Search Results (914)

Background/Objective: The quality of care afforded to rural, remote, and First Nations Peoples is dependent on access to a health workforce with the capacity to contextualize healthcare and practice to the needs and expectations of these populations. In Australia, the lack of representation of rural health in undergraduate and post graduate health professional education undermines this preparedness and consideration of rural practice uptake and longevity, compounding the inequities confronted by 7 million Australians residing in these locations. Urgent educational reforms are required to address this omission, the deficit discourses used to characterize rural healthcare, and the persistent health workforce shortages experienced. This paper presents the Anabranch Framework for the Ruralization of Health Professional Education, a high-level strategy to transform rural healthcare provision, professional practice, and health workforce outcomes. Methods: The framework was developed through an iterative process involving a series of systematic steps. The process included the following: individual and group critical dialogues with internal academic educators, external health service leaders, metropolitan academic allies, and leaders of other rural health academic departments; an internal review of empirical studies of relevance to the ruralization of health professional education and practice; the visualization of a place-based framework; the academic conceptualization of the framework; and further critical dialogues to test the framework’s face validity. Results: The Anabranch Framework comprises four inter-related rural domains: theories, pedagogies, practices, and connectivity; four constructs: knowledge acquisition and generation, immersion in rural curriculum, knowledge translation and sharing, and relational practice; and two structural elements: spiraled and scaffolded curriculum and duration and the quality of rural placement and practice. Conclusions: The Anabranch Framework is a high-level strategy to ruralize health professional worldviews, advance rural person-centered practice, enable a deeper understanding of rural places and the development of an equity-orientated, sustainable and rural-literate health workforce. Full article

The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human–cyber–physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments. Full article

Background/Objectives: May–Thurner syndrome (MTS) is a common cause of iliofemoral deep vein thrombosis (DVT). Venous bleeding associated with MTS is extremely rare and has been reported mainly as spontaneous iliac vein rupture (SIVR) with retroperitoneal or iliac hematoma. Additionally, standardized treatment strategies have not yet been established. Herein, we report a case of an iliac hematoma associated with MTS that was successfully treated with endovascular mechanical thrombectomy and bare-metal stenting. Case Presentation: A 69-year-old man presented with acute swelling and pain in the left lower extremity. Computed tomography angiography demonstrated extensive iliofemoral DVT and an iliac hematoma adjacent to the left common iliac vessels, without definite evidence of iliac vein rupture. Initial conservative management with anticoagulation therapy was selected as the patient was hemodynamically stable and showed no active bleeding. However, follow-up imaging one week later revealed persistent DVT with interval enlargement of the hematoma. Pelvic arteriography excluded an arterial bleeding source. Endovascular treatment was performed, including mechanical thrombectomy using the AngioJet system and bare-metal stent placement to restore venous outflow. Follow-up imaging revealed complete thrombus resolution, hematoma regression, and sustained stent patency. Conclusions: Iliac hematomas associated with MTS may occur without definite radiological evidence of iliac vein rupture. In selected hemodynamically stable patients, an individualized endovascular strategy focused on venous outflow restoration using the AngioJet system and bare metal stents may be a feasible treatment option. Full article

Floating-base robotic systems rely critically on their inertial geometry to maintain stability and regulate interaction forces in the absence of fixed ground constraints. Their control authority additionally depends on the placement and orientation of actuators relative to the center of mass, which determines the moment arms through which thrust or force inputs generate stabilizing actions. This paper develops a general theoretical framework showing that internal mass shifting provides a powerful, domain-independent mechanism for reshaping global system dynamics. Through geometric principles governing center-of-mass placement, moment-arm modification, and inertia redistribution, mass shifting enhances passive stability, reduces the torque induced by external disturbances, and improves the controllability of interaction-intensive tasks. The theory is first examined in a buoyancy-driven simulation of a two-mass floating body subjected to multi-sine wave excitation, which isolates the hydrostatic effects of center-of-mass displacement. To validate the generality of these principles, we further demonstrate their applicability in a radically different domain through real-world experiments on the AeroBull aerial robot, a multirotor platform equipped with an internal mass-shifting mechanism for aerial manipulation. Across both aquatic and aerial settings, mass shifting consistently improves stability, reduces control effort, and increases achievable interaction forces. These results establish internal mass redistribution as a platform-agnostic strategy for enhancing the stability and resilience of floating-base robots operating in uncertain and physically demanding environments. Full article

Lung cancer is considered to be a significant cause of death in the world, and the timely identification of nodules in the lungs in CT scans is very important to enhance the prognosis of patients. Although the state of the art of nodule delineation using deep learning-based segmentation models was achieved, major problems, including high feature diversity, low spatial discrimination, and overfitting of the models, require stronger feature-processing approaches. This research explores an enhanced symmetric encoder–decoder segmentation network known as the Improved Group–Shuffle Module (IGSM) to overcome these shortcomings. The most important feature of the proposed method is the IGSM, which hierarchically divides feature maps into a few groups, then transforms them independently, and then randomly switches channels between groups to increase inter-group interaction of features and diversity. This IGSM method is inspired by human brain functions, which are processed in specialized cortex areas, which are mimicked in this work through small-group feature processing. Channel shuffling is designed based on inter-modular communication in the human brain through coherent information sharing among the small groups of cortices. Through this mechanism, the model is much better at capturing discriminative spatial and contextual patterns, especially on complex and subtle nodule structures. The IGSM configurations have been optimized, specifically, the placement of the modules, grouping size, and shuffle permutation strategies. The proposed model’s performance is then compared with the benchmarked models, like U-Net and DeepLab, with various performance indicators such as mean Intersection over Union (mIoU), Dice Score, Accuracy, Sensitivity, and Specificity. The simulation results proved the superiority of the IGSM-enhanced model with the mIoU of 0.7735, the Dice Score of 0.9665, and the Accuracy of 0.9873. The addition of the group and shuffle module not only enhances the discrimination between the nodules and their background, but it also improves the ability to generalize over a variety of nodules’ morphology, thus producing a reliable tool for automated detection of lung cancer. Full article

Intensive pig production generates significant emissions of ammonia (NH₃) and methane (CH₄), gases with both environmental and health impacts, primarily originating from slurry storage lagoons and their management. This study monitored a maternity pig farm over a 360 day period, using sensors located next to the slurry storage lagoon (Sensor 4) and in the immediate external surroundings of the facility, while simultaneously recording environmental variables (temperature, relative humidity, wind, and precipitation). The results showed that concentrations at the lagoon were thousands to tens of thousands of times higher than those measured in the surrounding area, with temperature and relative humidity emerging as key factors that increase volatilization and microbial generation, especially in summer under medium humidity conditions. Precipitation and wind modulate concentrations through resuspension and dispersion processes. Overall, the slurry storage lagoon constitutes the primary hotspot of emissions, and proper sensor placement is essential to accurately estimate its real impact, while integrating climatic and spatial conditions is crucial for designing and implementing effective mitigation strategies in intensive pig production systems. Full article

Edge–cloud computing has emerged as a key enabling paradigm for augmented and virtual reality (AR/VR) systems because of the stringent computational and ultra-low-latency requirements of AR/VR workloads. Designing efficient edge–cloud systems for such workloads involves multiple technical aspects, including communication technologies, service placement, task offloading and caching, service migration, and security and privacy. This paper provides a structured and technical analysis of these aspects from an AR/VR perspective. We adopt a two-stage literature analysis, in which Google Scholar is used to identify fundamental technical aspects and solution approaches, followed by a focused analysis of recent research trends and future directions using academic databases (e.g., IEEE Xplore, ACM Digital Library, and ScienceDirect). We present an organized classification of the core technical aspects and investigate existing solution approaches, including heuristic, metaheuristic, learning-based, and hybrid strategies. Rather than introducing application-specific designs, the analysis focuses on workload-driven challenges and trade-offs that arise in AR/VR systems. Based on this classification, we analyze recent research trends, identify underexplored technical areas, and highlight key research gaps that hinder the efficient deployment of AR/VR services over edge–cloud infrastructures. The findings of this study provide practical insights for researchers and system designers and help guide future research toward more responsive, scalable, and reliable edge–cloud AR/VR systems. Full article

While deep fertilization improves crop yields and fertilizer use efficiency, it alters crop growth and soil nutrient/moisture distribution, driving nitrous oxide (N₂O) emissions—a potent greenhouse gas. However, conflicting evidence and the unknown effects of varying fertilizer placement depths in mechanized direct-seeded winter rapeseed fields leave the critical trade-off between productivity and emissions mitigation poorly understood. A 2-year field experiment (2019–2021) was conducted in the Yangtze River basin, China. The static closed chamber technique combined with gas chromatography was utilized to investigate the impacts of fertilizer placement depths (5 cm, 10 cm, and 15 cm, designated as D5, D10, and D15, respectively) on soil N₂O emissions, with a no-fertilization treatment serving as the control. Results demonstrated that N₂O fluxes under all treatments exhibited a rapid decline during the early growth stages of rapeseed, subsequently stabilizing at low levels; these dynamics were partially linked to soil temperature and soil water content (SWC). Specifically, N₂O flux showed a significant but moderate exponential response to soil temperature and a weak quadratic trend with SWC. As fertilization depth increased, the richness and diversity of AOA, AOB, and nirK communities showed a numerical decline (p > 0.05). N₂O emissions under D5 were on average 8.7% higher than D10 (p > 0.05), but were significantly 18.0% higher than D15 (p < 0.05). Yield-scaled N₂O emissions under D10 were reduced by 12.7% and 22.3% relative to D5 and D15, respectively. Compared with D10 and D15, the N₂O emission factor increased by 12.9% and 29.0% under D5, respectively (p < 0.05). The net ecosystem economic budget under D10 was 6.5% and 48.6% greater than that of D5 and D15, respectively. Considering crop yield, production costs, and carbon emission, a fertilizer placement depth of 10 cm is recommended as optimal. These findings offer valuable insights for mitigating N₂O emissions and informing rational fertilization strategies in rapeseed cultivation. Full article

Background/Objectives: There are multiple decision nodes, during and after subtotal colectomy for ulcerative colitis (UC), regarding the management of the rectal stump. Intra-operatively, the surgeon must decide on the closure technique and positioning of the retained stump, while post-operatively, clinicians often face the challenge of managing diversion proctitis, as well as determining an appropriate endoscopic surveillance strategy, given the increased risk of cancer. This narrative review aims to summarise the evidence relating to these key decision points in rectal stump management. Methods: A narrative review of the literature was performed. Relevant studies were identified through a search of Ovid Medline and Embase. Inclusion criteria were adult population and diagnosis of UC. Cohort studies, review articles, and guidelines were eligible for inclusion. The references were grouped according to the subject of interest and reported accordingly. Results: Intra-peritoneal closure has been shown to have higher pelvic sepsis rates (5–25%), whereas subcutaneous placement results in higher rates of wound infections (up to 15%). A mucous fistula has been shown to have the lowest overall complication rate. Microscopic findings compatible with diversion proctitis develop in most patients, with incidence ranging from 71.4% to 100%. However, only a minority of these patients (30–40%) develop symptoms. Suggested treatments for diversion proctitis include topical mesalamine, corticosteroids, or short-chain fatty acids. The overall risk of rectal stump neoplasia in patients with UC after subtotal colectomy is as low as 0.7%, with prior colorectal neoplasia being a major risk factor. No universal standardised guidance exists regarding endoscopic surveillance in this patient population. Conclusions: This narrative review has appraised the latest evidence on three crucial stages of rectal stump management in UC. There is still uncertainty about the optimal surgical management of the stump, with different complication profiles. Medical management of diversion proctitis remains a major unmet need, and there are no randomised trials addressing this issue. There are no universally accepted guidelines on endoscopic surveillance of the rectal stump. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

Unmanned Aerial Vehicle Communication Networks (UAVCNs) have emerged as a transformative solution to enable resilient, scalable, and infrastructure-independent wireless communication in urban and remote environments. A key challenge in UAVCNs is the optimal placement of Unmanned Aerial Vehicle (UAV) nodes to maximize coverage, connectivity, and overall network performance while minimizing latency, energy consumption, and packet loss. As this node placement problem is NP-hard, numerous meta-heuristic algorithms (MHAs) have been proposed to find near-optimal solutions efficiently. Although research in this area has produced a wide range of meta-heuristic algorithmic solutions, most existing review articles focus on MANETs with terrestrial nodes, while comprehensive reviews dedicated to node placement in UAV communication networks are relatively scarce. This article presents a critical and comprehensive review of meta-heuristic algorithms for UAVCN node placement. Beyond surveying existing methods, it systematically analyzes algorithmic strengths, vulnerabilities, and future research directions, offering actionable insights for selecting effective strategies in diverse UAVCN deployment scenarios. To demonstrate practical applicability, selected hybrid algorithms are evaluated in a reproducible Python framework using computational time and coverage metrics, highlighting their ability to optimize multiple objectives and providing guidance for future UAVCN optimization studies. Full article

The concept of active blade twisting as a method for reducing helicopter noise and vibration during flight is presented. Active twisting is achieved through piezoelectric actuators embedded in the blade skin, which generate dynamic twist when subjected to an electric field. Such dynamic deformation can lower fuel consumption while also reducing noise and vibration levels. A methodology for determining the optimal geometric dimensions of the cross-section of a helicopter blade, taking into account design constraints, is proposed to achieve the maximum twist angle of the blade under the action of piezoelectric actuators. First, a three-dimensional numerical model of the BO 105 model-scale rotor blade is developed in the finite element software ANSYS 16.0. The effect of the rotor blade’s cross-sectional dimensions on the cross-sectional properties and twist angle is investigated. It is found that skin thickness, spar flange thickness, and spar flange length affect the twist angle, with skin thickness showing a significant effect. Based on these results, an optimisation strategy is formulated to identify the optimal blade cross-section configuration to achieve the maximum twist angle. It was established that with the optimised geometric parameters of the cross-section the maximum active twist reaches 5.2°, while the positions of the elastic axis and the centre of gravity exhibit only minor deviations from those of the reference model. The placement of the piezoelectric actuators has a significant influence on both the flapwise bending stiffness and the torsional stiffness of the blade. Full article

Professional experience placements are a requirement for undergraduate nursing students enabling real world skill development. Barriers to meaningful and positive placements have previously been reported, however there is limited research on how the location of placement impacts the student experience and outcomes. This study investigates the placement experiences of undergraduate nursing students at the University of Tasmania (UTAS) over a five-year period, with a focus on urban versus rural settings and year-level differences. Findings reveal that over one-third of students reported constructive placement experiences, with rural placements yielding slightly more positive outcomes than urban ones. First-year students were more likely to report constructive experiences compared to their senior counterparts, suggesting that longer placement durations may contribute to increased dissatisfaction. Quality of placement—defined by supervision and skill development—emerged as the most influential factor in shaping student experiences. While most students praised the quality of supervision, third-year students expressed both the highest praise and criticism. Opportunities for clinical and interpersonal skill development were central to students’ perceptions of placement quality, with rural placements slightly outperforming urban in skill development. However, some students, particularly in later years, felt that certain venues lacked adequate opportunities for skill acquisition. The study underscores the importance of high-quality supervision and appropriate clinical settings in enhancing placement experiences and suggests that constructive placements are more conducive to learning. These insights can inform strategies to improve the educational value of nursing placements across diverse settings. Full article

Rainfall-adapted irrigation (RAI), the application of controlled-release nitrogen fertilizer (CRNF), and deep placement of nitrogen fertilizer can contribute to the improvement of resource utilization efficiency. Nevertheless, the interactive effects of these factors on nitrogen loss via runoff and leaching from paddy fields remain ambiguous. Consequently, a two-year field experiment was conducted to evaluate the interactive effects of four nitrogen management strategies on nitrogen losses through runoff and leaching from paddy fields and rice yield under RAI when compared to conventional flooding irrigation (CI). Compared to CI, RAI significantly reduced total nitrogen loss via runoff (−49.8%) and leaching (−35.9%) by lowering volume of runoff and leaching. Compared to conventional nitrogen application (surface application of common urea with 240 kg N ha⁻¹), deep placement of CRNF with 192 kg N ha⁻¹ decreased floodwater nitrogen concentration, reducing total nitrogen loss by 46.8% via runoff and 50.9% via leaching. Importantly, RAI combined with deep placement of CRNF with 192 kg N ha⁻¹ minimized nitrogen losses through leaching and runoff from paddy fields and maximized grain yield (8251 kg ha⁻¹) by improving nitrogen accumulation in rice. Collectively, RAI combined with deep-placed CRNF with an 80% nitrogen rate could reduce non-point source pollution from paddy fields. Full article

Despite its ecological and economic importance, Hampala macrolepidota (Cyprinidae: Smiliogastrinae) remains taxonomically debated, having undergone historical reclassifications across multiple taxonomic ranks. These challenges highlight the urgent need for integrative genomic analyses to resolve its phylogeny and assess genome-wide diversity, establishing a baseline for effective management and conservation. In this study, the newly assembled mitogenome of H. macrolepidota from within its native range in Lake Dibawah, West Sumatra, Indonesia, was sequenced. The mitogenome spanned 17,104 bp, encoded 37 genes and a control region, and exhibited a nucleotide composition biased toward adenine and thymine. The protein-coding genes (PCGs) predominantly utilized ATG as the initiation codon and showed a higher proportion of hydrophobic compared to hydrophilic amino acids. The nonsynonymous (Ka) and synonymous (Ks) substitution ratios were below ‘1’, which indicates negative selection on most of the PCGs within Hampala and other Smiliogastrinae species. Mitogenome-wide analysis revealed overall high intraspecific genetic diversity (≥2.7%) in the native Indonesian population compared to mainland populations in Southeast Asia. The Bayesian and maximum-likelihood phylogenetic analyses elucidated matrilineal evolutionary relationships within the subfamily Smiliogastrinae, with the Hampala species forming a monophyletic cluster. The present mitogenome-based phylogenetic topologies also supported the taxonomic placement of several species in the revised classification, which previously were classified under the genera Puntius and Barbus, respectively. Additionally, the investigation of partial mitochondrial COI and Cytb genes further elucidated the population genetic structure of H. macrolepidota across Southeast and East Asia. The observed genetic divergence (0–4.2% in COI and 0–4.5% in Cytb), together with well-resolved phylogenetic clustering and the presence of both shared and distinct haplotypes among Indonesian samples, provides strong evidence for long-term population isolation and local adaptation. These patterns are most plausibly driven by historical hydrological dynamics, paleo-drainage connectivity, and persistent geographic barriers that have structured population divergence over time. In addition, this study emphasizes the need to generate mitogenomes of seven additional Hampala species from Southeast Asia to better understand their evolutionary patterns. Further, broader sampling of wild H. macrolepidota populations across their biogeographical range will be essential to strengthen understanding of their genetic diversity and guide effective conservation strategies. Full article