bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of PLGA-PEG-PLGA Nanoparticles to Percutaneous Immunotherapy for Food Allergy
Authors: Sakurai Ryuse , Iwata Hanae , Gotoh Masaki , Ogino Hiroyuki , Takeuchi Issei , Makino Kimiko , Itoh Fumio , Saitoh Akiyoshi
Published: August 2024

Compared with oral or injection administration, percutaneous immunotherapy presents a promising treatment modality for food allergies, providing low invasiveness and safety. This study investigated the efficacy of percutaneous immunotherapy using hen egg lysozyme (HEL)-loaded PLGA-PEG-PLGA nanoparticles (NPs), as an antigen model protein derived from egg white, compared with that of HEL-loaded chitosan hydroxypropyltrimonium chloride (CS)-modified PLGA NPs used in previous research. The intradermal retention of HEL in excised mouse skin was measured using Franz cells, which revealed a 2.1-fold higher retention with PLGA-PEG-PLGA NPs than that with CS-modified PLGA NPs. Observation of skin penetration pathways using fluorescein-4-isothiocyanate (FITC)-labeled HEL demonstrated successful delivery of HEL deep into the hair follicles with PLGA-PEG-PLGA NPs. These findings suggest that after NPs delivery into the skin, PEG prevents protein adhesion and NPs aggregation, facilitating stable delivery deep into the skin. Subsequently, in vivo percutaneous administration experiments in mice, with concurrent iontophoresis, demonstrated a significant increase in serum IgG1 antibody production with PLGA-PEG-PLGA NPs compared with that with CS-PLGA NPs after eight weeks of administration. Furthermore, serum IgE production in each NP administration group significantly decreased compared with that by subcutaneous administration of HEL solution. These results suggest that the combination of PLGA-PEG-PLGA NPs and iontophoresis is an effective percutaneous immunotherapy for food allergies.
Randomized Clinical Trial to Evaluate the Effect of Probiotic Intake on Androgenic Alopecia
Authors: García-Navarro Alejandro , Vasallo-Morillas María Isabel , Navarro-Belmonte Roge , Vilanova Cristina , Torrent Daniel , Kilasoniya Alina , Moles-Ugeda Isabel , Gallego-Herrera Estefanía , Ramírez-Boscá Ana
Published: August 2024

This study aimed to assess the impact of a combination of probiotic strains of Lactiplantibacillus on the treatment of androgenic alopecia (AGA). To this end, 136 individuals with AGA (62 men and 74 women) aged 18–65 years were enrolled in a double-blind, parallel-group clinical trial. A total of 115 individuals (57 in the probiotic group and 58 in the placebo group) completed this study within a 16-week intervention period. Capillary density, thickness, and length of hair were analyzed before and after the intervention using FotoFinder Trichoscale Pro. In addition, the gut microbiota was assessed by paired-end sequencing on the Illumina MiSeq platform (2 × 300 bp). At the conclusion of the treatment period, a notable decline (p < 0.05) in the number of telogen hairs was evident in the probiotic group while hair thickness decreased in the placebo group (p < 0.05). However, the remaining variables did not exhibit any statistically significant changes. In the probiotic-treated group, individuals aged less than 37.5 years exhibited a reduction in the number and density of telogen hair (p = 0.0693 and p = 0.0669, respectively) and an increase in hair length (p = 0.0871). Furthermore, a notable decline in the number and density of vellus hair (p < 0.05) was observed, and this was accompanied by no change in the hair thickness. The probiotic-treated group exhibited a significantly higher abundance of Lactobacillus (p-adjusted < 0.05, DEseq2 test) and demonstrated a notable reduction in the number and density of telogen hair, and this was accompanied by an increase in the percentage of anagen hair. The probiotic mixture was well tolerated by the participants, with a treatment adherence rate of 90%. In light of this study’s limitations, it can be concluded that a mixture of three strains of Lactiplantibacillus promotes the presence of terminal follicles, preventing their gradual miniaturization, which is a characteristic of AGA.
Adapting an Online Guided Self-Help CBT Programme Targeting Disordered Eating for Students in Aotearoa New Zealand: A Qualitative Study
Authors: Mitlash Alisa , Rapsey Charlene , Treharne Gareth J. , Scarf Damian
Published: August 2024

Disordered eating is a significant issue in university student populations. Currently, access to interventions is limited. Online interventions present an innovative way to increase accessibility to treatment for those in need. The current study explored how an online intervention for disordered eating (everyBody) could be modified to suit the needs of university students in Aotearoa New Zealand. Aotearoa New Zealand is a unique cultural context, with an indigenous population that has a high incidence rate of disordered eating, highlighting the need to adapt everyBody to the local context. Individual interviews were conducted with nine students currently at university in Aotearoa New Zealand, aged between 18 and 33 years old (five females, four males). Three first-order themes were identified using template analysis. The themes indicate that participants perceived the programme as acceptable and feasible for use with Aotearoa New Zealand’s university student population. Furthermore, the themes provide insight into potential adaptions to the programme to facilitate engagement and uptake. The suggested changes were largely consistent with previous research on E-therapy design (e.g., content length, therapeutic alliance), and also highlight changes specific to fit Aotearoa New Zealand’s cultural context. The findings have implications for universities and other funders deciding on services for students with disordered eating and eating disorders.
Algerian Prickly Pear Seed By-Products: Fatty Acids Composition, Antioxidant, Enzyme Inhibitory Activities towards Tyrosinase, Urease, α-Amylase, and Cholinesterase, along with the Ability to Protect from Thermal Protein Denaturation
Authors: Chafaa Nassiba , Mosbah Camelia , Khattabi Latifa , Malaoui Karima , Zahnit Wafa , Smaali Mohamed El Amine , Houri Faiza , Medfouni Yazid , Al-Anazi Khalid Mashay , Ali Ahmad
Published: August 2024

Prickly pear seed is a source of the most expensive oil in the world, which is rich in vitamins and polyunsaturated fatty acids. Its extraction generates a large quantity of press cake. These two by-products need to be valued. The current study aimed to assess the fatty acid composition of oil and the phytochemical composition of press cake. In addition, the antioxidant and the inhibition of thermal protein denaturation effects of both Algerian seed by-products were evaluated with their inhibitory action against the activities of urease, tyrosinase, α-amylase, and cholinesterase enzymes. The GC MS analysis result revealed the richness of our oil in linoleic (74%) and palmitic (13%) acids methyl esters, respectively. The chemical composition of press cake was characterized by a high value of dry matter (94.94 ± 0.05%), especially the carbohydrates (85.13 ± 0.94%). The results of antioxidant activity presented by IC50 and A0.5 ranged from 7.51 ± 0.03 to 88.10 ± 0.92 µg/mL. Furthermore, the IC50 values were 40.19 ± 1.21 and 61.18 ± 0.03 µg/mL in thermal protein denaturation assay, and ranging from 22.97 ± 0.72 to 385.99 ± 0.27 µg/mL for the inhibition of enzymatic activities. These results indicate that the studied oil can be one of the strongest oils for its impressive effects and also encourage us to reuse its press cake in feed livestock.
Phenylethanol Glycoside from Cistanche tubulosa Attenuates BSA-Induced Liver Fibrosis in Rats by Modulating the Gut Microbiota–Liver Axis
Authors: Qi Xinxin , Sun Hongguang , Liu Jincun , Cong Meili , Zhang Xinxuan , Yan Yuxin , Xia Zhaolin , Liu Tao , Zhao Jun
Published: August 2024

This study aimed to investigate the effect of phenylethanol glycoside from Cistanche tubulosa (CPhGs) on the prevention of bovine serum albumin (BSA)-induced hepatic fibrosis in rats. Investigation of the mechanisms of the anti-hepatic fibrosis effect was focused on CPhGs’ influence on the “gut–liver” regulation, including the gut microbiota, intestinal barrier, systemic lipopolysaccharide (LPS) concentration, and LPS-related signaling pathway. The results show that CPhGs restored the diversity of gut microbiota, increased the relative abundance of Bacteroidetes, and decreased the relative abundance of Firmicutes and Proteobacteria in the fibrotic rats. In addition, CPhGs promoted the enrichment of probiotics such as Blautia, Oscillospira, Ruminococcus, Odoribacter, Bacteroides, and Parabacteroides in intestines of these rats. Furthermore, CPhGs reduced histopathological injury in the intestine and restored the tight junctions of the intestine by increasing the expression of ZO-1, occludin, and E-cadherin. CPhGs efficiently reduced serum LPS and liver lipopolysaccharide-binding protein (LBP) levels and inhibited the LPS-TLR4/MyD88/NF-κB pathway, which is related to protein expression in the liver. Correlation analysis confirmed that these beneficial bacteria were negatively associated with pathological damage, while LPS and harmful bacteria were positively associated with liver injury. Our fecal microbiota transplantation (FMT) experiment confirmed that gut microbiota is an important part of disease progression and that CPhGs is useful for the prevention and treatment of hepatic fibrosis. Our data demonstrate that the anti-hepatic fibrosis mechanism of CPhGs was mediated by regulation of the “gut–liver” axis. These results can stimulate consideration for its use in clinical practices.
Nutrient Allocation and Growth Responses of Senegalia polyphylla under Varied Fertilizer Regimes for Effective Forest Recovery
Authors: Araújo Fillipe Vieira de , Duque Tayna Sousa , Ferreira Evander Alves , Pereira Israel Marinho , Souza Iasmim Marcella , Oliveira Fernanda Santos , dos Santos José Barbosa
Published: August 2024

To restore invaded areas, planting fast-growing native species such as Senegalia polyphylla (DC.) Britton & Rose (Fabaceae) is widely used. However, invasive grasses reduce light availability, alter fire regimes, and compete for water and nutrients, hindering the growth of native trees. Fertilization practices influence the competition dynamics between natives and invasives by altering soil fertility. Therefore, this study investigated the effects of mineral and organic fertilization on the nutritional status and growth of S. polyphylla cultivated during the first 120 days after transplanting. The experiment was conducted in a completely randomized design comprising five treatments and four replications, along with the unfertilized control (0–0%) as an additional treatment. Dystrophic red latosol and different proportions of mineral and organic fertilizers were used. The variables evaluated included dry mass of aboveground parts and roots, nutrient content in leaves, and nutrient use efficiency. The results showed that fertilizations with high nutrient concentrations (100–0% and 75–25%) resulted in greater accumulation of N, P, and K in the leaves, while balanced fertilization (50–50% and 25–75%) led to greater root dry mass. These results emphasize the importance of strategically choosing fertilizer formulations to promote the healthy development of seedlings in areas subject to interference from invasive grasses.
Digestate Improves Stinging Nettle (Urtica dioica) Growth and Fiber Production at a Chlor-Alkali Site
Authors: Viotti Chloé , Bertheau Coralie , Martz Françoise , Yung Loïc , Placet Vincent , Ferrarini Andrea , Fornassier Flavio , Blaudez Damien , Puschenreiter Markus , Chalot Michel
Published: August 2024

Marginal lands have been proposed to produce non-food crop biomass for energy or green materials. For this purpose, the selection, implementation, and growth optimization of plant species on such lands are key elements to investigate to achieve relevant plant yields. Stinging nettle (Urtica dioica) is a herbaceous perennial that grows spontaneously on contaminated lands and was described as suitable to produce fibers for material applications. Two mercury-contaminated soils from industrial wastelands with different properties (grassland soil and sediment landfill) were used in this study to assess the potential growth of stinging nettle in a greenhouse mesocosm experiment. Two organic amendments were studied for their impact on nettle growth. The solid digestate from organic food wastes significantly doubled plant biomass whereas the compost from green wastes had a lower impact. The highest doses of organic amendments significantly increased the number of fibers, which doubled following digestate application, while reducing leaf Hg concentration. Both amendments significantly improved soil respiration and enzymatic activities linked to the microbial biomass in the soil from the sediment landfill by the end of the experiment. In the context of a phytomanagement scenario, solid digestate would be a preferred amendment resource to improve nettle production on industrial wastelands.
Conservation and Divergence of PEPC Gene Family in Different Ploidy Bamboos
Authors: Cheng Wenlong , Xu Junlei , Mu Changhong , Jiang Jutang , Cheng Zhanchao , Gao Jian
Published: August 2024

Phosphoenolpyruvate carboxylase (PEPC), as a necessary enzyme for higher plants to participate in photosynthesis, plays a key role in photosynthetic carbon metabolism and the stress response. However, the molecular biology of the PEPC family of Bambusoideae has been poorly studied, and the function of its members in the growth and development of Bambusoideae is still unclear. Here, we identified a total of 62 PEPC family members in bamboo. All the PEPC genes in the bamboo subfamily were divided into twelve groups, each group typically containing significantly fewer PEPC members in Olyra latifolia than in Phyllostachys edulis, Dendrocalamus latiflorus and Dendrocalamus brandisii. The results of an intraspecific and interspecies collinearity analysis showed that fragment replication and whole genome replication were the main driving forces of bamboo PEPC members. Furthermore, the Ka/Ks values of collinear genes showed that bamboo PEPC experienced purification selection. In addition, the promoter region of PEPC genes contains cis-acting elements related to light response, plant hormone response and response to stress. An analysis of the expression levels of the PEPC family in different developmental stages and tissues of bamboo shoots has shown that PhePEPC7, PhePEPC9 and PhePEPC10 were highly expressed in the leaves of non-flowering plants and culms. Furthermore, PhePEPC6 was significantly upregulated in leaves after GA treatment. Further research has shown that PhePEPC6 was mainly localized in the cell membrane. This provides a solid bioinformatics foundation for further understanding the biological functions of the bamboo PEPC family.
Characterization of Male Flower Induction by Silver Thiosulfate Foliar Spray in Female Cannabis at the Middle Reproductive Stage for Breeding
Authors: Kim Juyoung , Kim Dong-Gun , Kim Woon Ji , Lee Ye-Jin , Lee Seung Hyeon , Ryu Jaihyunk , Kim Jae Hoon , Kim Sang Hoon
Published: August 2024

Cannabis (Cannabis sativa) is a versatile crop belonging to the Cannabaceae family, and is dioecious, typically with separate male and female plants. The flowers of female plants, especially the trichomes, accumulate relatively higher contents of cannabinoids compared with those of male plants. For this reason, to obtain seeds that are genetically female, it is desirable to induce the development of male flowers on a female plant that produces genetically female haploid gametes. Silver thiosulfate (STS) is a highly effective chemical for male flower induction. We investigated male flower induction in three commercial cultivars of female cannabis (Spectrum303, SuperwomanS1, and CBGambit) regarding the treatment frequency, stage of application, and concentration of STS applied as a foliar spray. All three cultivars showed adequate induction of male flowers in response to 1.5 mM STS applied at the early reproductive stage. In particular, SuperwomanS1 was most highly responsive to induction of male flowers, even when treated with 0.3 mM STS at the early reproductive stage. Treatment with three applications of STS was more effective compared with a single application, but a single application of 1.5 mM STS at the early reproductive stage was sufficient for male flower induction. A single STS application during the middle stage of reproductive growth was inadequate for induction of male flowers. However, 6 weeks after three applications of STS, CBGambit exhibited approximately 54% male flower induction at 0.3 mM STS, Spectrum303 showed approximately 56% induction at 3 mM STS, and SuperwomanS1 yielded approximately 26% induction at 1.5 mM (expressed as percentage of total number of individuals with the induced male flowers). Pollen stainability tests using KI-I2 solution and Alexander’s staining showed high pollen viability with over 65% at different single STS concentrations, indicating that pollen grains induced by STS have sufficient viability for the self-pollination. This study demonstrated that different cultivars of cannabis respond diversely to different STS concentrations and highlighted the potential benefits of three STS applications during the middle reproductive stage for cannabis breeding.
Physiological and Proteomic Analysis of Various Priming on Rice Seed under Chilling Stress
Authors: Zhang Hua , Hui Guo , Gao Guoqing , Ali Izhar , Tang Maoyan , Chen Lei , Zhong Xiaoyuan , Jiang Ligeng , Liang Tianfeng , Zhang Xiaoli
Published: August 2024

Rice (Oryza sativa L.) cultivation using direct seeding is susceptible to chilling stress, particularly during seed germination and early seedling growth in the early season of a double cropping system. Alternatively, seed priming with various plant growth-promoting hormones is an effective technique to promote rapid and uniform emergence under chilling stress. Therefore, we evaluated the impact of gibberellin A3 (GA3) and brassinolide (BR) priming on rice seed emergence, examining their proteomic responses under low-temperature conditions. Results indicated that GA3 and BR increased the seed germination rate by 22.67% and 7.33% at 72 h and 35% and 15% at 96 h compared to the control (CK), respectively. Furthermore, proteomic analysis identified 2551, 2614, and 2592 differentially expressed proteins (DEPs) in GA, BR, and CK, respectively. Among them, GA exhibited 84 upregulated and 260 downregulated DEPs, while BR showed 112 upregulated and 102 downregulated DEPs, and CK had 123 upregulated and 81 downregulated DEPs. Notably, under chilling stress, both GA3 and BR are involved in peroxide metabolism, phenylpropanoid biosynthesis, and inositol phosphate metabolism, enhancing antioxidant capacity and providing energy substances for germination. In addition, GA3 triggers the specific regulation of stress responsive protein activation, GTP activation, and ascorbic acid biosynthesis and promotes the stability and integrity of cell membranes, as well as the synthesis of cell walls, providing physical defense for seeds to resist low temperatures. At the same time, BR triggers specific involvement in ribosome synthesis and amino acid synthesis, promoting biosynthetic ability and metabolic regulation to maintain plant life activities under low-temperature stress. Furthermore, the various genes’ expression (OsJ_16716, OsPAL1, RINO1) confirmed GA3 and BR involved in peroxide metabolism, phenylpropanoid biosynthesis, and inositol phosphate metabolism, enhancing antioxidant capacity and providing energy substances for germination. This study provides valuable insights into how rice seed embryo responds to and tolerates chilling stress with GA3 seed priming.
Soil Seed Bank of the Alpine Endemic Carnation, Dianthus pavonius Tausch (Piedmont, Italy), a Useful Model for the Study of Host–Pathogen Dynamics
Authors: Carasso Valentina , Bruns Emily L. , Antonovics Janis , Hood Michael E.
Published: August 2024

Soil seedbanks are particularly important for the resiliency of species living in habitats threatened by climate change, such as alpine meadows. We investigated the germination rate and seedbank potential for the endemic species Dianthus pavonius, a carnation native to the Maritime Alps that is used as model system for disease in natural populations due to its frequent infections by a sterilizing anther-smut pathogen. We aimed to ascertain whether this species can create a persistent reserve of viable seeds in the soil which could impact coevolutionary dynamics. Over three years, we collected data from seeds sown in natural soil and analyzed their germination and viability. We found that D. pavonius seeds are not physiologically dormant and they are able to create a persistent soil seed bank that can store seeds in the soil for up to three years, but lower than the estimated plant lifespan. We conclude that while the seedbank may provide some demographic stability to the host population, its short duration is unlikely to strongly affect the host’s ability to respond to selection from disease. Our findings have implications for the conservation of this alpine species and for understanding the evolutionary dynamics between the host and its pathogen.
Experimental Research on Prediction of Remaining Using Life of Solar DC Centrifugal Pumps Based on ARIMA Model
Authors: Hu Qin , Wang Jianbao , Xiong Jing , Zhang Meng , Fu Hua , Pei Ji , Wang Wenjie
Published: August 2024

In order to improve the stability and reliability of the solar DC centrifugal pump real-time operation and prevent the centrifugal pump failure caused by the unexpected shutdown of the system, a set of accurate and efficient centrifugal pump condition monitoring systems was built. A time series-based strategy for predicting the remaining using life (RUL) of centrifugal pumps was proposed. The time series of head and efficiency of centrifugal pumps at specific flow conditions were measured, the corresponding failure thresholds were set, and different differential autoregressive integrated moving average (ARIMA) models were developed to predict the remaining useful life of the pumps. The results show that the maximum prediction error of the head ARIMA model established under the design conditions of the pump was 0.040%, and the head time series reaches the failure threshold of 8 m at the 653rd data point; the maximum prediction error of the efficiency ARIMA model was 0.042%, and the efficiency time series reaches the failure threshold of 16% at the 672nd data point. According to the proposed prediction strategy, the RUL of the centrifugal pump under the design condition was 53 h. The head time series of the pump at high flow conditions reaches a failure threshold of 5 m at the 640th data point; the efficiency time series will reach a failure threshold of 12.5% at the 578th data point, and the RUL of the centrifugal pump at high flow conditions was 78 h. The established ARIMA model has a high prediction accuracy and can effectively predict the RUL of centrifugal pumps.
Post-Holocaust Immigration and Hassidic Leadership: The Cases of Viznitz and Satmar
Authors: Keren-Kratz Menachem
Published: August 2024

Immigration, particularly forced immigration, has a profound impact on every aspect of immigrants’ lives. One such aspect is their religious convictions and practices. Nowadays, Migration Studies is a major academic field that produces many books and articles each year. This article examines the impact of forced immigration on the daily practices and internal relationships between leaders and followers of a specific religious group—Hassidism, in one particular period—the early second half of the 20th century. It does so by examining how two Hassidic leaders, the Satmar Rebbe in America and the Viznitzer Rebbe in Israel, established their communities after the Holocaust. This is one of only a few academic studies that explore post-Holocaust Hassidism, with a specific focus on the effects of forced immigration on its development. Throughout Jewish history, large-scale immigration and the inevitable need to adapt to new political, religious, and cultural circumstances had a profound influence on the way Jews conducted their religious affairs. This article explores how the uprooting of Hassidism from Eastern Europe after the Holocaust and its transplantation in countries that were new to them prompted Hassidic leaders who wanted to reestablish their communities to adopt a new set of leadership priorities. The result was that despite bearing the same title, Hassidic communities that were established after the Holocaust were very different from those that operated in Europe previously.
A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
Authors: Iqbal Farhat , Koutmos Dimitrios , Ahmed Eman A. , Al-Essa Lulwah M.
Published: August 2024

The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.
Tetherbot: Experimental Demonstration and Path Planning of Cable-Driven Climbing in Microgravity
Authors: Harms Simon , Bizcocho Carlos Giese , Wakizono Hiroto , Murasaki Kyosuke , Kawagoe Hibiki , Nagaoka Kenji
Published: August 2024

In this paper, we introduce Tetherbot, a cable-driven climbing robot designed for microgravity environments with sparse holding points, such as space stations or asteroids. Tetherbot consists of a platform with a robotic arm that is suspended via cables from multiple grippers. It achieves climbing locomotion by alternately positioning the platform with the cables and relocating the grippers with the robotic arm from one holding point to the next. The main contribution of this work is the first experimental demonstration of autonomous cable-driven climbing in an environment with sparse holding points. To this end, we outline the design, kinematics, and statics of the Tetherbot and present a path planning algorithm to relocate the grippers. We demonstrate autonomous cable-driven climbing through an experiment conducted in a simulated microgravity environment using the path planning algorithm and a prototype of the robot. The results showcase Tetherbot’s ability to achieve autonomous cable-driven climbing locomotion, thereby demonstrating that cable-driven climbing is a viable concept and laying the foundation for future robots of this type.
Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods
Authors: Tasneem Sumaiya , Islam Kazi Aminul
Published: August 2024

Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly against adversarial attacks. In adversarial attacks, attackers manipulate benign data to create a perturbation to mislead AI models into predicting incorrect decisions, posing a catastrophic threat to the security of their applications, particularly in crucial decision-making contexts. These attacks pose a significant threat to the integrity and comprehensiveness of AI models in remote sensing applications, as they can lead to inaccurate decisions with substantial consequences. In this paper, we propose to develop an adversarial robustness technique that will ensure the AI model’s accurate prediction in the presence of adversarial perturbation. In this work, we address these challenges by developing a better adversarial training approach using explainable AI method-guided features and data augmentation techniques to strengthen the AI model prediction in remote sensing data against adversarial attacks. The proposed approach achieved the best adversarial robustness against Project Gradient Descent (PGD) attacks in EuroSAT and AID datasets and showed transferability of robustness against unseen attacks.
South-to-North Water Diversion Halting Long-Lived Subsidence in Tianjin, North China Plain
Authors: Jiang Zhongshan , Zhu Juyan , Guo Haipeng , Qiu Keshan , Tang Miao , Yang Xinghai , Liu Jinyu
Published: August 2024

The South-to-North Water Diversion Project in China is the world’s largest water transfer project, aiming to address water shortages in northern China by channeling water from the water-rich southern regions. Water resources in Tianjin have long been in severe deficit, with excessive groundwater extraction causing significant surface subsidence, negatively impacting urban infrastructure and economic development. As a result, Tianjin has become a key beneficiary of this water diversion project. To investigate the current situation of surface subsidence, we obtained the vertical displacement time series from 21 GNSS stations across Tianjin from 2011 to 2021 and analyzed overall subsidence changes and rehabilitation status. Results indicate that no clear surface subsidence was observed in the northern regions of Tianjin due to groundwater extraction mainly in unconfined aquifers. The southwestern region experienced the most significant surface subsidence due to overexploitation of deep groundwater, with peak cumulative subsidence exceeding 600 mm during the study period. The central, eastern, and southeastern coastal regions also faced severe surface subsidence with cumulative amounts ranging from 100 mm to 400 mm. The alleviation of subsidence predominantly benefits from continuous water supply from the South to North Water Diversion Project, which resulted in most stations significantly slowing down or even stabilizing their settlement rates after 2018. Therefore, the South-to-North Water Diversion Project plays a crucial role in addressing the persistent water resource shortage and mitigating long-term surface subsidence in Tianjin by ensuring a continuous water supply and significantly reducing the need for groundwater extraction. Our findings indicate positive measures, such as water diversion projects and water management policies, can serve as valuable references for other regions around the world facing similar water scarcity and groundwater overexploitation.
Radar Anti-Jamming Performance Evaluation Based on Logistic Fusion of Multi-Stage SIR Information
Authors: Zhao Linqi , Yan Liang , Duan Xiaojun , Wang Zhengming
Published: August 2024

When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model that characterizes the relationship between the signal-to-interference ratio (SIR) and the anti-jamming rate. The logistic curve’s inflection point and growth rate serve as crucial indices for evaluating radar anti-jamming performance, providing insights into the SIR threshold for successful jamming mitigation. The proposed model allows for the derivation of posterior distributions for these parameters using the MCMC sampling method and kernel density estimation. It also enables the fusion of anti-jamming data from multiple stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Through extensive simulations, our method achieves a remarkably low root mean square error (RMSE) of 0.0552. Compared with a conventional BETA fusion model, our proposed logistic fusion approach demonstrates superior performance and robustness in accurately estimating the anti-jamming rate. The fusion of multi-stage data, even with varying levels of reliability, improves the overall accuracy of the performance evaluation.
Lake Surface Temperature Predictions under Different Climate Scenarios with Machine Learning Methods: A Case Study of Qinghai Lake and Hulun Lake, China
Authors: Li Zhenghao , Zhang Zhijie , Xiong Shengqing , Zhang Wanchang , Li Rui
Published: August 2024

Accurate prediction of lake surface water temperature (LSWT) is essential for understanding the impacts of climate change on aquatic ecosystems and for guiding environmental management strategies. Predictions of LSWT for two prominent lakes in northern China, Qinghai Lake and Hulun Lake, under various future climate scenarios, were conducted in the present study. Utilizing historical hydrometeorological data and MODIS satellite observations (MOD11A2), we employed three advanced machine learning models—Random Forest (RF), XGBoost, and Multilayer Perceptron Neural Network (MLPNN)—to predict monthly average LSWT across three future climate scenarios (ssp119, ssp245, ssp585) from CMIP6 projections. Through the comparison of training and validation results of the three models across both lake regions, the RF model demonstrated the highest accuracy, with a mean MAE of 0.348 °C and an RMSE of 0.611 °C, making it the most optimal and suitable model for this purpose. With this model, the predicted LSWT for both lakes reveals a significant warming trend in the future, particularly under the high-emission scenario (ssp585). The rate of increase is most pronounced under ssp585, with Hulun Lake showing a rise of 0.55 °C per decade (R2 = 0.72) and Qinghai Lake 0.32 °C per decade (R2 = 0.85), surpassing trends observed under ssp119 and ssp245. These results underscore the vulnerability of lake ecosystems to future climate change and provide essential insights for proactive climate adaptation and environmental management.
Semantic Interaction Meta-Learning Based on Patch Matching Metric
Authors: Wei Baoguo , Wang Xinyu , Su Yuetong , Zhang Yue , Li Lixin
Published: August 2024

Metric-based meta-learning methods have demonstrated remarkable success in the domain of few-shot image classification. However, their performance is significantly contingent upon the choice of metric and the feature representation for the support classes. Current approaches, which predominantly rely on holistic image features, may inadvertently disregard critical details necessary for novel tasks, a phenomenon known as “supervision collapse”. Moreover, relying solely on visual features to characterize support classes can prove to be insufficient, particularly in scenarios involving limited sample sizes. In this paper, we introduce an innovative framework named Patch Matching Metric-based Semantic Interaction Meta-Learning (PatSiML), designed to overcome these challenges. To counteract supervision collapse, we have developed a patch matching metric strategy based on the Transformer architecture to transform input images into a set of distinct patch embeddings. This approach dynamically creates task-specific embeddings, facilitated by a graph convolutional network, to formulate precise matching metrics between the support classes and the query image patches. To enhance the integration of semantic knowledge, we have also integrated a label-assisted channel semantic interaction strategy. This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. Our empirical findings across four diverse datasets reveal that the PatSiML method achieves a classification accuracy improvement of 0.65% to 21.15% over existing methodologies, underscoring its robustness and efficacy.
Design and Evaluation of a Novel Venturi-Based Spirometer for Home Respiratory Monitoring
Authors: Ferreira Nunes Mariana , Plácido da Silva Hugo , Raposo Liliana , Rodrigues Fátima
Published: August 2024

The high cost and limited availability of home spirometers pose a significant barrier to effective respiratory disease management and monitoring. To address this challenge, this paper introduces a novel Venturi-based spirometer designed for home use, leveraging the Bernoulli principle. The device features a 3D-printed Venturi tube that narrows to create a pressure differential, which is measured by a differential pressure sensor and converted into airflow rate. The airflow is then integrated over time to calculate parameters such as the Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV1). The system also includes a bacterial filter for hygienic use and a circuit board for data acquisition and streaming. Evaluation with eight healthy individuals demonstrated excellent test-retest reliability, with intraclass correlation coefficients (ICCs) of 0.955 for FVC and 0.853 for FEV1. Furthermore, when compared to standard Pulmonary Function Test (PFT) equipment, the spirometer exhibited strong correlation, with Pearson correlation coefficients of 0.992 for FVC and 0.968 for FEV1, and high reliability, with ICCs of 0.987 for FVC and 0.907 for FEV1. These findings suggest that the Venturi-based spirometer could significantly enhance access to spirometry at home. However, further large-scale validation and reliability studies are necessary to confirm its efficacy and reliability for widespread use.
A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion
Authors: do Nascimento Vinicius D. , Alves Tiago A. O. , de Farias Claudio M. , Dutra Diego Leonel Cadette
Published: August 2024

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts’ tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.
A Fast and Accurate Mapping Method for an OPGW Tower Based on Hybrid Distributed Optical Fiber Sensing
Authors: Yao Yuanyuan , Wang Ruofan , Ding Hao , Tong Shuai , Han Yucheng , Zhao Shisong , Zou Ningmu , Xiong Fei , Zhang Yixin
Published: August 2024

The combination of the dark fiber in existing Optical Fiber Composite Overhead Ground Wire (OPGW) with Distributed Optical Fiber Sensing (DOFS) technology can be used to enable online monitoring and provide early warnings of anomalies in high-voltage transmission lines. Accurate mapping of the optical cable length to the geographic coordinates of actual towers is a key factor in achieving this goal. This paper discusses the principle of using a DOFS system for transmission line tower positioning and presents four available positioning features. To overcome the limitations of single physical parameter positioning, this paper presents a self-developed hybrid DOFS that simultaneously captures Rayleigh backscattering and Brillouin scattering signals. Several physical parameters, including temperature, strain, and vibration, are acquired synchronously. Through hybrid multi-parameter analysis, the rapid and accurate positioning of OPGW line towers is achieved. Experimental results have shown that the proposed method, based on the hybrid DOFS system, can locate up to 82 towers, while the traditional method could only identify 12. The hybrid system was able to complete 80% of the tension towers in 40 h. This paper presents a novel multi-parameter localization method that has the potential to significantly improve the efficiency and reliability of grid operation and maintenance.
Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
Authors: Sengodan Boopathi Chettiagounder , Stanislaus Prince Mary , Arumugam Sivakumar Sabapathy , Sah Dipak Kumar , Dhabliya Dharmesh , Chenniappan Poongodi , Hezekiah James Deva Koresh , Maheswar Rajagopal
Published: August 2024

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.
A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles
Authors: Du Jiale , Liu Zunyi , Dong Wenyuan , Zhang Weifeng , Miao Zhonghua
Published: August 2024

Surface electromyography (sEMG) offers a novel method in human–machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.
Research on Person Re-Identification through Local and Global Attention Mechanisms and Combination Poolings
Authors: Zhou Jieqian , Zhao Shuai , Li Shengjie , Cheng Bo , Chen Junliang
Published: August 2024

This research proposes constructing a network used for person re-identification called MGNACP (Multiple Granularity Network with Attention Mechanisms and Combination Poolings). Based on the MGN (Multiple Granularity Network) that combines global and local features and the characteristics of the MGN branch, the MGNA (Multiple Granularity Network with Attentions) is designed by adding a channel attention mechanism to each global and local branch of the MGN. The MGNA, with attention mechanisms, learns the most identifiable information about global and local features to improve the person re-identification accuracy. Based on the constructed MGNA, a single pooling used in each branch is replaced by combination pooling to form MGNACP. The combination pooling parameters are the proportions of max pooling and average pooling in combination pooling. Through experiments, suitable combination pooling parameters are found, the advantages of max pooling and average pooling are preserved and enhanced, and the disadvantages of both types of pooling are overcome, so that poolings can achieve optimal results in MGNACP and improve the person re-identification accuracy. In experiments on the Market-1501 dataset, MGNACP achieved competitive experimental results; the values of mAP and top-1 are 88.82% and 95.46%. The experimental results demonstrate that MGNACP is a competitive person re-identification network, and that the attention mechanisms and combination poolings can significantly improve the person re-identification accuracy.