Current Trajectory Research Articles

The Use of Trajectories in Management Research: A Review and Insights for Future Research

The increased incorporation of time, theoretically and empirically, has dramatically advanced our understanding of management. Trajectories, which describe longitudinal change in constructs using functions of time, have become one of management scholars’ most widely used methodological tools to examine if, how, and why constructs change. Yet, despite the rapid growth of trajectory research, we lack a coherent framework for understanding how trajectories can be used to generate valuable insights into change-related work phenomena. In this article, we review more than two decades of trajectory research to develop a taxonomy that delineates the theoretical extensions offered by different trajectory-related research questions. In addition, although seminal method articles offered initial how-to instructions for conducting trajectory analyses, there has since been no systematic review of the methodological practices actually used in trajectory research. Our review distinguishes eight critical methodological choices required in every trajectory study, catalogs the frequency of methodological choices made in prior research, identifies problematic practices that persist, and offers actionable recommendations to develop rigorous trajectory research. Finally, we discuss eight pressing issues in current trajectory research, including several prominent practices that must be abandoned, and suggest how to avoid or minimize these concerns in future studies. Together, our review provides management scholars with an organizing platform for developing trajectory-related research questions and a comprehensive guide to rigorous study designs and analyses.

Evaluating achieving tobacco control targets in Belgium using modelled projections

Abstract Background Tobacco use in Belgium remains a key public health concern with an estimated 15% of adults reporting daily tobacco use in 2018. In 2022, the Belgian government launched the “Interfederal Strategy 2022-2028 for a Smoke-free Generation.” The strategy outlines a number of targets for reducing daily tobacco use among adults 15+ years and young adults 15-24 years. We evaluate the feasibility of achieving these targets given current daily smoking trajectories at a national and regional level. Methods We modelled time trends of smoking in Belgium based on repeated cross-sectional data taken from the nationally representative Belgian Health Interview Survey. We applied a Bayesian inference model, the integrated nested Laplace approximation, to obtain a time series of daily tobacco use for adults by age, sex, and region of Belgium from 1997 to 2018. We then projected this model to 2070 using population projections from the Belgian Federal Planning bureau for population distributions. We compared trajectories with targets from the Interfederal Strategy to assess whether they are likely to be achieved. Results The national target of reducing daily smoking among adults 15+ years to 10% by 2028 and 5% by 2040 will not be achieved on current trajectories. Wallonia has the highest level of people smoking daily and is projected to miss the targets by the largest margin. Flanders is expected to reach the 10% threshold by 2032. The target for reduction of prevalence of daily smoking among youth 15-24 years to 6% by 2028 will not be achieved before 2040. Smoking among the youngest age group (15-24 years) is decreasing rapidly with prevalence of daily smoking in that age group projected to be < 2% in Flanders by 2059. Conclusions No region of Belgium is expected to achieve targets for reduction in daily smoking given current trajectories. Tobacco control policies in the country should be re-assessed, targeted and intensified. Key messages • The Belgian national target of 5% daily smoking prevalence in adults by 2040 will not be achieved. • Differences in smoking trajectories in Belgium exist by age and region; tobacco control policies should be targeted.

Understanding the challenge of decoupling transport-related CO2 emissions from economic growth in developing countries

The transition to net zero requires full decarbonization of the transport sector, currently one of the leading sources of emissions globally. Transport-related carbon emissions are growing fastest in the developing world. This makes it particularly critical to understand whether low- and middle-income countries are making progress towards decoupling transport sector emissions from economic growth, as well as the extent to which their current emissions trajectory is being shaped by structural factors versus more amenable policy choices. This paper assembles and analyzes a comprehensive dataset on transport-related emissions with a uniquely broad coverage of developing countries. The paper employs the Tapio decoupling model over the period 1990–2018 to demonstrate that high-income countries are almost twice as likely to have reached relative decoupling as low and middle-income countries (70 vs 36 percent, while the latter are almost twice as likely to be in a state of negative decoupling as the former (17 versus 41 percent). This paper conducts index-decomposition and econometric analysis to shed light on the factors driving transport-related carbon emissions. Index decomposition reveals that there have been only relatively modest reductions in the transport emissions intensity of GDP since 1990 and that these have not been large enough to offset economic growth in middle-income countries and demographic growth in low-income countries. Regression analysis further shows that urbanization and industrialization are important correlates of transport-related emissions, while the correlation of policy choices with reduced emissions is rather weak.

Phlebotomine sand flies in rural Mayan communities of Southern Mexico: The heterogeneity of the ruralscape increases the entomological risk

In the Yucatan Peninsula, tropical preserved forests harbor a high diversity of phlebotomine sand fly species, potential vectors of Leishmania. As a result, a significant risk of contracting localized cutaneous leishmaniasis (LCL) is associated with entry into these forest ecosystems. However, with the current trajectory of land use in the region, communities now live within modified landscapes which could increase their risk of contact with vectors. In this study, we characterized the sand fly fauna and its relationship with the characteristics of peridomestic habitats in two rural communities in Yucatan characterized by contrasting records of leishmaniasis. Five human dwellings in each community were randomly selected and the peridomestic landscape was characterized (i.e., type and percentage of land use). For the collection of sand flies, CDC traps were deployed in both the intra and peridomicile. Differences in alpha and beta diversity between communities and ecotopes were estimated considering three separate groups, all species together, zoophilic species, and anthropophilic species. In total, 1, 241 sand flies represented by eight species were collected from November 2021-March 2022. Both communities registered the presence of sand flies in the intra and peridomicile, and females were more abundant than males. However, the greatest diversity of species was recorded in the peridomicile of the community with the highest composition of land uses. Our study demonstrates that the transformation of the forest around the communities favors the abundance and richness of sand flies in the peridomestic environment. We discuss the limitations and implications of our findings regarding the potential risk of the emergence of peri‑rural cycles of leishmaniasis in the region.

The Rise of China and its Interplay with the Russian and Turkish Regimes

How does the rise of China challenge the existing international order? What are the possible outcomes of the interplay between China’s rise and the current trajectory of the Russian and Turkish regimes? Based on a qualitative analysis, I find twomajor dilemmas for the current power dynamics have resulted from China’s challenging behavior in the international system. First, while China promotes steady relationships based on mutual interests facilitated by the rule-based international order, it also carries out actions which feed its aspirations for exploiting the current international institutions. Second, while China underlines the importance of a Westphalian style of sovereignty and non-interference, it also takes interventionist actions in both cyberspace and territorial space. Based on identification of ChinaRussia and China-Turkey interactions through the variations in the most ‘central’ domestic issues of the current governments in Russia and Turkey, I predict four possible outcomes, shedding light on the future policy directions in Russia and Turkey: 1) a prolongation of Putin’s campaign in Ukraine, 2) an acceleration in the process of de-dollarization, 3) lasting Chinese influence on Turkey’s Kurdish issue, and 4) an increase in Turkey’s tendency toward a Chinese-style ‘growth’ model.

Life cycle climate performance evaluation (LCCP) of electric vehicle heat pumps using low-GWP refrigerants towards China's carbon neutrality

The environmental-friendly heat pump with low global warming potential (GWP), such as R1234yf, CO2, R290, and several potentially promising refrigerant mixtures, is increasingly essential for the electric vehicle (EV) to save energy consumption and extend the driving range. It is beneficial to achieve carbon neutrality by reducing both direct and indirect carbon emissions. Previously, the life cycle climate performance (LCCP) was a widely accepted metric to evaluate the carbon footprint of mobile air conditioning systems “from cradle to grave” for the internal combustion engine vehicle (ICEV), however, such LCCP analyses about EV heat pumps can hardly be found. To facilitate the EV industry and policymakers' better understanding of the environmental impacts of those low-GWP refrigerants, this study provided a comprehensive LCCP analysis for the EV heat pumps based on the system bench test results, local climates, local power supply characteristics, real-world driving patterns, vehicle cabin thermal sensation, and climate control load. Five low-GWP refrigerants, i.e., R1234yf, CO2, R290, binary blends of CO2 and R41 (with GWP values of 49), M2(R410A substitute with GWP values of 137), were compared against R134a and R410A in 31 provinces of mainland China. We also considered the impacts of electric vehicle adoption rates and the carbon intensity of electricity from 2020 to 2060. Results show that the total life-cycle emissions per vehicle are highly related to climate conditions in different provinces, while R290 shows the lowest emission on the national average. The national mean life-cycle emissions per vehicle are projected to decrease 14% by 2030 and 45.4% by 2060 under the current technological trajectory of electricity generation. By using low-GWP heat pump systems in EVs, the cumulative emissions counting all vehicles can be saved up to 1450 Mt. CO2e from 2020 to 2060.

Parallel Imports and the International Principle of Exhaustion of Rights under Sanctions

In the modern world, parallel imports implemented in compliance with the international principle of exhaustion of intellectual property rights can and should become a mechanism for developing free trade, ensuring international competition and protecting the interests of consumers around the world. The sanctions adopted in early 2022 and imposed against the Russian Federation, as well as suspension of the activities of a number of foreign companies in the Russian Federation, encouraged the author to examine the current trajectories of introducing the international principle of exhaustion of rights into legislation and the possibility of parallel imports applied in order to provide Russian consumers with goods that have ceased to be available on the domestic market. The author reviews legislative regulatory acts adopted in Russia for the purpose of ensuring parallel imports and implementing the international principle of exhaustion of rights, analyzes possible problems of its implementation and ways to solve the problems under consideration, examines foreign experience, international legal and regional foundations for the introduction of the exhaustion of rights regime.

Solid-State Cholesteric Liquid Crystals as an Emerging Platform for the Development of Optical Photonic Sensors.

Over the past decade, solid-state cholesteric liquid crystals (CLCsolid ) have emerged as a promising photonic material, heralding new opportunities for the advancement of optical photonic biosensors and actuators. The periodic helical structure of CLCsolid s gives rise to their distinctive capability of selectively reflecting incident radiation, rendering them highly promising contenders for a wide spectrum of photonic applications. Extensive research is conducted on utilizing CLCsolid 's optical characteristics to create optical sensors for bioassays, diagnostics, and environmental monitoring. This review provides an overview of emerging technologies in the field of interpenetrating polymeric network-CLCsolid (IPN) and CLCsolid -based optical sensors, including their structural designs, processing, essential materials, working principles, and fabrication methodologies. The review concludes with a forward-looking perspective, addressing current challenges and potential trajectories for future research.

Speculative Political Ecologies: (re)imagining urban futures of climate extremes

What role can a speculative political ecology play in (re)imaging urban futures of climate extremes? In recent years, narratives of dystopian futures of climate extremes have proliferated in geosciences, and across the media and creative arts. These anxiety-fueled narratives often generate a sense of resignation and unavoidability, which contributes to foreclosing the possibility of radically different political projects. In this article, we argue that these narratives conceal the coproduction of nature and society and treat nature as the problem, thereby locking futures into dystopic configurations. Political ecology scholarship can contribute to generate a politics of possibility by reconceptualizing the relations that constitute urban futures under climate extremes as socionatural. This, we argue, calls for a more experimental political ecology and new forms of theorizing. To this aim, we develop a speculative political ecological approach grounded on a numerical model that examines the potential of transformative change in the aftermath of extreme flood events in a capitalist city. Analytically, this opens a unique possibility of exploring urban futures beyond current trajectories, and how these alternative futures might transform vulnerability and inequality across urban spaces. From a policy perspective, we lay the foundations for a new generation of models that apprehend the role of power and agency in shaping uneven urban futures of climate extremes.

Mapping the Research Landscape: A Bibliometric Study of Fuel Cell Technologies and Microgrid Systems

The potency of renewable energy sources in power systems, particularly for rural areas, represents a transformative force that addresses energy challenges and brings about sustainable development. Fuel cells offer a transformative role in providing electricity to rural areas, addressing a reliable and off-grid power solution. This study aims to map the research landscape through the bibliometric study of fuel cell technologies and microgrid systems. The analysis includes author analysis, affiliation analysis, country or territory analysis, source analysis, keyword analysis, trend topic analysis, and thematic map analysis. Bibliometric analysis aids policymakers, industry professionals, and academics navigate the wealth of information, ensuring that fuel cell and microgrid integration efforts align with current research trajectories and contribute meaningfully to improving clean and resilient energy solutions. The results would be a valuable compass, guiding stakeholders through the expansive and dynamic landscape of fuel cell and microgrid research, facilitating evidence-based decision-making, and fostering a deeper understanding of the field’s trajectory.

Helicopter radio system for low altitudes and flight speed measuring with pulsed ultra-wideband stochastic sounding signals and artificial intelligence elements

The subject matter of this study is algorithms for measuring the components of an aircraft speed vector and altitude. The goal of this study is to improve algorithms for processing wideband stochastic pulse signals in helicopter low-altitude and flight-speed radio systems by introducing secondary signal processing based on artificial intelligence elements. The tasks to be solved are as follows: to develop an optimal algorithm for determining the speed and altitude of flight for a helicopter radio complex; to supplement the signal processing algorithm with an artificial intelligence-based processor to determine the "safety" of the current trajectory; provide the pilot with relevant information about possible options for further actions based on an analysis of the current position of the helicopter and flight parameters; and to analyse the efficiency of the proposed complex when using various artificial intelligence-based algorithms. The methods used are as follows: methods of mathematical statistics and optimal solutions for solving problems of statistical synthesis of active radio complex structure; methods of machine learning; and methods of computer simulation. The following results were obtained. The algorithms for signal processing in a helicopter radio complex are obtained by the method of maximum likelihood, and the use of three radio channels to calculate the full vector of speed and altitude is argued. The structure of a secondary information processing system, using algorithms based on artificial intelligence is proposed. The effectiveness of determining the safety of the current landing trajectory using various algorithms based on artificial intelligence (LinearSVC, GaussianNB, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, MLPClassifier and RidgeClassifier) was analysed. Conclusions. The simulation results show that in the presence of accurate (noise-free) information on the current location of the helicopter, its axial velocities, and a map of the terrain with defined areas dangerous for landing, the DecisionTreeClassifier and RandomForestClassifier algorithms can provide a high probability of correctly determining the safety of the current landing trajectory. At the same time, in the presence of instability in the measurements of helicopter movement parameters, only the RandomForestClassifier algorithm maintains high accuracy.

An Open-Circuit Fault Diagnosis Method Based on Adjacent Trend Line Relationship of Current Vector Trajectory for Motor Drive Inverter

This paper proposes a power switch open-circuit (OC) fault diagnosis method for a motor drive inverter system. This method utilizes adjacent trend lines to extract fault features from the current vector trajectory, enabling rapid fault diagnosis and localization. Firstly, the current vector trajectory is obtained by applying the Clark transformation to the three-phase currents, and trend line slopes are calculated using equidistant current signal nodes. Subsequently, the method determines fault by calculating changes in adjacent trend line slopes and quickly locates the faulty power switch by considering the average slope of adjacent trend lines and the beta-axis current direction in the alpha–beta coordinate system. Accurate OC fault localization can be achieved with just a few trend line data points calculated in each current cycle, reducing the extra hardware and computational burden. This method is not only suitable for load variations but also applicable to the diagnosis of both single-switch and multiple-switch faults. Finally, simulation and experimental results validate the effectiveness and robustness of the method.

The Brain Health Imperative in the 21st Century-A Call to Action: The AAN Brain Health Platform and Position Statement.

Brain health is crucial to optimizing both the function and well-being of every person at each stage of life and is key to both individual and social progress. As a concept, brain health is complex and requires a multidisciplinary collaborative approach between many professional and public organizations to bring into effect meaningful change. Neurologists are uniquely positioned to serve as specialists in brain health and to advance the newly evolving field of preventive neurology, which aims to identify individuals at high risk of brain disorders and other neurologic conditions and offer strategies to mitigate disease emergence or progression. For decades, the American Academy of Neurology (AAN) has demonstrated a commitment to brain health through its public outreach and advocacy. The AAN's Brain Health Initiative launched in 2022 with a strategic plan prioritizing brain health as a key aspect of public engagement and positioning the AAN and neurologists as champions of brain health in collaboration with a broad range of other brain health providers. In this study, we present (1) the new definition of brain health developed by the AAN for neurologists, patients, partners in health care, and the public; (2) the strategic objectives of the AAN Brain Health Initiative; and (3) the AAN Brain Health Platform and Action Plan framework, including key positions on brain health, its 3 ambitious goals, and a national brain health vision. The top-line priorities of the AAN Brain Health Action Plan highlight the need for research, education, public policy, and direct-to-public messaging across the individual's life span and will serve as a catalyst for future cross-disciplinary collaborations within each epoch and longitudinally. The AAN Brain Health Platform is designed to communicate the AAN's vision for brain health and provide a blueprint toward achieving the future of optimal brain health across the life span for all. Through this position statement, we call upon neurologists and other stakeholders in brain health to join our collective efforts to accomplish the ultimate goal of transforming the current trajectory of public health of an increasing burden of neurologic disorders-from both illness and injury-to achieving optimal brain health for all.

Business model pivoting and digital technologies in turbulent environments

PurposeThis paper aims to draw on the Dynamic Capabilities View to discuss how small and medium enterprises (SMEs) use digital technologies to develop digital capabilities that will enable them to change their current business model and trajectory, that is, to pivot-within turbulent environments, and subsequently to survive and grow.Design/methodology/approachThe authors collected and analysed qualitative data from 26 SMEs in South-East England that have used digital technologies to pivot during the pandemic. The data was collected via in-depth semi-structured interviews. The authors analysed the data by creating first-order concepts, second-order themes, and aggregating dimensions.FindingsThe findings suggest that (1) digital technologies enable pivoting by facilitating the creation of the following digital capability types: “digital sensing”, “digital seizing” and “digital transforming”; (2) Each of these digital capability types is underpinned by micro-foundations (sub-capabilities) and shaped by the digital culture of the organisation. (3) these capabilities are triggered by the turbulent environment and the existing digital technologies, and are shaped by the digital culture.Originality/valueThe authors contribute to the literature of digital entrepreneurship as the authors illustrate (1) how the micro-foundations of digital capabilities, as facilitated by digital technologies, assist pivoting; and (2) the process from key activities during pivoting to second-order themes that represent micro-foundations to digital (dynamic) capabilities for pivoting in turbulent environments. The study highlights the importance of digital pivoting for businesses in the UK Southeast that have many aspirations for growth and innovation, whilst striving to address multiple challenges including digital divide and literacy, inflation and cost of living crisis, as well as supply chain issues.

Leveraging Multimodality for Biodiversity Data: Exploring joint representations of species descriptions and specimen images using CLIP

In recent years, the field of biodiversity data analysis has witnessed significant advancements, with a number of models emerging to process and extract valuable insights from various data sources. One notable area of progress lies in the analysis of species descriptions, where structured knowledge extraction techniques have gained prominence. These techniques aim to automatically extract relevant information from unstructured text, such as taxonomic classifications and morphological traits. (Sahraoui et al. 2022, Sahraoui et al. 2023) By applying natural language processing (NLP) and machine learning methods, structured knowledge extraction enables the conversion of textual species descriptions into a structured format, facilitating easier integration, searchability, and analysis of biodiversity data. Furthermore, object detection on specimen images has emerged as a powerful tool in biodiversity research. By leveraging computer vision algorithms (Triki et al. 2020, Triki et al. 2021,Ott et al. 2020), researchers can automatically identify and classify objects of interest within specimen images, such as organs, anatomical features, or specific taxa. Object detection techniques allow for the efficient and accurate extraction of valuable information, contributing to tasks like species identification, morphological trait analysis, and biodiversity monitoring. These advancements have been particularly significant in the context of herbarium collections and digitization efforts, where large volumes of specimen images need to be processed and analyzed. On the other hand, multimodal learning, an emerging field in artificial intelligence (AI), focuses on developing models that can effectively process and learn from multiple modalities, such as text and images (Li et al. 2020, Li et al. 2021, Li et al. 2019, Radford et al. 2021, Sun et al. 2021, Chen et al. 2022). By incorporating information from different modalities, multimodal learning aims to capture the rich and complementary characteristics present in diverse data sources. This approach enables the model to leverage the strengths of each modality, leading to enhanced understanding, improved performance, and more comprehensive representations. Structured knowledge extraction from species descriptions and object detection on specimen images synergistically enhances biodiversity data analysis. This integration leverages textual and visual data strengths, gaining deeper insights. Extracted structured information from descriptions improves search, classification, and correlation of biodiversity data. Object detection enriches textual descriptions, providing visual evidence for the verification and validation of species characteristics. To tackle the challenges posed by the massive volume of specimen images available at the Herbarium of the National Museum of Natural History in Paris, we have chosen to implement the CLIP (Contrastive Language-Image Pretraining) model (Radford et al. 2021) developed by OpenAI. CLIP utilizes a contrastive learning framework to recognize joint representations of text and images. The model is trained on a large-scale dataset consisting of text-image pairs from the internet, enabling it to understand the semantic relationships between textual descriptions and visual content. Fine-tuning the CLIP model on our dataset of species descriptions and specimen images is crucial for adapting it to our domain. By exposing the model to our data, we enhance its ability to understand and represent biodiversity characteristics. This involves training the model on our labeled dataset, allowing it to refine its knowledge and adapt to biodiversity patterns. Using the fine-tuned CLIP model, we aim to develop an efficient search engine for the Herbarium's vast biodiversity collection. Users can query the engine with morphological keywords, and it will match textual descriptions with specimen images to provide relevant results. This research aligns with the current AI trajectory for biodiversity data, paving the way for innovative approaches to address conservation and understanding of our planet's biodiversity.

Advancements and hurdles of deeper‐offshore aquaculture in China

AbstractOpen offshore areas boast strong physical self‐purification capacity and abundant non‐fossil energy resources, such as wind, waves, and solar energy. Consequently, the global community anticipates nearshore aquaculture to transition towards offshore to help increase production, alleviate eutrophication, and reduce greenhouse gas emissions. To date, China has constructed over 40 sets of deeper‐offshore aquaculture (DOA) infrastructures, encompassing various types of pens, cages and closed containment systems. Although DOA holds vast potential to address food security and aquaculture sustainability in China, its current development trajectory struggles to meet those goals and primarily achieves profitability by focusing on high‐value species or products. For DOA to realize its potential, innovative production systems must tackle three key contradictions: enterprise profitability versus product affordability, clean energy‐based products versus carbon‐intensive products, and automated operation versus re‐employment of coastal fish farmers. Resolving these contradictions requires the development of a large‐scale, anti‐typhoon offshore enclosure that integrates mariculture with other industries, such as wind farming, food processing, and tourism. This approach will foster a sustainable balance between profitability, environmental impact, and employment opportunities in the sector.

Algorithm for planning energy-efficient local trajectories of ground-based robotics, considering the global route

In this paper, an algorithm is presented that provides dynamic planning of a local trajectory of movement based on the data from the 3D lidar, considering a predetermined global route on rough terrain. The proposed local planning algorithm considers the features of the LRLHD-A* global energy-efficient trajectory planning algorithm previously developed by the authors. The novelty of the proposed solution lies in the algorithm for generating a graph of the surrounding space from the data obtained from a three-dimensional lidar installed on a mobile robot with O(nlogn) complexity, which reduces the time to build a local trajectory. The developed solution makes it possible to combine the processes of constructing a local and global trajectory of movement by using the energy-efficient algorithm for constructing trajectories LRLHD-A* as the basis. During the experiments, it was revealed that when choosing a step of 0.5 m for a local grid of 10 × 10 m in size, the time for constructing the grid and the trajectory does not exceed 0.1 s. The developed algorithm can be used as a local scheduler in close to real time mode, providing prompt re-sponse to various obstacles on the global trajectory of the robot, considering the features of the terrain and the cur-rent global trajectory.

Zero-Backflow Power Control Scheme of Dual Bridge Series Resonant DC–DC Converters With High-Accuracy Time Domain Modeling

In the fundamental harmonic approximation (FHA) modelling of dual bridge series resonant DC-DC converters (DBSRCs), the approximation error of the FHA model will lead to low accuracy of optimization schemes, which will reduce the efficiency of DBSRCs. In this paper, firstly, the time domain analysis (TDA) modelling of the DBSRC is adopted, which can describe the characteristics of the DBSRC accurately. Secondly, the extended phase shift (EPS) modulation based on the TDA modelling is discussed, and the backflow power model with the TDA of the DBSRC is developed. On this basis, a zero-backflow power optimization scheme is proposed. In addition, the effects of dead time are analyzed and the compensation method is proposed. The proposed backflow power optimization method with dead time compensation can achieve zero-backflow power and improve the efficiency of the DBSRC. Especially, the proposed method with TDA modelling is more excellent when DBSRC operates in wide voltage range conditions. Finally, a comprehensive experiment comparison of DBSRC under the minimum current trajectory (MCT) with FHA modelling and the proposed method with TDA modelling is discussed. The experimental results have verified the effectiveness of the proposed scheme.

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. A power emergency material distribution model with priority conditions is proposed in this paper, along with a two-layer dynamic task-solving framework that takes task dynamics into account. This research proposes an algorithm (TD3PSO) that combines the particle swarm algorithm (PSO) updating technique with the double-delay depth deterministic policy gradient algorithm (TD3) algorithm’s capacity to dynamically parameterize. The final task allocation experiment demonstrates that the modified TD3PSO significantly outperforms the conventional algorithm on the Solomon data set, with an improvement of 26.3% on average over the RLPSO algorithm and a 11.0% reduction in the volatility of the solving impact. When solving under realistic circumstances, the solution effect increases by 1.6% to 13.4%, and the redistribution experiment confirms the framework’s efficacy. As a result, the algorithm and architecture suggested in this paper may successfully address the issue of scheduling drones for power emergencies while enhancing transportation efficiency.

The Effect of Commencing Rotating Shift Work on Diet and Body Composition Changes in Graduate Paramedics: A Longitudinal Mixed Methods Study

Objectives: Diet quality often changes as shift workers adjust to atypical work schedules, however, limited research exists examining the early effects of starting rotating shift work on diet and body composition. This study explored dietary behavior changes occurring in graduate paramedics during the first year of exposure to rotating shift work, and investigated dietary intake, diet quality and anthropometric changes over two years. Methods: Participants from a graduate paramedic cohort in Melbourne, Australia were approached after two years of shift work for study inclusion. Using a mixed method study approach, the qualitative component comprised individual in-depth interviews to explore perceived dietary behavior changes experienced over the first year of shift work. Interview transcripts were thematically analyzed and guided by the COM-B model (capability, opportunity, motivation, and behavior) and theoretical domains framework (TDF). Diet quality and dietary intake were quantitatively assessed by the Australian Eating SurveyTM at baseline, one year, and two years, along with body weight, waist circumference, and body mass index (BMI) to monitor changes. Results: Eighteen participants were included in the study. From the interviews, participants reported: 1. food choices are driven by wanting to fit in with coworker food habits, 2. food choices and mealtimes are unpredictable and 3. paramedics try to make healthy food choices but give in to less healthy options. While daily energy intake and diet quality scores did not differ in the first two years of shift work, daily energy from takeaway foods significantly increased (mean difference (MD): 2.96% EI; 95% CI: 0.44 − 5.48; p = 0.017) and increases in weight (MD: 2.96 kg; 95% CI: 0.89-5.04; p = 0.003), BMI (MD: 1.07 kg/m2; 95% CI: 0.26 – 1.87; p = 0.006) and waist circumference (MD: 5.07 cm; 95% CI: 1.25-8.89; p = 0.006) were also evident at two years. Conclusions: This study contributes new information on dietary changes and the current early trajectory of unintentional weight gain and takeaway reliance occurring within a graduate paramedic cohort over two years of shift work. To reduce the unintended metabolic consequences commonly observed with rotating shift schedules, workplaces could improve access to healthier food options and enable behavioral support/education to address nutrition-related health risks.