Their family members were pivotal in their adaptation period. Conclusions: Gaining insight into individuals’ adaptation experiences can facilitate early identification of potential challenges faced by new users of non-invasive ventilation. This study calls for healthcare professionals to assess users’ understanding of long-term commitment and their living conditions early for a successful NIV adaptation. Full article

Historic Cairo, recognized as a UNESCO World Heritage Site in 1979, is renowned for its rich Islamic architecture, including sabils, which have played a crucial role in the urban fabric of this arid region. This study focuses on the oldest surviving Ottoman sabil in Cairo—the Sabil and Kutab of Khusru Pasha—as a case study for digital heritage preservation using advanced documentation technologies. We propose a flexible, dynamic documentation workflow based on the heritage digital twin (HDT) framework, which integrates both physical and digital-native processes. Through a hybrid methodology that combines 3D laser scanning, photogrammetry, and building information modeling (BIM), this study aims to transition from static heritage record-keeping to an interactive, semantically structured digital representation. This approach enhances the efficiency and accuracy of documentation, supports long-term conservation, and facilitates immersive public engagement. Quantitative data, including scan resolution and processing time, are used to assess the effectiveness of the adopted workflow. The digital twin created from this case study offers a replicable model for safeguarding similar mid-scale heritage assets across Islamic Cairo. Furthermore, integrating HDTs into virtual tourism frameworks creates new possibilities for cultural accessibility, education, and sustainable tourism development. By illustrating how historical buildings like the Khusru Pasha Sabil can be virtually preserved, monitored, and promoted, this study highlights the transformative potential of digital twin technology in heritage conservation. It contributes to the evolving discourse on smart documentation and management strategies, aligning with global sustainability goals and digital heritage preservation initiatives. Full article

Objectives: A retrospective clinical audit was carried out to evaluate and compare the quality of record-keeping (QRK) and quality of root canal therapy (QRCT) performed by 4th-year and 5th-year predoctoral students. Methods: Electronic records and periapical radiographs of 702 root canal treated teeth performed by 4th-year and 5th-year predoctoral students from July 2018 to December 2021 were evaluated in compliance with standard European Society of Endodontology (ESE) and American Association of Endodontists (AAE) guidelines. Associations between the QRK and the QRCT were statistically analysed using the chi-square test (p < 0.05). Results: Overall acceptability of the QRK and the QRCT was 72.08% and 50.57%, respectively. The reference point of working length was the most common criterion not recorded (33.91%). No significant difference was observed in the QRK between 4th-year (76.05%) and 5th-year (69.70%) students (p = 0.226), as well as the QRCT between 4th-year (51.33%) and 5th-year (50.11%) students (p = 0.755). Acceptable root canal fillings were significantly higher in anterior teeth (57.48%) than in posterior teeth (47.54%) (p = 0.015). Satisfactory QRK was significantly associated with satisfactory QRCT (p = 0.046). Conclusions: Both predoctoral cohorts showed no difference in QRK and QRCT. However, QRK was better than QRCT. Comprehensive and accurate record-keeping positively impacted the QRCT. Full article

This research makes a strong focus on improving fluid dynamics inside the reservoir after stimulation for enhancing oil and gas well performance, particularly in terms of increasing the Gas–oil ratio (GOR) and injectivity leading to a better productivity index (PI). Advanced stimulation operation using new formulated emulsified acid treatment greatly improves the reservoir permeability, allowing for better fluid movement and less formation damage. This, in turn, results in injectivity increases of at least 2.5 times and, in some situations, up to five times the original rate, which is critical for sustaining reservoir pressure and ensuring effective hydrocarbon recovery. The emulsified acid outperforms typical 15% HCl treatments in terms of dissolving and corrosion rates, as it is tuned for the reservoir’s pressure, temperature, permeability, and porosity. This dual-phase technology increases injectivity by five times while limiting the environmental and material consequences associated with spent and waste acid quantities. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficient penetration within the rock pores to enhance oil flow and sweep. This increase in performance is linked to the creation of the wormholing impact of the emulsified acid, resulting in improved fluid dynamics and optimized reservoir efficiency, as shown by the enhanced gas–oil ratio (GOR) in the four mentioned cases. A critical component of attaining such improvements is the capacity to effectively analyze and forecast reservoir behavior prior to executing the stimulation in real life. Engineers can accurately forecast injectivity gains and improve fluid injection tactics by constructing an advanced predictive model with low error margins, decreasing the need for time-consuming and costly trial-and-error approaches. Importantly, the research utilizes sophisticated neural network modeling to forecast stimulation results with minimal inaccuracies. This predictive ability not only diminishes the dependence on expensive and prolonged trial-and-error methods but also enables the proactive enhancement of treatment designs, thereby increasing efficiency and cost-effectiveness. This modeling approach based on several operational and reservoir factors, combines real-time field data, historical well performance records, and fluid flow simulations to verify that the expected results closely match the actual field outcomes. A well-calibrated prediction model not only reduces uncertainty but also improves decision making, allowing operators to create stimulation treatments based on unique reservoir features while minimizing unnecessary costs. Furthermore, enhancing fluid dynamics through precise modeling helps to improve GOR management by keeping gas output within appropriate limits while optimizing liquid hydrocarbon recovery. Finally, by employing data-driven modeling tools, oil and gas operators can considerably improve reservoir performance, streamline operational efficiency, and achieve long-term production growth through optimal resource usage. This paper highlights a new approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. The use of real-time monitoring has boosted the study by enabling for exact measurement of post-injectivity performance and oil flow rates, hence proving the efficacy of these advanced stimulation approaches. The study offers unique insights into unconventional reservoir growth by combining numerical modeling, real-world data, and novel treatment methodologies. The aim is to investigate novel simulation methodology, advanced computational tools, and data-driven strategies for improving the predictability, reservoir performance, fluid behavior, and sustainability of heavy oil recovery operations. Full article

The Paris Agreement is aimed at keeping global warming well below 2 °C while pursuing efforts to limit it below 1.5 °C; however, achieving these goals implies a tight limit on cumulative net carbon emissions, which includes CO₂, CH₄, and NO₂. Moreover, the focus of carbon emission policies should differ from country to country depending on their national circumstances. In this study, based on forecast models, specifically, in 2005, the average annual per-capita CO₂ emissions was recorded as 6.8 tons for Brazil, 4.8 tons for China, 8.4 tons for EU28, 1.2 tons for India, 10.1 tons for Japan, 9.0 tons for Russia, and 18.6 tons for the USA. The carbon intensity is expected to range from 37% to 85% across the studied regions. Based on the AIM, POLES, and IMAGE models, the projected carbon prices for 2050 are estimated at USD 2000, USD 2045, and USD 940 per ton of CO₂, measured in 2005 US dollars, respectively. The forecast data support carbon policy making in major countries. Full article

Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations. Full article