Search Results (587)

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

There are many different cells that perform highly specialized functions in the human hematological and immune systems. Due to the relevance of their activity, in this work we investigated the cell types and subtypes that form this complex system, using single-cell RNA sequencing (scRNA-seq) to dissect and assess the markers that best define each cell population. We first developed an optimized computational workflow for analyzing large scRNA-seq datasets. We then used it to find gene markers of the different cell types present in bone marrow (BM) and peripheral blood (PB). We analyzed three different single-cell datasets to find specific cell markers using this strategy: first, we searched in the CD marker genes and then in the genes encoding membrane proteins and finally in all detected protein-coding genes. This allowed us not only to confirm known CDs that best mark some cell types (e.g., monocytes, B cells, NK cells, etc.) but also to test the ability of new genes to distinguish specific cell types. Finally, we applied a machine learning method (Random Forest) to test the accuracy of the different markers we found. As a result of all this work, we have found and propose specific and robust gene signatures to identify different types and subtypes of hematological and immune cells. Full article

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding. Full article

Objectives: Parenteral drug products manufactured under GMP conditions should be visually inspected for defects and particulate contamination by trained and qualified personnel. Although personnel qualification is required, no practical protocols or formal guidelines are available for the development of qualification test sets (QTSs) used for qualification procedures. The current practice is to either procure a standardized QTS from a commercial supplier or amass sufficient manufacturing rejects during visual inspection procedures to compile in-house QTSs. However, both strategies inherently possess disadvantages and limitations. The objective of this study was to develop a manufacturing protocol for an optimal and adaptable QTS for training and qualification procedures. Methods: We combined the results of a literature search, survey of five Dutch hospital pharmacy compounding facilities, semi-structured personnel interviews, and extensive pre-GMP formulation studies to develop an optimal and adaptable QTS manufacturing protocol. Results: The literature search did not identify a manufacturing protocol for an optimal and adaptable QTS, but did identify specifications and requirements for optimal QTSs. The survey among hospital pharmacy compounding facilities revealed considerable variability in the qualification procedures and used QTSs. Semi-structured personnel interviews and pre-GMP formulation studies demonstrated that defects encountered during routine productions could be realistically simulated with pharmaceutical-grade excipients. As a proof-of-concept, we manufactured two different QTSs under GMP conditions and assessed these for formal GMP training and qualification purposes, which were considered a significant improvement compared to using manufacturing rejects. Conclusions: To the best of our knowledge, this is the first study presenting these data and our adaptable protocol, which is provided in the Supplemental Materials, may aid compounding facilities in the standardization, training, and qualification of personnel involved in visual inspection procedures. Full article

The grey wolf optimization (GWO) algorithm is a simple and effective meta-heuristic algorithm that mimics the leadership of grey wolves and the social behavior of wolves in nature. However, the updating of GWO population positions only relies on the guidance of $\alpha$-wolf, $\beta$-wolf, and $\delta$-wolf, and individuals are updated with equal weights. This results in the GWO search process being unable to utilize the knowledge of superior wolves better. Therefore, in this study, we propose for the first time an attention mechanism-based GWO (AtGWO). Firstly, when each position is updated, the attention strategy can adaptively assign the weight of the corresponding leader wolf to improve the global exploration ability. Second, with the introduction of omega-wolves, each position update is not only guided by the three leader wolves but also learns from their current optimal values. Finally, a hyperbolic tangent nonlinear function is used to control the convergence factor to better balance exploration and exploitation. To validate its effectiveness, AtGWO is compared with the latest GWO variant with other popular algorithms on the CEC-2014 (dim 30, 50) and CEC-2017 (dim 30, 50, 100) benchmark function sets. The experimental results indicate that AtGWO outperforms the GWO-related variants almost all the time in terms of mean, variance, and best value, which indicates its superior ability and robustness to find optimal solutions. And it is also competitive when compared to other algorithms in multimodal functions. AtGWO outperforms the comparison algorithms in terms of the mean and best value in six real-world engineering optimization problems. Full article

Colombian goldenberries that do not reach sufficient quality for export are exposed to waste, so the search for processes that provide added value while guaranteeing the conservation of this fruit is paramount. Thus, snacks by vacuum frying from goldenberries (Physalis peruviana L.) with low export quality were made. Goldenberry slices previously subjected to ultrasound-assisted osmotic dehydration were used for this purpose. Response surface methodology with different levels of temperature (110 °C and 130 °C), vacuum pressure (0.3 bar and 0.5 bar), and time (2 min and 6 min) was used to optimize the process. At optimal vacuum frying conditions (i.e., 108 °C, 0.5 bar, and 5.5 min), snacks with lower (p ≤ 0.05) oil content were produced, compared to atmospheric frying chips. The optimized snacks had 9% oil, 7% moisture, ΔE of 13 (with respect to fresh fruit), a_w of 0.3, and hardness of 14 N. The kinetics and modeling of moisture loss and oil uptake were performed under optimal conditions, obtaining the best fit with the Page (R² = 99%) and the first-order (R² = 96%) models, respectively. There was a clear correlation between oil uptake and moisture loss, as the highest oil retention in the product took place when the product had lost the greatest amount of water; therefore, the low initial moisture in the product due to pretreatment resulted in lower oil uptake in it. The obtained goldenberry snack showed adequate physicochemical properties, and the pretreatment yielded a product with much healthier characteristics (i.e., lower oil content, and therefore, a lower caloric intake); so, the proposed process could represent an alternative to the processing of low-export quality Colombian goldenberries. Full article

With the development of unmanned aerial vehicle (UAV) technology, the threat of UAV intrusion is no longer negligible. Therefore, drone perception, especially anti-UAV tracking technology, has gathered considerable attention. However, both traditional Siamese and transformer-based trackers struggle in anti-UAV tasks due to the small target size, clutter backgrounds and model degradation. To alleviate these challenges, a novel contrastive-augmented memory network (CAMTracker) is proposed for anti-UAV tracking tasks in thermal infrared (TIR) videos. The proposed CAMTracker conducts tracking through a two-stage scheme, searching for possible candidates in the first stage and matching the candidates with the template for final prediction. In the first stage, an instance-guided region proposal network (IG-RPN) is employed to calculate the correlation features between the templates and the searching images and further generate candidate proposals. In the second stage, a contrastive-augmented matching module (CAM), along with a refined contrastive loss function, is designed to enhance the discrimination ability of the tracker under the instruction of contrastive learning strategy. Moreover, to avoid model degradation, an adaptive dynamic memory module (ADM) is proposed to maintain a dynamic template to cope with the feature variation of the target in long sequences. Comprehensive experiments have been conducted on the Anti-UAV410 dataset, where the proposed CAMTracker achieves the best performance compared to advanced tracking algorithms, with significant advantages on all the evaluation metrics, including at least 2.40%, 4.12%, 5.43% and 5.48% on precision, success rate, success AUC and state accuracy, respectively. Full article

Efficient Virtual Machine (VM) placement is a critical challenge in optimizing resource utilization in cloud data centers. This paper explores both exact and approximate methods to address this problem. We begin by presenting an exact solution based on a Multi-Objective Integer Linear Programming (MOILP) model, which provides an optimal VM Placement (VMP) strategy. Given the NP-completeness of the MOILP model when handling large-scale problems, we then propose an approximate solution using a Tabu Search (TS) algorithm. The TS algorithm is designed as a practical alternative for addressing these complex scenarios. A key innovation of our approach is the simultaneous optimization of three performance metrics: the number of accepted VMs, resource wastage, and power consumption. To the best of our knowledge, this is the first application of a TS algorithm in the context of VMP. Furthermore, these three performance metrics are jointly optimized to ensure operational efficiency (OPEF) and minimal operational expenditure (OPEX). We rigorously evaluate the performance of the TS algorithm through extensive simulation scenarios and compare its results with those of the MOILP model, enabling us to assess the quality of the approximate solution relative to the optimal one. Additionally, we benchmark our approach against existing methods in the literature to emphasize its advantages. Our findings demonstrate that the TS algorithm strikes an effective balance between efficiency and practicality, making it a robust solution for VMP in cloud environments. The TS algorithm outperforms the other algorithms considered in the simulations, achieving a gain of 2% to 32% in OPEF, with a worst-case increase of up to 6% in OPEX. Full article

Geographic Knowledge Base Question Answering (GeoKBQA) has garnered increasing attention for its ability to process complex geographic queries. This study focuses on schema retrieval, a critical step in GeoKBQA that involves extracting relevant schema items (classes, relations, and properties) to generate accurate operational queries. Current GeoKBQA studies primarily rely on rule-based approaches for schema retrieval. These predefine words or descriptions for each schema item. This rule-based method has three critical limitations: (1) poor generalization to undefined schema items, (2) failure to consider the semantic meaning of user queries, and (3) an inability to adapt to languages not used in the predefined step. In this study, we present a schema retrieval model by using few-shot prompting on GPT-4 Turbo to address these issues. Using the SKRE dataset, we searched for the best prompt in terms of enabling the model to handle Korean geographic questions across various generalization levels. Notably, this method outperformed fine-tuning in zero-shot scenarios, underscoring its adaptability to unseen data. To our knowledge, this is the first attempt to develop a schema retrieval model for GeoKBQA that purely utilizes a language model and is capable of processing Korean geographic questions. Full article

Code search uses natural language queries to retrieve code snippets from a vast database, identifying those that are semantically similar to the query. This enables developers to reuse code and enhance software development efficiency. Most existing code search algorithms focus on capturing semantic and structural features by learning from both text and code graph structures. However, these algorithms often struggle to capture deeper semantic and structural features within these sources, leading to lower accuracy in code search results. To address this issue, this paper proposes a novel semantics-enhanced neural code search algorithm called SENCS, which employs graph serialization and a two-stage attention mechanism. First, the code program dependency graph is transformed into a unique serialized encoding, and a bidirectional long short-term memory (LSTM) model is used to learn the structural information of the code in the graph sequence to generate code vectors rich in structural features. Second, a two-stage attention mechanism enhances the embedded vectors by assigning different weight information to various code features during the code feature fusion phase, capturing significant feature information from different code feature sequences, resulting in code vectors rich in semantic and structural information. To validate the performance of the proposed code search algorithm, extensive experiments were conducted on two widely used code search datasets, CodeSearchNet and JavaNet. The experimental results show that the proposed SENCS algorithm improves the average code search accuracy metrics by 8.30 % (MRR) and 17.85% (DCG) and compared to the best baseline code search model in the literature, with an average improvement of 14.86% in the SR@1 metric. Experiments with two open-source datasets demonstrate SENCS achieves a better search effect than state of-the-art models. Full article

Forest fires not only cause severe damage to ecosystems and biodiversity but also directly threaten the safety of human societies. Given the significant increase in both the frequency and intensity of forest fires worldwide, especially under extreme climate conditions, efficient fire detection and initial attack (IA) are particularly critical. The initial attack is a key stage in forest fire control, and the time taken for fire detection is a crucial factor influencing the success of the initial attack. In response to the challenges of forest fire prevention and control, this study explores Unmanned Aerial Vehicle (UAV) cruising strategies, aiming to develop appropriate approaches based on regional characteristics and provide efficient periodic monitoring solutions for areas with high ecological value and challenging accessibility. By optimizing UAV patrol routes, this research seeks to maximize coverage in areas with lower initial attack success rates and significantly reduce fire detection time, thereby improving detection efficiency. We developed and applied four optimization strategies, random search, high-risk first (HRF), nearest high-risk first (NHRF), and a genetic algorithm-based (GA-based) strategy, to compare different UAV flight routes. To evaluate the deployment effectiveness of the four UAV cruise strategies, we introduced two evaluation metrics: Average Grid Risk (AGR) and Average Distance Risk (ADR). Experimental results showed that the NHRF and GA-based strategies performed better. Specifically, NHRF achieved the highest high-risk coverage, ranging from 51.5% to 71.3%, significantly outperforming the random search strategy (4–7%) and the HRF strategy (23.1–37.5%). The GA-based algorithm achieved the highest grid coverage, ranging from 30% to 59.8%, far surpassing the random search strategy (4–6.6%) and the HRF strategy (10.2–19.1%). Additionally, the NHRF and GA-based strategies delivered the best AGR and ADR performance, respectively. The application of these innovative strategies and evaluation metrics enhances forest fire prevention through periodic monitoring and supports more efficient firefighting efforts. Full article

Background: The integration of maternal and child health services (MCH) with routine immunization is an important global health strategy, particularly in low- and middle-income countries (LMICs). However, evidence is lacking regarding the best practices for service integration and the effect of integration on immunization and linked health service outcomes. Methods: We searched publication databases and gray literature for articles published between 2011 and 2020 that include approaches to integrating MCH services with immunizations during the first two years of life in LMICs. Abstracts and full-text articles were screened for eligibility. For the included articles, data extraction and analysis examined the descriptive characteristics of studies, outcomes, and implementation considerations. Results: Among the 16,578 articles screened, 44 met the criteria for inclusion, representing 34 studies, of which 29 were from Africa. The commonly linked MCH services were family planning (24%), human immunodeficiency virus (HIV) diagnosis or care (21%), and malaria prevention or control (21%). Multiple integration strategies were typically used; the co-location of linked services (65%), the provision of extra services by immunization staff (41%), and/or the provision of extra information by immunization staff (41%) were the most common. In general, integration improved MCH service outcomes (76%) and was either beneficial (55%) or neutral for immunization (35%), with some examples in family planning, malaria, and HIV where integrated services were not beneficial. Important implementation considerations included the careful matching of target populations in service re-design, ensuring support from policy, logistics, and information systems, the provision of adequate training and support of staff to avoid overload, clear client communication regarding service integration, and the need to address community concerns. Conclusions: Integrating MCH services with routine immunization can expand linked services and improve immunization coverage. This study has identified key implementation considerations relevant to both childhood and adult vaccination programs. More research is needed regarding costs and client preferences. Full article

In modern circuit design, the short-circuit problem is one of the key factors affecting routability. With the continuous reduction in feature sizes, the short-circuit problem grows significantly in detailed routing. Track assignment, as a crucial intermediary phase between global routing and detailed routing, plays a vital role in preprocessing the short-circuit problem. However, existing track assignment algorithms face the challenge of easily falling into local optimality. As a typical swarm intelligence technique, particle swarm optimization (PSO) is a powerful tool with excellent optimization ability to solve large-scale problems. To address the above issue, we propose an effective track assignment algorithm based on social learning discrete particle swarm optimization (SLDPSO-TA). First, an effective wire model that considers the local nets is proposed. By considering the pin distribution of local nets, this model extracts and allocates more segments to fully leverage the role of track assignment. Second, an integer encoding strategy is employed to ensure that particles within the encoding space range correspond one-to-one with the assignment scheme, effectively expanding the search space. Third, a social learning mode based on the example pool is introduced to PSO, which is composed of other particles that are superior to the current particle. By learning from various objects in the example pool, the diversity of the population is improved. Fourth, a negotiation-based refining strategy is utilized to further reduce overlap. This strategy intelligently transfers and redistributes wire segments in congested areas to reduce congestion across the entire routing panel. Experimental results on multiple benchmarks demonstrate that the proposed SLDPSO-TA can achieve the best overlap cost optimization among all the existing methods, effectively reducing congestion in critical routing areas. Full article

Context: The best treatment for end-stage chronic kidney disease (ESKD) is kidney transplantation (KT). As a result of an aging population, each year more kidney transplants in older adults are performed. Nevertheless, older recipients, characterized by more comorbidities and frailty, raise concerns about the outcomes, potential complications, and the general approach. Aim: The aim of this literature review was to study the outcomes, graft and patient survival, as well as common complications, to establish safety and increase awareness of the potential complications of kidney transplantation in the older population. Methods: PubMed and Google scholar databases were searched. The cut-off age defining an old patient was 60 years. The inclusion criteria were as follows: first kidney transplantation, and studies in English language. The exclusion criteria were as follows: more than one organ transplant, dual transplants, articles published before 2015, meta-analysis, reviews, letter to the editor, case reports, and studies published only as a conference abstract. Comparative and noncomparative studies addressing patient survival, death-censored graft survival, surgical complications, and clinical complications, such as delayed graft function (DGF) and biopsy proven acute rejection (PBAR), were included. Results: After screening the papers, 17 studies met the inclusion criteria and were included for review. Eleven papers compared older recipients with younger recipients and in six papers only older patients were analysed. Two studies used paired deceased donors to eliminate donor bias. The rest of the studies used either deceased donors or both living and deceased donors. The majority of patients were male (61.83%) and received a kidney from a deceased donor (58.08%). Conclusions: Kidney transplantation is safe and can be beneficial for recipients over 60 years of age. Older patients suffered more infectious complications, which were also one of the main reasons for death. Most studies did not show a significant difference in death-censored graft survival compared to the younger population. More research is needed to establish the prevalence of surgical complications, and some clinical complications. Full article

Background: The role of brain metastases (BM) surgery is of paramount importance for patients’ progression-free and overall survival. “En-bloc” and “piecemeal” resection represent the main surgical techniques. Although en-bloc resection remains the best surgical option, it is not widely adopted or feasible as the first choice. We describe our point of view about the en-bloc surgical technique with an illustrative case and discuss its indications with pros and cons through a comprehensive literature review. Materials and methods: A Medline search up to December 2023 in the Embase and PubMed online electronic databases was made and PRISMA statement was followed. An illustrative case of “en-bloc” resection from our surgical series was also added as a technical note. Results: We describe tips and tricks of our surgical technique and added a surgical video from our series. The literature review disclosed 19 studies. Resulting data suggested that “en-bloc” resection, when feasible, provides lesser risk of leptomeningeal dissemination, local recurrence rates, intraoperative bleeding occurrence and perioperative complications; in addition, it preserves the normal anatomy. Conclusions: En-bloc resection is the gold standard technique for surgical treatment of brain metastases especially for patients with superficial lesions that are small in size and far from eloquent areas. Full article