Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as $L_{forest}$, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

With the rapid development of the emerging intelligent, flexible, transparent, and wearable electronic devices, such as quantum-dot-based micro light-emitting diodes (micro-LEDs), thin-film transistors (TFTs), and flexible sensors, numerous pixel-level printing technologies have emerged. Among them, inkjet printing has proven to be a useful and effective tool for consistently printing micron-level ink droplets, for instance, smaller than 50 µm, onto wearable electronic devices. However, quickly and accurately determining the printing quality, which is significant for the electronic device performance, is challenging due to the large quantity and micron size of ink droplets. Therefore, leveraging existing image processing algorithms, we have developed an effective method and software for quickly detecting the morphology of printed inks served in inkjet printing. This method is based on the edge detection technology. We believe this method can greatly meet the increasing demands for quick evaluation of print quality in inkjet printing. Full article

The liver fluke disease caused by Clonorchis sinensis is one of the most serious food-borne parasitic diseases in China. Many freshwater fish and shrimps can be infected with C. sinensis metacercariae as the second intermediate hosts in endemic regions. Owing to the lack of infected humans and the good administration of pet dogs and cats in cities of non-endemic regions, few fish are expected to be infected with C. sinensis metacercariae in urban lakes. To determine the infection of C. sinensis metacercariae in freshwater fish and shrimps in urban lakes, a total of 18 fish species and one shrimp species were investigated in the East Lake of Wuhan City. Metacercariae were isolated by artificial digestive juice and identified using morphology and rDNA-ITS2 sequences. Five species of fish, Pseudorasbora parva, Ctenogobius giurinus, Squalidus argentatus, Hemiculter leuciclus, and Rhodeus spp., were infected with C. sinensis metacercariae. The overall prevalence of C. sinensis was 32.5%. The highest prevalence was found in P. parva with 57.9%, while S. argentatus exhibited the highest mean abundance (13.9). Apart from the C. sinensis metacercariae, four species of other trematode metacercariae were also identified across twelve fish species in total. Owing to the consumption of undercooked fish and feeding cats with small fish caught by anglers, there is a potential risk that the small fish infected with C. sinensis metacercariae may act as an infection source to spread liver fluke. Given the complete life cycle of C. sinensis, stray cats and rats were inferred to act as the important final hosts of C. sinensis in urban lakes in non-endemic areas. Full article

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R² value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron. Full article

(1) Background: This study investigated the effects of sequenced electromagnetic fields, modulated at extremely low frequencies and intensities, in the treatment of drug-resistant Escherichia coli (E. coli)-induced chronic bacterial cystitis. (2) Methods: A total of 148 female participants, aged 18 to 80 years diagnosed with chronic bacterial cystitis caused by drug-resistant E. coli, were recruited for this study. Participants were randomly assigned to two groups: an experimental group (n = 74) with osteopathic palpation and assessment treated with a sequence of electromagnetic fields, and a control group (n = 74) receiving a placebo treatment. Both groups were assessed at this study’s outset, 4 weeks after eight applications, and at 12 weeks for symptomatic presentation and laboratory parameters. (3) Results: After 4 weeks of treatment, a significant difference was observed between the two groups regarding D-DIMER levels, IL-6 levels, erythrocyte levels, leukocyte levels, and E. coli levels (p < 0.001). By the 12th week, the experimental group continued to exhibit a significant reduction in the examined parameters compared to the control group (p < 0.001). Additionally, the treatment did not induce any side effects in the patients in the experimental group. (4) Conclusions: Treatment with coherently sequenced electromagnetic fields, modulated at an extremely low frequency and intensity, not only appears to provide an effective alternative for the symptoms of chronic bacterial cystitis caused by drug-resistant E. coli but also demonstrates a potent antibacterial effect. Full article

Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences. Full article

HKUST−1 is an MOF adsorbent industrially produced in powder form and thus requires a post-shaping process for use as an adsorbent in fixed-bed separation processes. HKUST−1 is also sensitive to moisture, which degrades its crystalline structure. In this work, HKUST−1, in the form of crystalline powder, was extruded into pellets using a hydrophobic polymeric binder to improve its moisture stability. Thermoplastic polyurethane (TPU) was used for that purpose. The subsequent HKUST−1/TPU extrudate was then compared to HKUST−1/PLA extrudates synthesized with more hydrophilic polymer: polylactic acid (PLA), as the binder. The characterization of the composites was determined via XRD, TGA, SEM-EDS, and an N₂ adsorption isotherm analysis. Meanwhile, the gas-separation performances of HKUST−1/TPU were investigated and compared with HKUST−1/PLA from measurements of CO₂ and CH₄ isotherms at three different temperatures, up to 10 bars. Lastly, the moisture stability of the composite materials was investigated via an aging analysis during storage under humid conditions. It is shown that HKUST−1’s crystalline structure was preserved in the HKUST−1/TPU extrudates. The composites also exhibited good thermal stability under 523 K, whilst their textural properties were not significantly modified compared with the pristine HKUST−1. Furthermore, both extrudates exhibited larger CO₂ and CH₄ adsorption capacities in comparison to the pristine HKUST−1. After three months of storage under atmospheric humid conditions, CO₂ adsorption capacities were reduced to only 10% for HKUST−1/TPU, whereas reductions of about 25% and 54% were observed for HKUST−1/PLA and the pristine HKUST−1, respectively. This study demonstrates the interest in shaping MOF powders by extrusion using a hydrophobic thermoplastic binder to operate adsorbents with enhanced moisture stability in gas-separation columns. Full article

End-of-life electric vehicle (EV) batteries can be reused to reduce their environmental impact and economic costs. However, the growth of the second-life market is limited by the lack of information on the characteristics and performance of these batteries. As the volume of end-of-life EVs may exceed the amount of batteries needed for stationary applications, investigating the possibility of repurposing them in mobile applications is also necessary. This article presents an experimental test that can be used to collect the data necessary to fill a battery passport. The proposed procedure can facilitate the decision-making process regarding the suitability of a battery for reuse at the end of its first life. Once the battery passport has been completed, the performance and characteristics of the battery are compared with the requirements of several mobile applications. Mobile charging stations and forklift trucks were identified as relevant applications for the reuse of high-capacity prismatic cells. Finally, a definition of the state of health (SoH) is proposed to track the suitability of the battery during use in the second-life application considering not only the energy but also the power and efficiency of the battery. This SoH shows that even taking into account accelerated ageing data, a repurposed battery can have an extended life of 11 years at 25 °C. It has also been shown that energy fade is the most limiting performance factor for the lifetime and that cell-to-cell variation should be tracked as it has been shown to have a significant impact on the battery life. Full article

For most patients admitted to a hospital, it is a requirement to continuously monitor their vital signs. Among these are the waveforms from ECG and the pulmonary arterial pulse. At present, there are several electronic devices that can measure the arterial pulse waveform. However, they can be affected by electromagnetic wave radiation, and the fabrication of electronic sensors is complicated and contributes to the e-waste, among other problems. In this paper, we propose an optical method to measure arterial pulse based on a Fabry-Perot interferometer composed of two mirrors. A pulse sensor formed by an acrylic cell with a thin membrane is used to gather the vasodilatation of the wrist, forming an air pulse that is enacted by means of a tube to a metallic cell containing a mirror that is glued to a thin silicone membrane. When the air pulse arrives, a displacement of the mirror takes place and produces a shift of the interference pattern fringes given by the Fabry-Perot. A detector samples the fringe intensity. With this method, an arterial pulse waveform is obtained. We characterize this optical device as a test of concept, and its application to measuring artery pulse is presented. The optical device is compared to other electronic devices. Full article

This work presents the research results on the development of an innovative, hydrometallurgical technology for the production of manganese(II) perrhenate dihydrate from recycled waste. These wastes are scraps of Ni-based superalloys containing Re and scraps of Li–ion batteries containing Mn—specifically, solutions from the leaching of black mass. This work presents the conditions for the production of Mn(ReO₄)₂·2H₂O. Thus, to obtain Mn(ReO₄)₂·2H₂O, manganese(II) oxide was used, precipitated from the solutions obtained after the leaching of black mass from Li–ion batteries scrap and purified from Cu, Fe and Al (pH = 5.2). MnO₂ precipitation was carried out at a temperature < 50 °C for 30 min using a stoichiometric amount of KMnO₄ in the presence of H₂O₂. MnO₂ precipitated in this way was purified using a 20% H₂SO₄ solution and then H₂O. Purified MnO₂ was then added alternately with a 30% H₂O₂ solution to an aqueous HReO₄ solution. The reaction was conducted at room temperature for 30 min to obtain a pH of 6–7. Mn(ReO₄)₂·2H₂O precipitated by evaporating the solution to dryness was purified by recrystallization from H₂O with the addition of H₂O₂ at least twice. Purified Mn(ReO₄)₂·2H₂O was dried at a temperature of 100–110 °C. Using the described procedure, Mn(ReO₄)₂·2H₂O was obtained with a purity of >99.0%. This technology is an example of the green transformation method, taking into account the 6R principles. Full article

Medulloblastomas comprise a molecularly diverse set of malignant pediatric brain tumors in which patients are stratified according to different prognostic risk groups that span from very good to very poor. Metastasis at diagnosis is most often a marker of poor prognosis and the relapse incidence is higher in these children. Medulloblastoma relapse is almost always fatal and recurring cells have, apart from resistance to standard of care, acquired genetic and epigenetic changes that correlate with an increased dormancy state, cell state reprogramming and immune escape. Here, we review means to carefully study metastasis and relapse in preclinical models, in light of recently described molecular subgroups. We will exemplify how therapy resistance develops at the cellular level, in a specific niche or from therapy-induced secondary mutations. We further describe underlying molecular mechanisms on how tumors acquire the ability to promote leptomeningeal dissemination and discuss how they can establish therapy-resistant cell clones. Finally, we describe some of the ongoing clinical trials of high-risk medulloblastoma and suggest or discuss more individualized treatments that could be of benefit to specific subgroups. Full article

Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies. Full article

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. Full article

Social media recruitment and online surveys are valuable tools in social science research, but their effectiveness in reaching seldom-heard victims of gender violence in low-middle income (LMI) countries is under-explored. This empirical study aims to: (1) describe violence and abuse experiences and (2) assess the benefits and limitations of using social media to document violence against women and girls (VAWGs) in a LMI country to render visible the experiences of potentially isolated victims. A total of 453 Iranian women (aged 14–59, mean = 28.8, SD = 8.04) responded to an Instagram invitation for a study on women’s health and violence exposure from February 2020 to January 2022. The questionnaire covered general gendered abuse, domestic violence (DV), and forced unemployment. The analysis was performed using Stata 17. Nearly all participants reported abuse, including sexual (85.0%), psychological (83.4%), and technology-facilitated (57.4%) abuse, with 77.4% experiencing multiple forms. The street (62%) and home (52.8%) were common abuse locations. The perpetrators included known individuals (75.9%) and strangers (80.8%), with 56.7% reporting abuse by both. DV was reported by 72.6%, mainly involving psychological (73.1%), physical (53.4%), and/or sexual (17.2%) violence, with fathers (47.8%), husbands (42.7%), and brothers (40.2%) as frequent perpetrators. A quarter reported forced unemployment. Those experiencing DV and/or forced unemployment showed higher depression levels, suicidal ideation, and lower marital satisfaction. The study suggests using social media recruitment for VAWG research but cautions against overgeneralising from these data. Full article

(1) Background: Walnut consumption has been associated with having a positive effect on controlling and/or reducing the co-morbidities associated with cardiovascular disease (CVD). The effects of consuming walnuts of Portuguese origin on risk factors related to CVD were evaluated by measuring glucose, urea, TC, HDL-C, LDL-C, TG, AST, and ALT levels, anthropometric profiles, and blood pressure. (2) Methods: This trial study involved 24 volunteers, both female (n = 15) and male (n = 9), from Fernando Pessoa University, Porto. It consisted of a daily intake of 25 g of walnut kernels over a period of 45 days. Before and after intake, biochemical parameters, BMI and BP were measured. (3) Results: Despite the intake of nuts revealing a reduction in mean values of most of the parameters assessed, a significant drop was only observed in AST (p = 0.04).There was also a significant reduction in the mean values for Glu (p = 0.01), UR (p = 0.01) and HDL-C (p = 0.02) for women but not for men. (4) Conclusions: The dose and the period of intake were not effective in lowering the lipid profile but may have had a protective effect on liver function. The benefits were greater in women than in men. Full article

The Shuangjianzishan silver polymetallic deposit is located in the copper–tin–lead–zinc–silver polymetallic metallogenic belt in the Southern Great Xing’an Range, with silver resources of more than 18,000 t, which is the largest silver polymetallic deposit in Asia. Early studies concluded that the Shuangjianzishan deposit is typically an epithermal Ag-Pb-Zn deposit that lacks a high-temperature mineralization stage. In recent years, with the deepening of research, a large amount of Cu-Sn mineralization has been found in the deep part of the Shuangjianzishan deposit, but it is less studied. The laser-ablation inductively coupled mass spectroscopy (LA-ICP-MS) technique is used to investigate the distribution and substitution of trace elements in chalcopyrite and cassiterite. In this paper, the trace element study of chalcopyrite and cassiterite from the Shuangjianzishan deposit reveals that Sn, In, As, Se, Sb, and Tl mainly exist in chalcopyrite in isomorphic form, while Pb, Bi, and Ni mainly exist in chalcopyrite in the form of mineral inclusions. The enrichment of the high-temperature elements Sn and Se in chalcopyrite, and the deficit of the middle- and low-temperature elements Ga, Sb, etc., reflect that the chalcopyrite in the Shuangjianzishan deposit was formed in a middle- and high-temperature environment, and it also indicates that the early ore-forming hydrothermal solution may be rich in Sn. Fe, In, Co, and Ni mainly exist in cassiterite in isomorphic form, and the content of W in cassiterite is high. There are two main forms, one is isomorphic and the other is wolframite inclusion. Cassiterite has Fe-rich and W-U-poor characteristics, indicating that cassiterite from the Shuangjianzishan deposit was formed under relatively oxidized conditions, and the relative enrichment of elements such as Fe, W, Zr, and Hf indicates that the temperature of cassiterite formation was high. The elemental content and inter-ionic coupling relationships suggest that the cassiterite from the Shuangjianzishan deposit may have an elemental replacement mechanism of W⁶⁺ + Fe²⁺ ↔ 2Sn⁴⁺ or Fe³⁺ + OH⁻ ↔ Sn⁴⁺ + O²⁻. The trace elements in cassiterite of the Shuangjianzishan deposit are rich in Fe and Mn and depleted in Nb and Ta, according to the Fe-W diagram, and the tin mineralization of the Shuangjianzishan deposit belongs to cassiterite–sulfide-type tin mineralization. Chalcopyrite Co/Ni ratios >1 are consistent with the characteristics of chalcopyrite genesis in hydrothermal deposits. Full article

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty. Full article

We propose an efficient scheme to enhance the generation of optical second-order sidebands (OSSs) in an atom-assisted optomechanical system. The cavity field is coupled with a strong driving field and a weak probe field, and a control field is applied to the atom. We use the steady-state method to analyze the nonlinear interaction in the system, which is different from the traditional linear analysis method. The existence of an auxiliary three-level atom driven by the control field significantly enhances the generation of an OSS. It is found that the efficiency of the OSS can be effectively modulated by adjusting the Rabi frequency of the control field, optomechanical cooperativity and atomic coupling strength. Our scheme provides a promising solution for controlling light propagation and has potential application in quantum optical devices and quantum information networks. Full article

Background: The natural history of spinal muscular atrophy (SMA) is well understood, with progressive muscle weakness resulting in declines in function. The development of contractures is common and negatively impacts function. Clinically, joint hypermobility (JH) is observed but is poorly described, and its relationship with function is unknown. Methods: Lower-limb ROM (range of motion) assessments of extension and flexion at the hip, knee, and ankle were performed. ROMs exceeding the published norms were included in the analysis. The functional assessments performed included the six-minute walk test (6 MWT) and the Hammersmith Functional Motor Scale—Expanded (HFMSE). Results: Of the 143 participants, 86% (n = 123) had at least one ROM measure that was hypermobile, and 22% (n = 32) had three or more. The HFMSE scores were inversely correlated with hip extension JH (r = −0.60, p = 0.21; n = 6) and positively correlated with knee flexion JH (r = 0.24, p = 0.02, n = 89). There was a moderate, inverse relationship between the 6 MWT distance and ankle plantar flexion JH (r = −0.73, p = 0.002; n = 15). Conclusions: JH was identified in nearly all participants in at least one joint in this study. Hip extension, knee flexion and ankle plantar flexion JH was associated with function. A further understanding of the trajectory of lower-limb joint ROM is needed to improve future rehabilitation strategies. Full article

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. Full article