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In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.

Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer’s mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction.

Objective Thalassemia, an inherited hemoglobin synthesis disorder, imposes a significant health burden in Asian regions with high prevalence. Detailed patterns and trends of the disease across countries and territories within these regions remain underexplored. Our study focuses on the disease burden indices of thalassemia within the four GBD-defined Asian regions and the twenty-five included countries and territories. It provides insights into the gender-age distribution, temporal changes, and economic aspects of the thalassemia burden. Methods Data on thalassemia prevalence, incidence, mortality, and Disability-Adjusted Life Years (DALYs) were extracted from the Global Burden of Disease (GBD) 2021 study for South, East, Southeast, and high-income Asia regions, encompassing the relevant countries and territories from 1990 to 2021. The Average Annual Percent Change (AAPC) in age-standardized rates of thalassemia was determined to assess temporal trends. Age-gender cohort proportions were considered. The economic aspect of the disease burden and frontier analysis were evaluated using the GBD Socio-Demographic Index and Global Health Expenditure data. Results Southeast Asia exhibited notably high age-standardized mortality rate (ASMR), age-standardized prevalence rate (ASPR), and age-standardized DALYs rate among the four studied Asian regions in 2021. The East Asia region had recorded the highest age-standardized incidence rate (ASIR). A general decline in disease burden indices across the four regions from 1990 to 2021 was evident, with the exception of ASIR in Southeast Asia. The ASMR was highest among pediatric population under five years old, with a significant male preponderance. An unusual increase in ASMR was detected among females of childbearing age and the elderly within the studied region. Further analysis had identified six high-burden countries and territories, particularly those with low-middle Socio-Demographic Index (SDI) rankings and limited health expenditure. Conclusion Although the overall burden of thalassemia has decreased substantially, the disease burden was influenced by gender, age, geography, temporal trends, and economic factors in distinct manners. Based on the current SDI, many countries and regions still have greater improvement potential in the disease burden. There is a necessity for enhanced attention and resource allocation, particularly in low-middle and low SDI countries, with an emphasis on policies that promote early diagnosis and comprehensive care.

The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method.

The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper analyzes several typical problems in designing IoFR for real applications, including wireless communication support, monitoring targets of interest, serving a wireless sensor network, and collaborating with ground robots. In particular, an overview of the existing publications on the coverage problem, connectivity of flying robots, energy capacity limitation, target searching, path planning, flying robot navigation with collision avoidance, etc., is presented. Beyond the discussion of these available approaches, some shortcomings of them are indicated and some promising future research directions are pointed out.

This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm.

This paper proposes a novel parcel delivery system which consists of a drone and public transportation vehicles such as trains, trams, etc. This system involves two delivery schemes: drone-direct scheme referring to delivering to a customer by a drone directly and drone–vehicle collaborating scheme referring to delivering a customer based on the collaboration of a drone and public transportation vehicles. The fundamental characteristics including the delivery time, energy consumption and battery recharging are modelled, based on which a time-dependent scheduling problem for a single drone is formulated. It is shown to be NP-complete and a dynamic programming-based exact algorithm is presented. Since its computational complexity is exponential with respect to the number of customers, a sub-optimal algorithm is further developed. This algorithm accounts the time for delivery and recharging, and it first schedules the customer which leads to the earliest return. Its computational complexity is also discussed. Moreover, extensive computer simulations are conducted to demonstrate the scheduling performance of the proposed algorithms and the impacts of several key system parameters are investigated.

In this paper, a tunable broadband metamaterial absorber (MA) based on graphene is investigated theoretically and numerically at mid-infrared regions. Compared with the previously reported multiband graphene-based MAs, a broad bandwidth of 11.7 THz with the absorption over 90% is obtained in the proposed MA, which is composed of a Jerusalem cross (JC) metal encrusting into the slot graphene layer in the top layer. The results show that the origin of broadband absorption is caused by coupling effect between metal and graphene, and this effect is explained by the two-mode waveguide coupling theory. The tunability of MA is achieved via changing the external gate voltage to modify the Fermi energy of graphene. Further results show that the proposed MA can be used as the permittivity sensor with a high absorption. This work indicates that the proposed MA has the potential applications with respect to sensors and infrared absorbers.

Endometriosis is a debilitating, chronic disease that affects approximately 10% of women of reproductive age worldwide. The most common symptom is chronic pelvic pain, which leads to a reduced quality of life and requires lifelong treatment. The current standard of care for endometriosis is pain management, which consists mainly of medical and surgical treatment. Appropriate physical activity (PA) and exercise can help manage both physical and psychological symptoms of chronic conditions. Consequently, this systematic review and meta-analysis was designed to assess the effectiveness and safety of PA and exercise in women with endometriosis. We searched the published literature in Pubmed, Medline, Embase, The Cochrane Library, and Web of Science. Randomized controlled trials (RCTs) were obtained to assess the effects of physical activity and exercise on women with endometriosis. The random or fixed effects model was used to analyze the data in meta-analysis. The results were expressed as weighted mean differences (WMD) and their corresponding 95% confidence intervals (CIs). Six RCTs were identified in our systematic review, involving 251 patients. The results indicated that physical activity and exercise have a beneficial impact on quality of life, pain intensity, mental health, pelvic floor dysfunction, and bone density. However, due to the heterogeneity of the outcome measures and the incomplete reporting of the results in the studies included in this review, only a simple meta-analysis of two studies could be performed. The meta-analysis demonstrated that physical activity and exercise have a significant impact on the improvement of quality of life, particularly in the context of pain (P <0.0001), control and powerlessness (P <0.00001), and emotional well-being (P = 0.006). The present review indicates that physical activity and exercise have beneficial effects on the treatment of symptoms associated with endometriosis, particularly in terms of improving quality of life and providing pain relief. Due to the limitation in the quality of involved studies and the short duration of treatment, more RCTs with high-quality, long-term duration are needed for further validation. Systematic review registration: Registration number: CRD 42024547551.

Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable, especially in environments dangerous to humans, for example, in radiation or pollution-exposed areas. Two path-planning algorithms for reconnaissance and surveillance are proposed in this paper, which ensures every point on the target ground area can be seen at least once in a complete surveillance circle. Moreover, the geometrically complex environments with occlusions are considered in our research. Compared with many existing methods, we decompose this problem into a waypoint-determination problem and an instance of the traveling-salesman problem.