bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study
Authors: Liu Xiaoni , Lai Rui , Wu Chaoling , Yan Changjian , Gan Zhe , Yang Yaru , Zeng Xiangtai , Liu Jin , Liao Liangliang , Lin Yuansheng , Jing Hongmei , Zhang Weilong
Published: May 2024

Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natural language processing tool powered by artificial intelligence technology, is increasingly utilized in medicine. To facilitate and expedite patients’ navigation to the appropriate department, we conducted an outpatient triage evaluation of ChatGPT. For this evaluation, we posed 30 highly representative and common outpatient questions to ChatGPT and scored its responses using a panel of five experienced doctors. The consistency of manual triage and ChatGPT triage was assessed by five experienced doctors, and statistical analysis was performed using the Chi-square test. The expert ratings of ChatGPT’s answers to these 30 frequently asked questions revealed 17 responses earning very high scores (10 and 9.5 points), 7 earning high scores (9 points), and 6 receiving low scores (8 and 7 points). Additionally, we conducted a prospective cohort study in which 45 patients completed forms detailing gender, age, and symptoms. Triage was then performed by outpatient triage staff and ChatGPT. Among the 45 patients, we found a high level of agreement between manual triage and ChatGPT triage (consistency: 93.3–100%, p<0.0001). We were pleasantly surprised to observe that ChatGPT’s responses were highly professional, comprehensive, and humanized. This innovation can help patients win more treatment time, improve patient diagnosis and cure rates, and alleviate the pressure of medical staff shortage.
Long-acting prescriptions and therapy for HIV-1 from market launch to the present in Germany (May 2021 to December 2023)
Authors: Schmidt Daniel , Kollan Christian , Stoll Matthias
Published: May 2024

Background

In Europe, the combination of cabotegravir (CAB) with rilpivirine (RPV) has been approved as a dual injection long-acting (LA) therapy for the treatment of human immunodeficiency virus type 1 (HIV-1) infections in adults since December 2020. Studies have shown that between 36 and 61% of people living with HIV (PLWHIV) prefer LA therapy. However, there are no real-world data on the number of people receiving LA therapy, in Germany or internationally. The aim of this study was to assess the current situation and trends in usage of LA therapy for the treatment of HIV-1 in Germany.

Methods

Based on pharmacy prescription data derived from Insight Health, the monthly number of prescriptions for oral CAB, CAB-LA, and RPV-LA over the entire period of availability in Germany was analyzed and evaluated (May 2021 to December 2023). The number of 1st and 2nd initiation injections and subsequent maintenance injections was calculated on the basis of the prescriptions for oral CAB initiation.

Results

The bimonthly schedule resulted in two growing cohorts from September 2021 with an estimated 14,523 CAB-LA prescriptions over the entire period. Accordingly, in December 2023, there were approximately 1,364 PLWHIV receiving LA therapy, of whom 1,318 were receiving maintenance therapy. Only treatments with bimonthly regimens were carried out. Accounting for people not covered by statutory health insurance (~13%), a total of ~1,600 PLWHIV were receiving LA therapy in Germany in December 2023. The average rounded annual cost of therapy in 2023 was €11,940 (maintenance therapy with initiation) and €10,950 (maintenance therapy without initiation).

Conclusion

To our knowledge, this is the first study of real-world use and number of people receiving LA therapy. A strength of our study is the nearly complete coverage of people with statutory health insurance in Germany. The predicted demand for LA therapy does not match the actual number of people receiving LA therapy. Although the number of PLWHIV receiving LA therapy increased steadily, they accounted for just under 2% of the estimated total number of people receiving HIV therapy in Germany in 2023, almost 2 years after the market launch. No significant increase in prescriptions is expected; on the contrary, the trend is leveling off and is unlikely to change drastically in the near future. Hence, the need for this mode of therapy in Germany appears to be limited. Follow-up studies at regular intervals on the further course would be useful and are recommended, as well as investigations into the possible reasons for the slow uptake to inform public health experts and possibly broaden treatment options.
Hate speech detection with ADHAR: a multi-dialectal hate speech corpus in Arabic
Authors: Charfi Anis , Besghaier Mabrouka , Akasheh Raghda , Atalla Andria , Zaghouani Wajdi
Published: May 2024

Hate speech detection in Arabic poses a complex challenge due to the dialectal diversity across the Arab world. Most existing hate speech datasets for Arabic cover only one dialect or one hate speech category. They also lack balance across dialects, topics, and hate/non-hate classes. In this paper, we address this gap by presenting ADHAR—a comprehensive multi-dialect, multi-category hate speech corpus for Arabic. ADHAR contains 70,369 words and spans four language variants: Modern Standard Arabic (MSA), Egyptian, Levantine, Gulf and Maghrebi. It covers four key hate speech categories: nationality, religion, ethnicity, and race. A major contribution is that ADHAR is carefully curated to maintain balance across dialects, categories, and hate/non-hate classes to enable unbiased dataset evaluation. We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. Extensive qualitative and quantitative analyses demonstrate the quality and usefulness of ADHAR. Our experiments with various classical and deep learning models demonstrate that our dataset enables the development of robust hate speech classifiers for Arabic, achieving accuracy and F1-scores of up to 90% for hate speech detection and up to 92% for category detection. When trained with Arabert, we achieved an accuracy and F1-score of 94% for hate speech detection, as well as 95% for the category detection.
Threat modelling in Internet of Things (IoT) environments using dynamic attack graphs
Authors: Salayma Marwa
Published: May 2024

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator’s control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs.
Supporting health and wellbeing in health care employees: a documentary review of organizational policies, strategies and frameworks
Authors: Hoyle Louise Patricia , Ryde Gemma Cathrine , Coulter Jamie , Rollason Jennie
Published: May 2024

Background

Health care workers are crucial for a productive and thriving health care system, yet the health and lifestyle behaviour of key groups within this workforce (for example nurses and healthcare assistants/support workers) is typically poor. The extent of health and wellbeing documents that guide action towards improving their health and wellbeing is unknown. Using one health care system, NHS Scotland, as an example, the aim of this study was to assess the number of NHS health boards with workplace documents focused on health and wellbeing of employees, the quality of these documents and the extent to which they reference lifestyle behaviours, namely physical activity.

Methods

Documentary analysis was undertaken on employee health and wellbeing policies (and wider documents). These were sourced through online searches on Google search engine and Freedom of Information Requests sent to all 14 Scottish NHS Health Boards. Titles and content were assessed for relevance to employee health and wellbeing. Content analysis was used to analyse the included documents against eight predefined codes.

Results

Thirteen documents were retrieved with 11 of the 14 Health Boards having at least one relevant document. The content varied greatly between documents with regards to how many reported the eight codes and the quality of content within these. Nine documents mentioned physical activity but mainly in relation to current activities rather than in the context of a future healthy workforce.

Conclusions

Despite the importance of a healthy, health care workforce, more work is needed to ensure high level documents are able to support these efforts, especially with reference to lifestyle behaviours.
Uptake of climate-smart agricultural technologies and practices: a three-phase behavioral model
Authors: Khademi Noshabadi S. M. , Omidi Najafabadi M. , Mirdamadi S. M.
Published: May 2024

Climate change due to human activities is a reality and a growing threat to global food security. The trend of increasing temperature and decreasing rainfall in Iran shows climate warming, the result of climate change in Iran. Climate-smart agriculture (CSA) is an approach to developing new agricultural practices that support food security against climate change. The purpose of this study was to design a behavioral model for using CSA technologies in three phases: cognitive, motivational, and volitional, using the theories of planned behavior, self-determination, and social cognition. This research is the first to combine behavioral models to understand the application of CSA technologies. The research method in this study was quantitative and non-experimental. The statistical population included 800 wheat farmers from the city of Nazarabad in the Alborz province of Iran. A sample size of 260 people was determined using the Cochran formula. Appropriate stratified sampling was selected as the sampling method. Wheat farmers were then randomly selected in each village through proportional assignment. The research data was collected through a cross-sectional survey and a questionnaire. The models and hypotheses were tested using structural equation modeling. The statistical results confirmed the theoretical model and 15 research hypotheses. Additionally, the estimated field model explained about 62% of the variance in the behavior of using CSA technologies. Therefore, cognition, motivation, and volition effectively shape the behavior of using CSA technologies. In the cognitive phase, environmental concerns have the greatest impact on attitude toward the behavior of using CSA technologies (β = 0.714). In the motivational phase, personal norms have the most impact on the intention to use CSA technologies (β = 0.643). Finally, in the volitional phase, the intention to use CSA technologies has the highest impact on the behavior of using CSA technologies (β = 0.386). The increase in the guaranteed purchase price of wheat and paying soft and long-term loans by the policymakers will lead to an increase in the intention of wheat farmers to use CSA technologies.
Perceived risk and risk reduction behaviors in fish and seafood consumption
Authors: An Soyoung , Eck Thomas , Choi Jinkyung
Published: May 2024

This study aimed to explore what risk factors consumers perceive when consuming fish and seafood at a time when treated wastewater was being discharged. Moreover, this study attempted to examine what risk reduction behaviors consumers take to minimize these risk factors. The data was collected online for one week in September 2023 and 334 responses from Korean consumers were used for the analysis. Five risk dimensions were identified: biochemical risk, hygiene risk, environmental risk, value risk, and socio-phycological risk were identified. Higher levels of perceived risks were identified in female respondents and four dimensions of risk (except hygiene risk) showed a significant relationship with risk reduction behavior. Such risk reduction behavior significantly influenced the intention to consume seafood. Practical and theoretical implications were also suggested from the findings of this study.
A nomogram for predicting the probability of femoral head collapse in convalescent SARS patients with glucocorticoid-induced osteonecrosis of the femoral head: an 18-year population-based retrospective cohort study
Authors: Qu Rundong , Gao Fuqiang , Sun Wei , Li Zirong
Published: May 2024

Background

This study aimed to develop a nomogram capable of predicting the probability of femoral head collapse based on an 18-year follow-up cohort of convalescent severe acute respiratory syndrome (SARS) patients with glucocorticoid-induced osteonecrosis of the femoral head (ONFH).

Methods

Data on the natural history of 120 patients (205 hips) who underwent glucocorticoid-induced ONFH at China-Japan Friendship Hospital (CJFH) in 2003 were retrospectively collected. Follow-up was conducted from June 2003 to October 2021. A nomogram was developed in a training cohort and validated in another cohort.

Results

A total of 205 hips were included for analysis, with 143 hips in the training cohorts and 62 hips in the validation cohorts. After 18 years of follow-up, 53 femoral heads collapsed, while 152 femoral heads spontaneously repaired to some extent (necrotic areas reduced or vanished). Following multivariate regression analysis, the Association Research Circulation Osseous (ARCO) staging, necrosis index (NI), and CJFH Classification were entered into the nomogram. The nomogram showed robust discrimination, with an AUC of 0.907 (95% CI: 0.85–0.96). The calibration curves showed an agreement between the probability as predicted by the nomogram and the actual probability. Application of the nomogram in the validation cohort also yielded good discrimination (AUC, 0.876, 95% CI: 0.7751–0.9761) and calibration.

Conclusion

The nomogram successfully predicted femoral head collapse in glucocorticoid-induced ONFH. With the nomogram, the prognosis for an individual patient with glucocorticoid-induced ONFH can be determined, which can lead to a rational therapeutic choice.
70 years of heat waves and summer climate change affecting Italian small ruminant populations
Authors: Bionda Arianna , Cortellari Matteo , Negro Alessio , Crepaldi Paola
Published: May 2024

Climate change and heat stress pose significant challenges to livestock. Local breeds, particularly small ruminants, are gaining importance due to their adaptability to harsh climates. However, the extensive system they are commonly reared in leaves them exposed to the effects of climate change. This study aims to describe the distribution and climate-related challenges faced by registered Italian sheep and goat breeds over the past seven decades. Geolocalized data from all registered small ruminant farms were combined with climatic information retrieved from the “ERA-5-Land hourly data from 1950 to present” dataset. These data were used to calculate average daily temperature, temperature humidity index (THI), and total precipitation during summer. Additionally, THI-based heat waves (HWs) were examined, including the yearly number of HW days and mean THI during HW days. These data were analysed through linear regression models including region or breed, year, and their interaction as fixed factors. The climate data indicate a concerning trend of rising summer temperatures, THI, and HW frequency and intensity, particularly over the past three decades. Central-northern Italy, including the Po Valley and the Alpine Arch, is the most affected region, impacting breeds like Rosset and Brogne sheep, and Lariana and Frisa Valtellinese goats. This is of particular concern because these populations have not been selected for hot climates, and their already small population size exacerbates the problem. Conversely, southern Italy, characterized by hotter and drier temperatures, remained relatively stable. Breeds from this region, such as Girgentana and Nicastrese goats and Nera di Arbus sheep, might represent excellent case studies for climatic adaptation and potential resources for selection for resilience in the face of ongoing climate changes. The findings presented here are essential for the development of monitoring and intervention strategies for breeds facing future vulnerabilities, as well as for designing experiments to explore environmental adaptability in small ruminants.
A Discrete Cramér–Von Mises Statistic Related to Hahn Polynomials with Application to Goodness-of-Fit Testing for Hypergeometric Distributions
Authors: Pycke Jean-Renaud
Published: May 2024

We give the Karhunen–Loève expansion of the covariance function of a family of discrete weighted Brownian bridges, appearing as discrete analogues of continuous Gaussian processes related to Cramér –von Mises and Anderson–Darling statistics. This analogy enables us to introduce a discrete Cramér–von Mises statistic and show that this statistic satisfies a property of local asymptotic Bahadur optimality for a statistical test involving the classical hypergeometric distributions. Our statistic and the goodness-of-fit problem we deal with are based on basic properties of Hahn polynomials and are, therefore, subject to some extension to all families of classical orthogonal polynomials, as well as their q-analogues. Due probably to computational difficulties, the family of discrete Cramér–von Mises statistics has received less attention than its continuous counterpart—the aim of this article is to bridge part of this gap.
Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix
Authors: Zhang Wenxuan , Lu Benzhuo
Published: May 2024

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.
Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management
Authors: Mei Yingxue , Jin Zicai , Ma Weiguo , Ma Yingjun , Deng Ning , Fan Zhiyuan , Wei Shujun
Published: May 2024

Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients. The model’s efficacy was evaluated using metrics such as area under the curve (AUC), precision–recall (PR), and F1-scores. Additionally, a patient management platform was developed to facilitate personalized treatment strategies. Results: Incorporating a gating mechanism substantially improved the Transformer model’s performance, especially in identifying true-positive cases. The TabTransformer+Gate model demonstrated an AUC of 0.836, a 14% increase in average precision (AP), and a 6.2% enhancement in accuracy, significantly outperforming other deep learning approaches. The patient management platform enabled healthcare professionals to effectively assess patient risks and tailor treatments, improving patient outcomes and quality of life. Conclusion: The integration of a gating mechanism within the Transformer model markedly increases the accuracy of MACE risk predictions in ACS patients, optimizes personalized treatment, and presents a novel approach for advancing clinical practice and research.
Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows
Authors: García-Córdova Francisco , Guerrero-González Antonio , Hidalgo-Castelo Fernando
Published: May 2024

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments.
A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time
Authors: Huang Gewen , Qi Yuanhang , Cai Yanguang , Luo Yuhui , Huang Helie
Published: May 2024

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm’s convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT’s average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.
Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control
Authors: Wen Shengjun , Shi Zhaoyuan , Li Hongjun
Published: May 2024

Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader–follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control.
The Effective Potential of Scalar Pseudo-Quantum Electrodynamics in (2 + 1)D
Authors: Nascimento Leandro O. , Junior Carlos A. P. C. , Santos José R.
Published: May 2024

The description of the electron–electron interactions in two-dimensional materials has a dimensional mismatch, where electrons live in (2 + 1)D while photons propagate in (3 + 1)D. In order to define an action in (2 + 1)D, one may perform a dimensional reduction of quantum electrodynamics in (3 + 1)D (QED4) into pseudo-quantum electrodynamics (PQED). The main difference between this model and QED4 is the presence of a pseudo-differential operator in the Maxwell term. However, besides the Coulomb repulsion, electrons in a material are subjected to several microscopic interactions, which are inherent in a many-body system. These are expected to reduce the range of the Coulomb potential, leading to a short-range interaction. Here, we consider the coupling to a scalar field in PQED for explaining such a mechanism, which resembles the spontaneous symmetry breaking (SSB) in Abelian gauge theories. In order to do so, we consider two cases: (i) by coupling the quantum electrodynamics to a Higgs field in (3 + 1)D and, thereafter, performing the dimensional reduction; and (ii) by coupling a Higgs field to the gauge field in PQED and, subsequently, calculating its effective potential. In case (i), we obtain a model describing electrons interacting through the Yukawa potential and, in case (ii), we show that SSB does not occur at one-loop approximation. The relevance of the model for describing electronic interactions in two-dimensional materials is also addressed.
The Supplementation Therapy in Autism and Response to Treatment (START) Study: An Open-Label Feasibility Trial of Ultramicronized Palmitoylethanolamide Potential to Alleviate Psychic Distress among Autistic Adults
Authors: Bortoletto Riccardo , Basaldella Marta , Candolo Anna , Garzitto Marco , Comacchio Carla , Curcio Francesco , Fabris Martina , Fornasaro Stefano , Piscitelli Fabiana , Sepulcri Orietta , Balestrieri Matteo , Colizzi Marco
Published: May 2024

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impaired social communication and restricted or repetitive behavior and interests. Psychic distress is common among individuals with ASD, especially in its milder form (level 1), with anxiety and depression being the most common types. Recent research has identified neuroinflammation and gut dysbiosis as potential neurobiological mechanisms underlying ASD. Palmitoylethanolamide (PEA), an endocannabinoid (eCB)-like compound, has shown promise in modulating such mechanisms and may thus have therapeutic implications for ASD. To date, no clinical trial has evaluated the efficacy of PEA in adults with ASD. This 12-week open-label study will assess the feasibility, tolerability, safety, and efficacy of ultramicronized PEA (um-PEA) in treating symptoms of psychic distress, such as anxiety and depression, in adults with level 1 ASD. Secondary research endpoints will include um-PEA’s effects on levels of personal autonomy and neurocognitive and interpersonal function. From a biological point of view, this study will assess um-PEA’s effects on inflammatory markers, the metabolic profile, eCB system modulation, and microbial composition as potential mechanisms of action for its therapeutic effect. In conclusion, this study will investigate a novel approach to the treatment of adults presenting with psychic distress in the context of level 1 ASD. The results may provide valuable insight into the use of um-PEA as a treatment option for ASD adults, addressing a significant unmet clinical need.
Ensemble Learning for Pea Yield Estimation Using Unmanned Aerial Vehicles, Red Green Blue, and Multispectral Imagery
Authors: Liu Zehao , Ji Yishan , Ya Xiuxiu , Liu Rong , Liu Zhenxing , Zong Xuxiao , Yang Tao
Published: May 2024

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R2) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research.
The Development of an Optimal Operation Algorithm for Food Delivery Using Drones Considering Time Interval between Deliveries
Authors: Ko Young Kwan , Han Hyeseon , Oh Yonghui , Ko Young Dae
Published: May 2024

These days, many attempts are being made worldwide to use drones for food delivery. Especially in the case of food, fast delivery is required, while maintaining its temperature and taste to the maximum. Therefore, using drones is suitable for food delivery because they can move through the air without being affected by traffic congestion. In this study, the purpose is to develop an optimal algorithm that can complete the delivery of customer food orders in the shortest time using drones. We have applied mathematical-model-based optimization techniques to develop an algorithm that reflects the given problem situation. Since the delivery capacity of drones is limited, and especially small, multiple drones may be used to deliver the food ordered by a particular customer. What is important here is that the drones assigned to one customer must arrive consecutively within a short period of time. This fact is reflected in this mathematical model. In the numerical example, it can be confirmed that the proposed algorithm operates optimally by comparing a case where the arrival time of multiple drones assigned to one customer is limited to a certain time and a case when it is not.
Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome
Authors: Kovács Béla , Vörös Fanni , Vas Tímea , Károly Krisztián , Gajdos Máté , Varga Zsófia
Published: May 2024

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.
Monitoring Nodal Transportation Assets with Uncrewed Aerial Vehicles: A Comprehensive Review
Authors: Askarzadeh Taraneh , Bridgelall Raj , Tolliver Denver
Published: May 2024

Using Uncrewed Aerial Vehicles (UAVs) to monitor the condition of nodal transportation assets—airports, seaports, heliports, vertiports, and cargo terminals—presents a transformative approach to traditional inspection methods. The focus on nodal assets rather than linear assets like roads, railways, bridges, and waterways fills a gap in addressing the dynamic challenges specific to transportation hubs. This study reviews scholarly literature on applying UAV-based remote sensing (URS) techniques to assess the condition of various transportation hubs, which are critical junctures in global logistics networks. Utilizing a systematic literature review framework, this study reviewed 486 publications from 2015 to 2023 to extract insights from the evolving discourse on URS applications. The findings suggest that these emerging methods resulted in substantial enhancements in time saving, cost efficiency, safety, and reliability. Specifically, this study presents evidence on how URS approaches can overcome the constraints of conventional inspection methods by enabling rapid, high-precision mapping and surveillance in complex and constrained environments. The findings highlight the role of UAVs in enhancing operational workflows and decision making in transportation planning and maintenance. By bridging the gap between traditional practices and innovative technology, this research offers significant implications for stakeholders in the field, advocating for a shift towards more dynamic, cost-effective, and precise asset management strategies.
Towards Fully Autonomous Drone Tracking by a Reinforcement Learning Agent Controlling a Pan–Tilt–Zoom Camera
Authors: Wisniewski Mariusz , Rana Zeeshan A. , Petrunin Ivan , Holt Alan , Harman Stephen
Published: May 2024

Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators.
Training Acceleration Method Based on Parameter Freezing
Authors: Tang Hongwei , Chen Jialiang , Zhang Wenkai , Guo Zhi
Published: May 2024

As deep learning has evolved, larger and deeper neural networks are currently a popular trend in both natural language processing tasks and computer vision tasks. With the increasing parameter size and model complexity in deep neural networks, it is also necessary to have more data available for training to avoid overfitting and to achieve better results. These facts demonstrate that training deep neural networks takes more and more time. In this paper, we propose a training acceleration method based on gradually freezing the parameters during the training process. Specifically, by observing the convergence trend during the training of deep neural networks, we freeze part of the parameters so that they are no longer involved in subsequent training and reduce the time cost of training. Furthermore, an adaptive freezing algorithm for the control of freezing speed is proposed in accordance with the information reflected by the gradient of the parameters. Concretely, a larger gradient indicates that the loss function changes more drastically at that position, implying that there is more room for improvement with the parameter involved; a smaller gradient indicates that the loss function changes less and the learning of that part is close to saturation, with less benefit from further training. We use ViTDet as our baseline and conduct experiments on three remote sensing target detection datasets to verify the effectiveness of the method. Our method provides a minimum speedup ratio of 1.38×, while maintaining a maximum accuracy loss of only 2.5%.
Identifying a Role of Polysaccharides from Agaricus Blazei Murill in Combating Skin Photoaging: The Effect of Antioxidants on Fibroblast Behavior
Authors: Di Feiqian , Cheng Wenjing , Li Luyao , Pu Chunhong , Sun Ruiliang , Zhang Jiachan , Wang Changtao , Li Meng
Published: May 2024

Irradiation with ultraviolet A (UVA) plays an important role in the pathogenesis of skin photoaging since it increases oxidative stress and inflammation in the epidermis. There is an urgent need to screen, investigate, and apply the potential anti-photoaging active ingredients. Agaricus blazei Murill (ABM) polysaccharides have a wide range of promising pharmacological applications. Previous studies have confirmed their antioxidant effect, but whether it has an anti-photoaging effect is unclear. In this study, two ABM polysaccharides (AB-J and AB-K) were obtained to discuss the potential photodamage-protective capacity. The free radical scavenging abilities in vitro, the safety assessment, and their protective effects and mechanisms on UVA-induced human fibroblasts (HSFs) were evaluated. The intracellular antioxidant enzyme levels and extracellular matrix proteins, such as COL-I and ELN, were significantly accelerated, and metalloproteinases (MMP-1, and MMP-9) were decreased by AB-J and AB-K. The Keap-1-Nrf2/ARE signaling pathway was activated, thus inducing the upregulated expression of downstream genes (Ho-1 and Nqo-1). The suppression of P38 and Jnk1 by AB-J and AB-K was speculated to be the inducer of the activation of the Keap-1-Nrf2/ARE signaling pathway. Owing to the excellent exhibition of AB-J, its safety assessment and the structural characterization are discussed further.
Investigation of the Coupling Schemes between the Discrete and the Continuous Phase in the Numerical Simulation of a 60 kWth Swirling Pulverised Solid Fuel Flame under Oxyfuel Conditions
Authors: Askarizadeh Hossein , Pielsticker Stefan , Nicolai Hendrik , Kneer Reinhold , Hasse Christian , Maßmeyer Anna
Published: May 2024

Detailed numerical analyses of pulverised solid fuel flames are computationally expensive due to the intricate interplay between chemical reactions, turbulent multiphase flow, and heat transfer. The near-burner region, characterised by a high particle number density, is particularly influenced by these interactions. The accurate modelling of these phenomena is crucial for describing flame characteristics. This study examined the reciprocal impact between the discrete phase and the continuous phase using Reynolds-averaged Navier–Stokes (RANS) simulations. The numerical model was developed in Ansys Fluent and equipped with user-defined functions that adapt the modelling of combustion sub-processes, in particular, devolatilisation, char conversion, and radiative heat transfer under oxyfuel conditions. The aim was to identify the appropriate degree of detail necessary for modelling the interaction between discrete and continuous phases, specifically concerning mass, momentum, energy, and turbulence, to effectively apply it in high-fidelity numerical simulations. The results of the numerical model show good agreement in comparison with experimental data and large-eddy simulations. In terms of the coupling schemes, the results indicate significant reciprocal effects between the discrete and the continuous phases for mass and energy coupling; however, the effect of particles on the gas phase for momentum and turbulence coupling was observed to be negligible. For the investigated chamber, these results are shown to be slightly affected by the local gas phase velocity and temperature fields as long as the global oxygen ratio between the provided and needed amount of oxygen as well as the thermal output of the flame are kept constant.
Exploring Cellular Dynamics in the Goldfish Bulbus Arteriosus: A Multifaceted Perspective
Authors: Mokhtar Doaa M. , Abd-Elhafez Enas A. , Albano Marco , Zaccone Giacomo , Hussein Manal T.
Published: May 2024

The bulbus arteriosus of goldfish, Carassius auratus, possesses unique structural features. The wall of the bulbus arteriosus is exceptionally thick, with an inner surface characterized by longitudinally arranged finger-like ridges, resulting in an uneven luminal appearance. These ridges are covered by endocardium and encased in an amorphous extracellular matrix. The inner surface of the bulbus arteriosus also contains rodlet cells at different developmental stages, often clustered beneath the endothelium lining the bulbar lumen. Ruptured rodlet cells release their contents via a holocrine secretion process. The high abundance of rodlet cells in the bulbus arteriosus suggests that this is the site of origin for these cells. Within the middle layer of the bulbus arteriosus, smooth muscle cells, branched telocytes (TCs), and collagen bundles coexist. TCs and their telopodes form complex connections within a dense collagen matrix, extending to rodlet cells and macrophages. Moreover, the endothelium makes direct contact with telopodes. The endocardial cells within the bulbus arteriosus display irregular, stellate shapes and numerous cell processes that establish direct contact with TCs. TEM reveals that they contain moderately dense bodies and membrane-bound vacuoles, suggesting a secretory activity. TCs exhibit robust secretory activity, evident from their telopodes containing numerous secretory vesicles. Furthermore, TCs release excretory vesicles containing bioactive molecules into the extracellular matrix, which strengthens evidence for telocytes as promising candidates for cellular therapies and regeneration in various heart pathologies.
A Proportional-Integral-One Plus Double Derivative Controller-Based Fractional-Order Kepler Optimizer for Frequency Stability in Multi-Area Power Systems with Wind Integration
Authors: Alqahtani Mohammed H. , Almutairi Sulaiman Z. , Aljumah Ali S. , Shaheen Abdullah M. , Moustafa Ghareeb , El-Fergany Attia A.
Published: May 2024

This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration.