bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and field evaluation of a novel sugar bait device for controlling residential vector mosquitoes in Zhejiang Province, China
Authors: Wu Yuyan , Zhang Chuan , Feng Wei , Fu Sanjun , Dong Weibo , Wang Jinna , Liu Qinmei , Li Tianqi , Luo Mingyu , Gong Zhenyu
Published: March 2024

Background

Mosquito-borne diseases pose serious public health threats in Zhejiang Province, China, and vector control is believed to be the primary method for reducing transmission. Due to severe resistance problems, effective and sustainable methods without chemical insecticides are urgently required to control mosquito vectors. Attractive toxic sugar baits (ATSB) are newly developed methods to control mosquitoes in recent decades with the core element sugar bait, which was invented according to the sugar-feeding behavior of mosquitoes. In this study, we developed a Novel Sugar Bait Device (NSBD) trap by combining sugar bait and physical adhesive capture technology. The study aimed to evaluate the effect of the NSBD trap on controlling mosquitoes in residential environments and to identify the optimal sugar solution concentration in the sugar bait of the NSBD for real use.

Methods

Four residential villages in Ningbo City with similar geographic environments and mosquito densities were selected for field trials in 2022. One village (site 1) was designated as the control group, and three villages (sites 2–4) served as the test groups to assess the effectiveness of NSBD traps with different sugar solution concentrations (6, 8, and 10%) in the sugar bait. Larval and adult mosquito densities were monitored monthly before and semi-monthly after the trials using the CDC light trap and larval pipette method.

Results

Before the trials, we monitored mosquito density for 3 months to confirm the baseline mosquito density among the four sites, and no statistical differences in adult and larval mosquitoes were found (adult, F = 3.047, p > 0.05; larvae, F = 0.436, p > 0.05). After the trials, all NCBD traps effectively controlled larval and adult mosquito densities, with the highest standard decrease rates of larval and adult mosquito densities at 57.80 and 86.31%, respectively, observed in site 4. The most suitable sugar solution concentration in the sugar bait was 10%.

Conclusion

NSBD traps effectively controlled mosquitoes in residential environments during field trials. Without the use of insecticides, this may be a promising choice for mosquito vector control to prevent mosquito-borne diseases.
A study on the treatment effects of Crataegus pinnatifida polysaccharide on non-alcoholic fatty liver in mice by modulating gut microbiota
Authors: Hao Ping , Yang Xiaonan , Yin Wen , Wang Xinyi , Ling Yun , Zhu Mengyao , Yu Yue , Chen Shouhai , Yuan Yuan , Quan Xiaoyu , Xu Zhiheng , Zhang Jiahui , Zhao Wenjia , Zhang Ying , Song Chunlian , Xu Qing , Qin Shuangshuang , Wu Yi , Shu Xianghua , Wei Kunhua
Published: March 2024

The objective of this study was to investigate the protective effect of Crataegus pinnatifida polysaccharide (CPP) on non-alcoholic fatty liver disease (NAFLD) induced by a high-fat diet (HFD) in mice. The findings demonstrated that CPP improved free fatty acid (FFA)-induced lipid accumulation in HepG2 cells and effectively reduced liver steatosis and epididymal fat weight in NAFLD mice, as well as decreased serum levels of TG, TC, AST, ALT, and LDL-C. Furthermore, CPP exhibited inhibitory effects on the expression of fatty acid synthesis genes FASN and ACC while activating the expression of fatty acid oxidation genes CPT1A and PPARα. Additionally, CPP reversed disturbances in intestinal microbiota composition caused by HFD consumption. CPP decreased the firmicutes/Bacteroidetes ratio, increased Akkermansia abundance, and elevated levels of total short-chain fatty acid (SCFA) content specifically butyric acid and acetic acid. Our results concluded that CPP may intervene in the development of NAFLD by regulating of intes-tinal microbiota imbalance and SCFAs production. Our study highlights that CPP has a potential to modulate lipid-related pathways via alterations to gut microbiome composition thereby ex-erting inhibitory effects on obesity and NAFLD development.
Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
Authors: Ganopoulou Maria , Kontopoulos Efstratios , Fokianos Konstantinos , Koparanis Dimitris , Angelis Lefteris , Kotsianidis Ioannis , Moysiadis Theodoros
Published: March 2024

Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal discovery. This study explored causal discovery techniques within HRQoL, assessed various considerations for reliable estimation, and proposed means for interpreting outcomes. Five causal structure learning algorithms were employed to examine different aspects in structure estimation based on simulated data derived from HRQoL-related directed acyclic graphs. The performance of the algorithms was assessed based on various measures related to the differences between the true and estimated structures. Moreover, the Resource Description Framework was adopted to represent the responses to the HRQoL questionnaires and the detected cause–effect relationships among the questions, resulting in semantic knowledge graphs which are structured representations of interconnected information. It was found that the structure estimation was impacted negatively by the structure’s complexity and favorably by increasing the sample size. The performance of the algorithms over increasing sample size exhibited a similar pattern, with distinct differences being observed for small samples. This study illustrates the dynamics of causal discovery in HRQoL-related research, highlights aspects that should be addressed in estimation, and fosters the shareability and interoperability of the output based on globally established standards. Thus, it provides critical insights in this context, further promoting the critical role of HRQoL questionnaires in advancing patient-centered care and management.
Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
Authors: Lee Alvin , Yong Suet-Peng , Pedrycz Witold , Watada Junzo
Published: March 2024

Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view.
An Entrepreneurial Perspective on the Transition of Lignite Rural Areas to a New Regime within a Suffocating Timeframe
Authors: Makris Ilias , Apostolopoulos Sotiris , Anastasopoulou Eleni E.
Published: March 2024

Timetables for energy transition vary across EU Member States. The planning for dealing with the consequences of the transition in affected regions also varies. Under the pressure of the ten-year economic crisis, Greece is one of the few countries that has committed to stop using lignite in electricity generation, achieving 80% by 2023 and definitively by 2028. Apart from its environmental impact, the country assessed using lignite in electricity generation as unprofitable and damaging to the Greek economy. Simultaneously, it formulated a national energy transition plan for the country’s lignite areas. The plan also provides support for existing enterprises and incentives for new enterprises. European and domestic financial instruments accompany the support incentives. The compressed timetable is ambitious and demanding, entailing risks of project failure. As one of the first Member State projects to be completed in 2028, its outcome will shape an ambient atmosphere, positive or negative, for other Member States’ projects as well. Based on the transition plan and the timelines to which Greece has committed, this research examines the reanimation of rural areas involved in lignite mining from the perspective of entrepreneurship and financial support instruments. The findings revealed that while there is strong social consensus around the design that incorporates best practices, the basis for the completion schedule has unrealistic elements that will have potentially negative consequences for the restoration of the areas that the energy transition affects. The present study highlights the risks for entrepreneurship that tight schedules pose in the context of lignite area redevelopment.
Does Ecological Planting–Breeding Mix Pattern Improve Farmers’ Subjective Well-Being? Evidence from the Middle and Lower Reaches of the Yangtze River
Authors: Li Xinyao , Wang Xicong , Zhu Yangyang , Liu Zhe , Qi Zhenhong
Published: March 2024

The Chinese government is making vigorous efforts to control agricultural pollution. The promotion of an ecological planting–breeding mix pattern is one of them. Farmers’ mode of production will affect their subjective well-being. Thus, this paper aims to analyze the impact of adopting the ecological planting–breeding mix pattern on farmers’ subjective well-being based on 895 pieces of survey data from the provinces of Hubei, Hunan, and Jiangxi in the middle and lower reaches of the Yangtze River. Using the endogenous switching regression model, we find that the adoption of the ecological planting–breeding mix pattern has a significant positive effect on farmers’ subjective well-being. Based on this counterfactual hypothesis, if the farmers who actually adopted the ecological planting–breeding mix pattern did not adopt it, their subjective well-being would decrease from 4.006 to 3.669. Further examination indicates that self-worth identification, income increase, and neighborhood communication could be potential mechanisms. Additionally, the effect of the ecological planting–breeding mix pattern on subjective well-being is stronger in the group with low financial support. The technical support provided by the government does not have a significant regulatory effect on the adoption of the ecological planting–breeding mix pattern on subjective well-being. Our results suggest that farmers’ ecological production behavior can affect their subjective well-being. This may be relevant to many developing countries today that are attempting to adopt ecological agriculture patterns.
Optimization Design and Experiment of a Cotton Straw-Crushing Device Based on Computational Fluid Dynamics
Authors: Wu Tao , Chen Xuegeng , Yan Limin , Gou Haixiao , Li Ying , Zhang Jinhao , Fu Xuanhe
Published: March 2024

In order to design the structure and optimize the working parameters of a straw-crushing device, and to improve the qualified rate of straw-crushing length and the rate of straw fluidity, the influence of the airflow characteristics and pressure distribution in the working state of the crushing chamber and the different structure and working parameters of the straw-crushing device on the airflow field were studied based on the computational fluid dynamics (CFD) software Fluent 19.2. The simulation results show that by changing the rotation speed of the cutter shaft, the negative pressure and mass flow rate of the inlet surface of the flow field reach the maximum values of 63.6 Pa and 1.64 kg/s, respectively, when the rotation speed of the cutter shaft is 2000 rpm, which proves that the rotation speed of the cutter shaft increases, and the feeding performance also increases. In the case of 0, 8 and 16 air knives, the peak flow velocity in the crushing chamber is 43.1 m/s, 50.1 m/s and 54.48 m/s, respectively, and the airflow in the crushing chamber is improved to a certain extent, which proves the feasibility of the air knives’ structure. In order to verify the simulation results, a field experiment was carried out. The results showed that the qualified rate of straw crushing was 94.13% and the drop rate was 4.26% under the conditions of a cutter shaft speed of 1800 rpm, a machine forward speed of 6 km/h and a height of 80 mm of the cutter off the ground. The field experiment and simulation show that the qualified rate of crushing increases with the increase in the rotation speed of the cutter shaft, but the increase rate slows down, and the random speed decreases. The drop rate decreases with the increase in tool height and tool speed. The air knife structure improves the crushing qualification rate and significantly reduces the drop rate, which verifies the simulation accuracy. This study provides a theoretical basis for the optimization of straw-crushing devices.
Growth Dynamic, Productivity, Evapotranspiration, and Water-Economic Indices of Forage Cactus under Different Irrigation Depths
Authors: Araújo Júnior George do Nascimento , Leite Renan Matheus Cordeiro , Morais José Edson Florentino de , Alves Cleber Pereira , Souza Carlos André Alves de , Almeida Alexsandro Claudio dos Santos , Jardim Alexandre Maniçoba da Rosa Ferraz , Souza Luciana Sandra Bastos de , Eugenio Danielle da Silva , Silva Thieres George Freire da
Published: March 2024

Supplementary irrigation can be strategic for forage cactus, aiming to guarantee higher levels of productivity and economic return. Therefore, this study aimed to evaluate the growth dynamics, productivity, evapotranspiration, and water economy indices of three forage cactus clones under different water regimes. The study was carried out in the Brazilian semi-arid region during two cycles between 2020 and 2022. Clones of forage cactus Orelha de Elefante Mexicana—OEM (Opuntia stricta (Haw.) Haw.) were evaluated; IPA Sertânia—IPA and Miúda—MIU (both species Nopalea cochenillifera (L.) Salm-Dyck) were subjected to four water regimes: rainfed and three irrigation depths (4, 8 and 12 mm). Growth dynamics and evapotranspiration were evaluated throughout the cycles, and productivity and water economy indices were based on data from the harvest time. The OEM clone showed higher dry matter productivity (21.8 Mg ha−1). The MIU clone, when intended for seed sales, presented a higher economic return (USD 20,823.33). The use of irrigation brought forward the cutting time and reduced water saving rates. With this, it can be concluded that the increase in water availability in cactus increases water consumption and reduces economic-water indices but brings important productive returns.
Enhancing Drought Resistance and Yield of Wheat through Inoculation with Streptomyces pactum Act12 in Drought Field Environments
Authors: Yang Bin , Wen Hongwei , Wang Shanshan , Zhang Jinhui , Wang Yuzhi , Zhang Ting , Yuan Kai , Lu Lahu , Liu Yutao , Xue Quanhong , Shan Hao
Published: March 2024

Drought stress is the primary abiotic factor affecting wheat growth, development, and yield formation. The application of plant growth-promoting rhizobacteria (PGPR) represents an environmentally sustainable approach to mitigate the impacts of drought stress on wheat. This study conducted field experiments using two winter wheat varieties, the drought-sensitive variety Jimai 22 and the drought-resistant variety Chang 6878, aiming to investigate the effects of Streptomyces pactum Act12 inoculation on photosynthetic characteristics, physiological parameters, and yield traits during the jointing, heading, and middle-filling stages under drought stress. The results revealed that drought stresses significantly reduced chlorophyll content, leaf area, biomass, and yield in wheat, while Act12 inoculation significantly increased chlorophyll content, photosynthetic efficiency, antioxidant enzyme activity such as superoxide dismutase (SOD) and peroxidase (POD), osmolyte content (proline and soluble proteins), and decreased malondialdehyde (MDA) content. These combined effects alleviated drought stress, resulting in increased biomass and yield in wheat. Under drought stress, an increase in leaf proline content of 13.53% to 53.23% (Jimai 22) and 17.17% to 43.08% (Chang 6878) was observed upon Act12 inoculation. Moreover, a decrease in MDA content was recorded of 15.86% to 53.61% (Jimai 22) and 13.47% to 26.21% (Chang 6878). Notably, there was a corresponding increase in yield of 11.78% (Jimai 22) and 13.55% (Chang 6878). In addition, grain quality analysis revealed a significant improvement in grain hardness with Act12 inoculation. Therefore, Act12 demonstrates the potential for enhancing the sustainable development of wheat production in arid and semi-arid regions.
Stereoselective Pharmacokinetics of Ketamine Administered at a Low Dose in Awake Dogs
Authors: Pargätzi Gwenda , Bergadano Alessandra , Spadavecchia Claudia , Theurillat Regula , Thormann Wolfgang , Levionnois Olivier L.
Published: March 2024

The present study aimed to examine the stereoselective pharmacokinetics of racemic ketamine in dogs at low doses. The secondary aims were to identify associated behavioural effects and propose a ketamine infusion rate. The study was conducted on nine intact male beagles, with each dog undergoing two treatments (BOL and INF). For treatment BOL, an intravenous bolus of 1 mg/kg was administered over 2 min. The treatment INF involved an initial bolus of 0.5 mg/kg given over 1 min, followed by an infusion at 0.01 mg/kg/min for 1 h. Blood samples were collected for pharmacokinetic analysis. The median R/S enantiomer ratio of ketamine remained close to 1 throughout the study. Levels of S-norketamine were significantly higher than those of R-norketamine across all time points. Based on the collected data, the infusion rate predicted to achieve a steady-state racemic ketamine plasma concentration of 150 ng/mL was 0.028 mg/kg/min. Higher scores for behavioural effects were observed within the first five minutes following bolus administration. The most common behaviours observed were disorientation, head movements and staring eyes. Furthermore, employing ROC curve analysis, a racemic ketamine plasma concentration of 102 ng/mL was defined as the cut-off value, correlating with the occurrence of undesirable behavioural patterns.
Clinical and Clinico-Pathological Observations of the Erythrocyte Sedimentation Rate in Dogs Affected by Leishmaniosis and Other Inflammatory Diseases
Authors: Lubas George , Paltrinieri Saverio , Papini Roberto Amerigo , Lensi Ilaria , Benali Silvia Lucia , Cortadellas Oscar , D’Anna Nunzio , Fondati Alessandra , Roura Xavier , Zini Eric
Published: March 2024

The erythrocyte sedimentation rate (ESR) has been used in canine medicine in several disorders, above all, to evaluate levels of inflammation. This study evaluated the ESR in canine leishmaniosis (CanL) and other inflammatory conditions. Three groups of dogs were examined: CanL affected dogs without clinical signs (INFECTED group, #25) or with clinical signs (SICK group, #43) and dogs affected by acute or acute-on-chronic conditions (OTHER DISEASE group, #65). The ESR was compared with acute phase proteins or reactants either positive or negative (leukogram, fibrinogen, iron, unsaturated iron binding capacity, ferritin, haptoglobin, and albumin) and immunological markers (gamma-globulins, IgG, and IgM). The ESR was higher in the SICK group than in the INFECTED group (median 39 vs. 11 mm/h; p < 0.0001), as well as in the OTHER DISEASE than in the INFECTED groups (median 41 vs. 11 mm/h; p < 0.0001). The ESR appeared outside the reference range for all dogs in the SICK and OTHER DISEASE groups and almost with similar values (mm/h; median 39, 95% CI 31–51 vs. 41, 95% CI 12–87; p > 0.05). The extent of changes in ESR can help to establish the severity of CanL and other inflammatory disorders. As a point-of-care test, the ESR can be used to screen dogs for unhealthy conditions, and its values correlate with the severity of any disease, including CanL.
How Do Escape Distance Behavior of Broiler Chickens Change in Response to a Mobile Robot Moving at Two Different Speeds?
Authors: da Rocha Balthazar Glauber , Silveira Robson Mateus Freitas , da Silva Iran José Oliveira
Published: March 2024

In poultry farming, robots are considered by birds as intruder elements to their environment, because animals escape due to their movement. Their escape is measured using the escape distance (ED) technique. This study analyzes the behavior of animals in relation to their ED through the use of a robot with two speeds: 12 rpm and 26 rpm. The objective is to understand whether the speeds cause variations in ED and their implications for animal stress. A broiler breeding cycle was analyzed (six weeks) through the introduction of the robot weekly. ED analyses were carried out on static images generated from footage of the robot running. The results indicate higher escape distance rates (p < 0.05) peaking midway through the production cycle, notably in the third week. Conversely, the final weeks saw the lowest ED, with the most significant reduction occurring in the last week. This pattern indicates a gradual escalation of ED up to the fourth week, followed by a subsequent decline. Despite RPM12 having shown low ED results, it did not show enough ED to move the animals away from their path of travel, causing bumps and collisions. RPM26 showed higher ED in all breeding phases, but showed ED with no bumps and collisions.
Coccidiosis in Egg-Laying Hens and Potential Nutritional Strategies to Modulate Performance, Gut Health, and Immune Response
Authors: Sharma Milan Kumar , Kim Woo Kyun
Published: March 2024

Avian coccidiosis, despite advancements in management, nutrition, genetics, and immunology, still remains the most impactful disease, imposing substantial economic losses to the poultry industry. Coccidiosis may strike any avian species, and it may be mild to severe, depending on the pathogenicity of Eimeria spp. and the number of oocysts ingested by the bird. Unlike broilers, low emphasis has been given to laying hens. Coccidiosis in laying hens damages the gastrointestinal tract and causes physiological changes, including oxidative stress, immunosuppression, and inflammatory changes, leading to reduced feed intake and a drastic drop in egg production. Several countries around the world have large numbers of hens raised in cage-free/free-range facilities, and coccidiosis has already become one of the many problems that producers have to face in the future. However, limited research has been conducted on egg-laying hens, and our understanding of the physiological changes following coccidiosis in hens relies heavily on studies conducted on broilers. The aim of this review is to summarize the effect of coccidiosis in laying hens to an extent and correlate it with the physiological changes that occur in broilers following coccidiosis. Additionally, this review tries to explore the nutritional strategies successfully used in broilers to mitigate the negative effects of coccidiosis in improving the gut health and performance of broilers and if they can be used in laying hens.
Outcome of the Use of Clinical Reasoning Alone in Dogs with Acute Thoracolumbar Myelopathy and/or Pain
Authors: Scalia Bruno , Freeman Paul
Published: March 2024

Acute thoracolumbar myelopathy is a common neurological presentation in dogs. Although certain spinal conditions present with characteristic clinical pictures, managing such cases with clinical reasoning alone (i.e., without cross-sectional imaging) has never been explored. The aim of this study was to define the outcome of patients with suspected intervertebral disc extrusion (IVDE), ischaemic myelopathy (IM) or acute non-compressive nucleus pulposus extrusion (ANNPE) that were managed with clinical reasoning alone. The database of the Queen’s Veterinary School Hospital (Cambridge) was searched for paraparetic or paraplegic dogs with suspected IVDE or IM/ANNPE that were initially managed medically without undergoing imaging. Clinical presentation and outcome were recorded. If cross-sectional imaging was subsequently performed, information about the final diagnosis was collected and compared with the initially suspected diagnosis. A total of 123 IVDE cases were collected: 81% had a successful outcome with no imaging performed; 16% had IVDE confirmed with imaging and successful outcome with surgery or medical management; and just 3% were found to have an alternative diagnosis or were euthanised without imaging. A total of 16 IM/ANNPE cases were collected: 94% had a successful outcome, and one dog was euthanised. Successful outcomes can be obtained by using clinical reasoning alone in most dogs with suspected IVDE or IM/ANNPE.
Trends in Rescue and Rehabilitation of Marsupials Surviving the Australian 2019–2020 Bushfires
Authors: Cope Holly R. , McArthur Clare , Gray Rachael , Newsome Thomas M. , Dickman Christopher R. , Sriram Aditi , Haering Ron , Herbert Catherine A.
Published: March 2024

The 2019–2020 Australian bushfire season had a devastating impact on native wildlife. It was estimated that 3 billion native animals were impacted by the fires, yet there are few estimates of the number of animals that were rescued and rehabilitated post-fire. Focusing on the state of New South Wales (NSW) and Kangaroo Island, South Australia, we used a case study approach to determine the number of marsupials that were reported rescued due to the 2019–2020 bushfires in these areas and analysed species-specific trends in rescue and release success. In NSW, we found 889 reports of fire-affected marsupials in 2019–2020, mostly comprising kangaroos and wallabies (macropods; n = 458), koalas (n = 204), and possums (n = 162), with a smaller number of wombats (n = 43) and other marsupial species. Most reports of fire-affected marsupials occurred 6–8 weeks after fire ignition, and there was no difference in temporal frequency of rescues between marsupial groups. For the three main groups, the probability of survival and subsequent release differed, with macropods having the lowest probability of release after rescue (0.15 ± 0.04) compared to koalas (0.47 ± 0.04) and possums (0.55 ± 0.10). The type of injury was the main predictor of survival during rehabilitation for all three marsupial groups, with those malnourished/moribund or with traumatic injuries less likely to survive rehabilitation. Death or euthanasia occurred on the day of rescue for 77% of macropods, 48% of possums and 15% of koalas. Koalas most often died during rehabilitation rather than on the day of rescue, with 73% either dying or being euthanised between day 1 and 30 post-rescue, representing a potential welfare concern. On Kangaroo Island, koalas were the most frequently rescued marsupial species; most euthanasia cases and deaths occurred in a hospital, whereas other marsupials were mostly euthanised at triage. In both jurisdictions, koalas were over-represented while possums were under-represented relative to baseline population densities and wildlife rescue trends in the years before the 2019–2020 bushfires. These species differences in presentation post-fire warrant further investigation, as do the differences in triage, survival and release outcomes. It is hypothesised that the high intensity and large scale of the 2019–2020 fires impeded marsupial fire evasion tactics, as evidenced by the small number of animals found for rescue, and the differing rates of presentation relative to underlying population densities for the main marsupial groups. Based on our findings, there is a need for detailed record keeping and data sharing, development of consistent and evidence-based triage, treatment and euthanasia guidelines and deployment of trained wildlife emergency rescue teams with advanced search techniques to minimise animal suffering where safe to do so.
Phytochemical Characterization, Antioxidant, and Anti-Proliferative Activities of Wild and Cultivated Nigella damascena Species Collected in Sicily (Italy)
Authors: Scognamiglio Monica , Maresca Viviana , Basile Adriana , Pacifico Severina , Fiorentino Antonio , Bruno Maurizio , Badalamenti Natale , Kapelusz Marta , Marino Pasquale , Capasso Lucia , Bontempo Paola , Bazan Giuseppe
Published: March 2024

The use of Nigella damascena seeds in the culinary field or as aerial parts infusions in the pharmaceutical and cosmetic fields is widely reported. The biological activity of this plant, as demonstrated over the years, is closely related to its phytochemical content. This investigation focused on the comparative study of the same plants of N. damascena, one totally wild (WND), while the other two, one with white flowers (CWND) and the other with blue flowers (CBND), were subject to cultivation, irrigation, and manual weeding. Using the potential of 1D and 2D-NMR spectroscopy, coupled with MS/MS spectrometric studies, the three methanolic extracts of N. damascena were investigated. Chemical studies have highlighted the presence of triterpene saponin compounds and various glycosylated flavonoids. Finally, the in vitro antiproliferative and antioxidant activities of the three individual extracts were evaluated. The antiproliferative activity performed on U-937, HL-60, and MCF-7 tumor cell lines highlighted a greater anticancer effect of the CBND and CWND extracts compared to the data obtained using WND. The antioxidant activity, however, performed to quantify ROS generation is comparable among the extracts used.
A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
Authors: Chen Jianqiao , Tian Guofu
Published: March 2024

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective.
A Generalized Nonlinear Beam Element for Slender RC Members Using a Polygonization Section Approach—Part II: Verification of Analytical with Experimental Results
Authors: Sfondylis Dimitris K. , Sfakianakis Manolis G.
Published: March 2024

The proposed mathematical model, described in detail in Part I of the present paper, aims to solve the common numerical problems that currently characterize classic fiber models when used with a moderate fiber grid for reasons of computational cost. To confirm these objectives, comparisons were made between analytical predictions of the model with experimental results of RC specimens and of a 2D scaled frame for a wide range of parameters that mainly concern slender specimens without significant influence of the bond slip phenomenon, different types of cross section shapes, and various loading histories. The agreement between analytical to experimental results was found to range from very good to excellent in some cases.
Bridge-Borne Noise Induced by High-Speed Freight Electric Multiple Units: Characteristics, Mechanisms and Control Measures
Authors: Du Miao , Wang Kaiyun , Ge Xin , Zhang Xiaoan
Published: March 2024

The high-speed freight electric multiple unit (EMU) is one of the important development directions for railway freight transportation. To investigate the bridge radiation noise induced by the freight EMU, a noise prediction model consisting of the containers–vehicle–track–bridge dynamic model, finite element model, and boundary element model are established and validated. Through simulation, the bridge radiation noise under different train loading conditions is compared, and the noise radiation mechanism is revealed. Moreover, the noise reduction effect of the noise wall is studied, and the influences of noise wall heights and sound absorption materials are investigated. Results indicate that the bridge sound power and the sound pressure levels (SPLs) of near-field points increase slightly with train loads in the frequency range below 20 Hz and above 125 Hz, with a maximum increase of about 6.8 dB. The structure resonance, intense local vibration, and high acoustic radiation efficiency cause strong bridge radiation noise. The noise wall can realize a good overall noise reduction effect in the sound shadow zone; nevertheless, SPLs increased in areas between the bridge and the noise wall. The ground reflection affects the superposition of transmitted, reflected, and diffracted sound waves, which causes nonlinear relationships of noise reduction effects with the noise wall height. From the perspective of human hearing sensitivity, the loudness levels of typical field points increase with the frequency in the range of 20~80 Hz, and SPLs below 25 Hz are less than the threshold of hearing. Setting the noise wall can effectively reduce the loudness levels, and the reduction effect increases with the noise wall height.
A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model
Authors: Li Xiaojing , Fang Jiandong , Zhao Yvdong
Published: March 2024

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%.
Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
Authors: Fang Siwei , Zhang Guiqi , Li Yufeng , Li Jiangtao
Published: March 2024

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.
Contrastive Learning Penalized Cross-Entropy with Diversity Contrastive Search Decoding for Diagnostic Report Generation of Reduced Token Repetition
Authors: Zhang Taozheng , Meng Jiajian , Yang Yuseng , Yu Shaode
Published: March 2024

Medical imaging description and disease diagnosis are vitally important yet time-consuming. Automated diagnosis report generation (DRG) from medical imaging description can reduce clinicians’ workload and improve their routine efficiency. To address this natural language generation task, fine-tuning a pre-trained large language model (LLM) is cost-effective and indispensable, and its success has been witnessed in many downstream applications. However, semantic inconsistency of sentence embeddings has been massively observed from undesirable repetitions or unnaturalness in text generation. To address the underlying issue of anisotropic distribution of token representation, in this study, a contrastive learning penalized cross-entropy (CLpCE) objective function is implemented to enhance the semantic consistency and accuracy of token representation by guiding the fine-tuning procedure towards a specific task. Furthermore, to improve the diversity of token generation in text summarization and to prevent sampling from unreliable tail of token distributions, a diversity contrastive search (DCS) decoding method is designed for restricting the report generation derived from a probable candidate set with maintained semantic coherence. Furthermore, a novel metric named the maximum of token repetition ratio (maxTRR) is proposed to estimate the token diversity and to help determine the candidate output. Based on the LLM of a generative pre-trained Transformer 2 (GPT-2) of Chinese version, the proposed CLpCE with DCS (CLpCEwDCS) decoding framework is validated on 30,000 desensitized text samples from the “Medical Imaging Diagnosis Report Generation” track of 2023 Global Artificial Intelligence Technology Innovation Competition. Using four kinds of metrics evaluated from n-gram word matching, semantic relevance, and content similarity as well as the maxTRR metric extensive experiments reveal that the proposed framework effectively maintains semantic coherence and accuracy (BLEU-1, 0.4937; BLEU-2, 0.4107; BLEU-3, 0.3461; BLEU-4, 0.2933; METEOR, 0.2612; ROUGE, 0.5182; CIDER, 1.4339) and improves text generation diversity and naturalness (maxTRR, 0.12). The phenomenon of dull or repetitive text generation is common when fine-tuning pre-trained LLMs for natural language processing applications. This study might shed some light on relieving this issue by developing comprehensive strategies to enhance semantic coherence, accuracy and diversity of sentence embeddings.
Special Issue “Artificial Intelligence in Complex Networks”
Authors: Liu Xiaoyang
Published: March 2024

Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...]
Pollutants and Climate Change, Runoff, Behavior and Adverse Effects in Aquatic Ecosystems
Authors: Marini Mauro , DelValls T. Ángel
Published: March 2024

This Special Issue addresses topics related to the sources of pollutants and their diffusion in ecosystems and aquatic organisms [...]
Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
Authors: Liu Xianzhu , Liu Shi , Zheng Jiawei , Wang Yu , Li Huanyu , Wen Zhuoao
Published: March 2024

In view of the lack of high-precision optical simulation equipment for dim space targets at present, in this study, a simulation method for dim space targets based on polarization stray light suppression is proposed, the overall optical system architecture of the optical engine for depolarization stray light suppression is constructed, the mechanism of stray light generation is explored, and the dark state light leakage suppression method is presented by compensating the phase of LCOS reflected light with wave plate; a high-image quality collimating optical system with large field of view and flat aberration is designed based on the illumination system optimization method of the critical angle matching of the spectroscopic film; and the polarization stray light suppression effect and star position simulation error of the dim target simulator are tested. The test results show that the illumination of the simulated dim target is ≮10−10 lx, the contrast is 6.96, the non-uniformity of the bright state is only 5.88%, and the simulation error of the star position is 9.9″. This research can make some contributions to enhancing the observability of detecting dim targets, breaking through the detection technology of extremely dark targets in space, developing advanced deep space detection capabilities, and improving the engineering technology system of deep space exploration.
Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
Authors: Zhong Huaqiang , Hu Hao , Hou Ning , Fan Ziyuan
Published: March 2024

The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance.
Product Space Clustering with Graph Learning for Diversifying Industrial Production
Authors: Cortial Kévin , Albouy-Kissi Adélaïde , Chausse Frédéric
Published: March 2024

During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research.