Hospital service robots must coordinate time-sensitive and heterogeneous tasks while maintaining reliable awareness of nearby robots in structured, human-populated clinical spaces where GPS, wireless communication, and global localization can be degraded. Existing studies typically address perception, task allocation, or path optimization as separate components, which limits their ability to support closed-loop multi-robot coordination under communication-constrained hospital conditions. This paper presents a graph-driven multi-robot coordination framework with situational awareness for hospital service robotics. The framework couples three coordination modules on a shared MAKLINK Graph Theory (MGT) representation of the hospital layout. First, a lightweight CNN-LiDAR module performs point-wise binary segmentation of 2D LiDAR scans to detect peer robots and maintain local fleet awareness during GPS or network outages. Second, the detected peer positions and confidence scores are passed to a priority-weighted Self-Organizing Map (SOM), which allocates asynchronous service tasks according to task urgency, deadline, risk, proximity, and workload balance, with Lazy Theta* generating feasible any-angle initial routes on the MGT graph inside the allocation loop to link assignment decisions to executable navigation. Finally, an improved Moss Growth Optimization (iMGO) module refines the initial routes by optimizing waypoint locations on a compact line-of-sight-preserving MAKLINK domain. Simulation and comparison studies validate the perception, allocation, path-refinement, and integrated pipeline components. The CNN-LiDAR subsystem sustains reliable peer-robot detection during GPS and communication outages, with near-perfect accuracy at the close range most critical for collision avoidance. The priority-weighted SOM improves task-completion behavior and workload balance compared with clustering and non-clustering genetic algorithm baselines. The iMGO module produces shorter and smoother collision-free paths with faster convergence than competing optimizers. The integrated multi-robot simulations further show reduced mission time and travel distance relative to random allocation, supporting the proposed framework as a scalable and safety-aware coordination architecture for hospital service robotics.
With the rapid uptake of environmental DNA (eDNA) metabarcoding for a wide range of conservation and management uses, there is a growing need for guidance and best practices for species identification. A range of methodological and interpretive decisions influences taxa assignment. Here we provide a review and perspective of pitfalls and issues that can impact accurate taxonomic assignment across the full eDNA metabarcoding workflow: primer selection, lab handling, bioinformatics, taxonomic assignment, and communication of results. This paper addresses the complexities and challenges of the metabarcoding-based species identification process, offering recommendations for robust workflows and effective communication of findings. Accurate interpretation and communication of eDNA metabarcoding results requires an acknowledgment of methodological limitations such as incomplete reference databases, contamination risks, ambiguous sequences, and detection biases. Thus, we argue that transparency of methods and limitations, alongside proactive alignment of decisions with project objectives are critical for the successful application of eDNA metabarcoding in conservation and management decisions. We provide guidance for developing protocols that support species identification from eDNA metabarcoding sequences, and give recommendations on communication strategies for stakeholders and end users. To support these recommendations, we outline steps in the workflow that can impact species identification, with a discussion of the strengths and weaknesses at each stage. Ultimately, this guidance can improve the accuracy, reliability, and usability of eDNA and other metabarcoding and amplicon sequencing approaches to species identification while fostering trust and understanding among diverse end users.
This study provides a comparative review of existing research on happiness at work before and after the onset of the COVID-19 pandemic. A systematic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure comprehensive retrieval of relevant literature. The study sourced articles from various academic databases, resulting in 27 publications from 2009 to 2019 (the decade preceding the pandemic) and 61 publications from 2020 to 2023 (after the onset of COVID-19). This was subsequently complemented by a narrative review to extract qualitative thematic insights pertaining to organizational, leadership, employee, and work-level factors. At the organizational level, research since the start of the pandemic has shifted focus toward creating a positive environment centered on human-centered ethics, organizational culture, practices, certification, and strategic approaches rooted in workplace happiness to benefit employees. Leadership styles have adapted in response to the pandemic, emphasizing human-centered and empathetic methods. At the employee level, the focus has been on individual customization, coping strategies, promoting psychological well-being, and mindfulness. At the work level, attention is given to issues such as flexible work arrangements, remote work, autonomy, job demands, and job crafting. Various theoretical frameworks exist within the literature on happiness at work; however, Fisher's Happiness at Work has gained significant prominence since the pandemic's onset, with 51% of articles focusing on this framework. Organizations must acknowledge that happiness at work is evolving alongside changes in work practices and arrangements and management must prioritize and actively support employees' well-being to foster a positive work environment. This paper offers valuable insights for engineering managers by highlighting human-centered leadership and employee well-being, which are often ignored as critical priorities in the post-pandemic era. Just as employees are integral components of an organizational system essential for its proper functioning, focusing on this system and enhancing employee happiness at work will improve productivity, quality, reduce absenteeism, and overall enhance work experience. Therefore, this review clears the way for future happiness at work research specifically focused on engineering management.
Pollinator communities in urban and peri-urban turfgrass remain poorly characterized despite the ubiquity of lawns and growing interest in management practices that enhance floral resources within these otherwise resource-limited environments. To address this gap, we evaluated the effectiveness of active (sweep netting at multiple times of day) and passive (molasses traps and blue, white, and yellow pan traps) sampling methods for surveying insect pollinators in forb-enhanced bermudagrass plots in Mississippi and Georgia. Eight replicate plots were sampled over the course of one week at two sites, using a randomized complete block design. We identified 4,638 insect pollinator specimens representing 153 distinct taxa. Overall, sweep netting captured more individuals but tended to be biased toward larger, social taxa (e.g., Apis, Bombus), whereas pan traps collected greater per-sample species richness and disproportionately sampled smaller, solitary taxa. Molasses traps yielded comparatively few individuals and low taxonomic diversity relative to other methods and were therefore excluded from final comparative analyses. Community composition differed significantly among sampling methods. These results demonstrate that no single method adequately captures full pollinator assemblages in forb-enhanced turfgrass systems; a complementary protocol combining sweep netting and pan trapping is recommended for comprehensive pollinator surveys in managed turf landscapes. Implications for Insect Conservation: Although conventional turfgrass monocultures generally provide limited value for insect pollinators, turfgrass-dominated landscapes are widespread in urban and peri-urban environments and therefore represent an opportunity for targeted enhancement through the incorporation of flowering forbs. However, realizing this potential requires unbiased, efficient, and reliable pollinator surveying methods that are specifically tailored to forb-enhanced turf. Here, we provide analyses that quantify the method-specific biases, temporal effects, and complementary strengths of active and passive surveying methods for monitoring pollinator abundance, richness, and community composition.
Rapid urban growth in tropical megacities is putting serious pressure on critical ecosystem services, thereby complicating the implementation of sustainable urban planning frameworks. To better understand and address these challenges, we used artificial intelligence (AI) and satellite data to map and predict land-use changes in Chittagong City Corporation (CCC), Bangladesh, from 2000 to 2048. We combined Random Forest (RF) classification with a Cellular Automata–Artificial Neural Network (CA–ANN) model. The Random Forest method classified four land-cover types: vegetation, built-up areas, barren land, and open water bodies, with over 97
