bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
Authors: Yang Lei , Cao Jianzhong , Bian He , Qu Rui , Guo Huinan , Ning Hailong
Published: September 2024

Remote-sensing image dehazing (RSID) is crucial for applications such as military surveillance and disaster assessment. However, current methods often rely on complex network architectures, compromising computational efficiency and scalability. Furthermore, the scarcity of annotated remote-sensing-dehazing datasets hinders model development. To address these issues, a Dual-View Knowledge Transfer (DVKT) framework is proposed to generate a lightweight and efficient student network by distilling knowledge from a pre-trained teacher network on natural image dehazing datasets. The DVKT framework includes two novel knowledge-transfer modules: Intra-layer Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT) modules. Specifically, the Intra-KT module is designed to correct the learning bias of the student network by distilling and transferring knowledge from a well-trained teacher network. The Inter-KT module is devised to distill and transfer knowledge about cross-layer correlations. This enables the student network to learn hierarchical and cross-layer dehazing knowledge from the teacher network, thereby extracting compact and effective features. Evaluation results on benchmark datasets demonstrate that the proposed DVKT framework achieves superior performance for RSID. In particular, the distilled model achieves a significant speedup with less than 6% of the parameters and computational cost of the original model, while maintaining a state-of-the-art dehazing performance.
Geochemical Characteristics and Depositional Environment from the Permian Qipan Formation Hydrocarbon Source Rocks in the Piedmont of Southwestern Tarim Basin
Authors: Wu Qiong , Zhou Guoxiao , Yin Jie , Ye Lin , Wang Zhenqi
Published: September 2024

The Permian Qipan Formation (P1-2q) is the aim horizon for hydrocarbon source rocks in the piedmont area of southwestern Tarim Basin. In the present study, the depositional environment and geochemical characteristics of muddy hydrocarbon source rocks of P1-2q were systematically evaluated using total organic carbon (TOC), Rock-Eval pyrolysis, vitrinite reflectance (Ro), reflected light microscopy, main and trace element, and biomarker parameters of 167 outcrop samples and 176 core samples. The TOC of P1-2q is primarily concentrated within the range of 0.36% to 2.77%, with an average of 1.58%. This suggests that the overall evaluation of the hydrocarbon source rock is fair to good. The source rocks of P1-2q predominately contain Type III and Type II2 kerogen. The overall Tmax values of P1-2q hydrocarbon source rocks are notably elevated, with the majority exceeding 490 °C or falling between 455 °C and 490 °C. The Ro value is between 0.90% and 2.00%, indicating that the maturity has reached a high, over-mature evolutionary stage. The trace element and biomarker parameters indicate that hydrocarbon source rocks of P1-2q are predominantly slightly oxygen-rich, with a minor anoxic component. The asymmetric ‘V’ arrangement of the C27-C29 regular steranes indicates that the hydrocarbon parent material is predominantly derived from algae or aquatic organisms, with varying degrees of mixing with organic matter of terrestrial origin. The study of source rock geochemistry of the P1-2q makes the exploration target of the southwest depression of the Tarim Basin more accurate in the complex tectonic geological environment.
Silicon Surface Nanostructuration with Symmetric Cathode Configurations for Photonic Devices
Authors: Ramadan Rehab , Elshorbagy Mahmoud Hamdy , Martín-Palma Raúl J.
Published: September 2024

The physical properties of porous silicon (PSi) can be adjusted to provide a better performance in optoelectronic devices. A controlled method commonly used to fabricate PSi is the anodization process, which employs platinum as a conventional cathode. Herein, we investigate the effect of replacing the Pt cathode with symmetric heavily doped silicon on the resulting surface structure on silicon substrates. The symmetric configuration is established when both anode and cathode are from the same material. Three different samples were anodized using both configurations and under different fabrication conditions. The results demonstrate the possibility to produce porous silicon structure using the heavily doped Si as alternative to the expensive Pt counter electrode. Furthermore the modified configuration offers the possibility of manufacturing large areas of nanostructured PSi without limitation of the counter electrode area and the applied current density. The formed porous structures using Si cathode have better uniformity, larger pore size, and lower number of interlinked and shallow holes than traditional methods. The porous structures fabricated with this configuration show broadband reduction in spectral reflectivity and changes in the schottky diode dark characteristics when compared with PSi fabricated with Pt conventional electrode.
Haematological and Biochemical Alterations in Pekin Ducks Affected by Short Beak and Dwarfism Syndrome: An Analytical Study
Authors: Szczepankiewicz Barbara , Popiel Jarosław , Graczyk Stanisław , Ciaputa Rafał , Bobrek Kamila , Gaweł Andrzej
Published: September 2024

Short beak and dwarfism syndrome (SBDS), characterised by growth retardation and short beak, is a contagious disease of ducks, caused by goose parvovirus (GPV). This study aimed to compare morphology and biochemistry data obtained from 4-week-old Pekin ducks naturally infected with parvovirus causing SBDS in healthy Pekin ducks of the same age. Materials and Methods: Forty Pekin ducks (twenty infected GPV and twenty clinically healthy controls) were examined. Measurement of the beak and metatarsus and histopathological examination were conducted, and blood morphological and biochemical analyses were performed for each individual. Results: Statistically significant increases in the SBDS group were observed in white blood cells (WBCs), alkaline phosphatase (ALP), and albumin levels, while decreases were noted in non-organic phosphorus, potassium, and amylase levels. ALP in the control group was 465.70 ± 161.49, while in the SBDS group it was 353.68 ± 79.97 (p ˂ 0.006). 1,2-o-dilauryl-rac-glycero-3-glutaric acid-(6′-methylresorufin) ester (DGGR) lipase marker offered a refined gauge for pancreatic function, with established reference values for the healthy control group set at 14.95 ± 4.27 U/L. Conclusions: This study sheds light on the unique impact of GPV on the skeletal system of Pekin ducks, revealing significant insights into the mechanisms of SBDS without osteitis. Additionally, this work offers groundbreaking insights into the morphological and biochemical alterations in the blood during SBDS, establishing normative haematological and biochemical indices for Pekin ducks. It also introduces the DGGR lipase marker as a refined marker for pancreatic function for the healthy control group set at 14.95 ± 4.27 U/L. It highlights the role of ALP in ensuring proper bone growth and the need for ongoing research on its activity in the context of viral infections.
Optimizing HAWK Signature Scheme Performance on ARMv8
Authors: Eum Siwoo , Lee Minwoo , Seo Hwajeong
Published: September 2024

This study proposes an optimized implementation of the HAWK Signature algorithm, one of the candidates in the first evaluation round for additional digital signature schemes in the NIST Post-Quantum Cryptography competition. The core motivation of this research is to improve the performance of HAWK algorithm. By conducting profiling analysis to identify, we identified the most resource-intensive functions. And then we optimized the functions. The optimization techniques through profiling analysis are not limited to HAWK but can be applied to other algorithms as well. Additionally, the study demonstrates how efficient optimization can be achieved using fewer instructions by leveraging lesser-known ARMv8 instructions. By targeting the functions with the highest overhead and utilizing fewer instructions, a performance improvement of approximately 2.5% for Hawk512 and 4% for Hawk1024 was achieved, respectively. These results confirm that combining profiling analysis with efficient instruction usage can lead to significant performance improvements.
Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
Authors: Assi Mariam , Favre Julien , Brykala Marcin , Tancret Franck , Fraczkiewicz Anna
Published: September 2024

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required.
Language Model-Based Text Augmentation System for Cerebrovascular Disease Related Medical Report
Authors: Kim Yu-Hyeon , Kim Chulho , Kim Yu-Seop
Published: September 2024

Texts in medical fields containing sensitive information pose challenges for AI research usability. However, there is increasing interest in generating synthetic text to make medical text data bigger for text-based medical AI research. Therefore, this paper suggests a text augmentation system for cerebrovascular diseases, using a synthetic text generation model based on DistilGPT2 and a classification model based on BioBERT. The synthetic text generation model generates synthetic text using randomly extracted reports (5000, 10,000, 15,000, and 20,000) from 73,671 reports. The classification model is fine-tuned with the entire report to annotate synthetic text and build a new dataset. Subsequently, we fine-tuned a classification model by incrementally increasing the amount of augmented data added to each original dataset. Experimental results show that fine-tuning by adding augmented data improves model performance by up to 20%. Furthermore, we found that generating a large amount of synthetic text is not necessarily required to achieve better performance, and the appropriate amount of data augmentation depends on the size of the original data. Therefore, our proposed method reduces the time and resources needed for dataset construction, automating the annotation task and generating meaningful synthetic text for medical AI research.
Seismic Retrofit Case Study of Shear-Critical RC Moment Frame T-Beams Strengthened with Full-Wrap FRP Anchored Strips in a High-Rise Building in Los Angeles
Authors: Anacleto-Lupianez Susana , Herrera Luis , Arnold Scott F. , Chai Winston , Erickson Todd , Lemnitzer Anne
Published: September 2024

This paper discusses the iteration of a seismic retrofit solution for shear-deficient end regions of 19 reinforced concrete (RC) moment-resisting frame (MRF) T-beams located in a 12-story RC MRF building in downtown Los Angeles, California. Local strengthening with externally bonded (EB) fiber-reinforced polymer (FRP) fabric was chosen as the preferred retrofit strategy due to its cost-effectiveness and proven performance. The FRP-shear-strengthening scheme for the deficient end-hinging regions of the MRF beams was designed and evaluated through large-scale cyclic testing of three replica specimens. The specimens were constructed at 4/5 scale and cantilever T-beam configurations with lengths of 3.40 m or 3.17 m. The cross-sectional geometry was 0.98 × 0.61 m with a top slab of 1.59 m in width and 0.12 m in thickness. Applied to these specimens were three different retrofit configurations, tested sequentially, namely: (a) unanchored continuous U-wrap; (b) anchored continuous U-wrap with conventional FRP-embedded anchors at the ends; and (c) fully closed external FRP hoops made of discrete FRP U-wrap strips and FRP through-anchors that penetrate the top slab and connect both ends of the FRP strips, combined with intermediate crack-control joints. The strengthening concept with FRP hoops precluded the premature debonding and anchor pullout issues of the two more conventional retrofit solutions and, despite a more challenging and labor-intensive installation, was selected for the in-situ implementation. The proposed hooplike EB-FRP shear-strengthening scheme enabled the deficient MRF beams to overcome a 30% shear overstress at the end-yielding region and to develop high-end rotations (e.g., 0.034 rad [3.4% drift] at peak and 0.038 rad [3.8% drift]) at strength loss for a beam that, otherwise, would have prematurely failed in shear. These values are about 30% larger than the ASCE 41 prescriptive value for the Life Safety (LS) performance objective. Energy dissipation achieved with the fully closed scheme was 108% higher than that of the unanchored FRP U-wrap and 45% higher than that of the FRP U-wrap with traditional embedded anchors. The intermediate saw-cut grooves successfully attracted crack formation between the strips and away from the FRP reinforcement, which contributed to not having any discernable debonding of the strips up to 3% drift. This paper presents the experimental evaluation of the three large-scale laboratory specimens that were used as the design basis for the final retrofit solution.
Phytoplankton as CO2 Sinks: Redirecting the Carbon Cycle
Authors: Zafrilla Basilio , Matarredona Laura , Bonete María-José , Zafrilla Guillermo , Esclapez Julia
Published: September 2024

Since the Industrial Revolution, nearly 700 Gt of carbon (GtC) have been emitted into the atmosphere as CO2 derived from human activities, of which 292 GtC remain uncontrolled. By the end of this century, the atmospheric CO2 concentration is predicted to surpass 700 ppm. The effects of this sudden carbon release on the worldwide biogeochemical cycles and balances are not yet fully understood, but global warming and climate change are undeniable, with this gas playing a starring role. Governmental policies and international agreements on emission reduction are not producing results quickly enough, and the deadline to act is running out. Biological CO2 capture is a fast-acting carbon cycle component capable of sequestering over 115 GtC annually through photosynthesis. This study analyses a hypothetical scenario in which this biological CO2 capture is artificially enhanced through the large-scale cultivation of phytoplankton in partially natural photobioreactors (PBRs). To develop this approach, the current figures of the carbon cycle have been updated, and the key aspects of phytoplankton cultivation technology have been analysed. Our results show that a global increase of 6.5% in biological capture, along with the subsequent stabilization of the produced biomass, could counteract the current CO2 emission rate and maintain atmospheric levels of this gas at their current levels. Based on a review of the available literature, an average production rate of 17 g/m2·day has been proposed for phytoplankton cultivation in horizontal PBRs. Using this value as a key reference, it is estimated that implementing a large-scale production system would require approximately 2.1 × 106 km2 of the Earth’s surface. From this, a production system model is proposed, and the key technological and political challenges associated with establishing these extensive cultivation areas are discussed.
Steel Columns under Compression with Different Sizes of Square Hollow Cross-Sections, Lengths, and End Constraints
Authors: Fonseca Elza M. M.
Published: September 2024

This work presents several results of the stability in steel columns subject to pure compression. A square hollow cross-section with different sizes was considered. This study presents all the analytical equations that need to be used to verify the stability of each column with different lengths and boundary conditions. A finite element program was also chosen to achieve the most critical loads (Euler and buckling resistance loads) in the calculation for each element under study, using linear and nonlinear geometric and material modeling. Steel material was used for the columns, where damage due to plasticity was included, through plastic behavior with isotropic hardening. Comparing the results allows us to conclude that the use of the finite element method is an alternative methodology to be used in other types or configurations of columns, where parameterized tests can be easily implemented and to contribute to the development of a wide-ranging database. The finite element method led to an accurate solution when compared with the analytical results with a maximum deviation of 14.7%. By increasing the column length and reducing the cross-section size, the design buckling resistance of the studied columns also decreases. These studies demonstrate that the length and size of the column cross-section can meaningfully increase the structural behavior of the columns.
Aluminum Phosphate Nanoplates Synthesized via Green Method Using Cork Oak Somatic Embryo-Derived Phytates
Authors: Pintos Beatriz , Gomez-Garay Arancha
Published: September 2024

This study presents a novel green synthesis method for aluminum phosphate (AlPO4) nanoplates using extracts from Quercus suber somatic embryos. Traditionally, metallic nanoparticles are synthesized using harmful chemical reagents and solvents, but our approach leverages bioactive compounds, such as phytic acid (IP6) from plant extracts, offering a more sustainable alternative. We observed a high phosphate content of 23.6 μMol PO43−/mg dry weight (DW) in the extracts, which is significantly higher than in other Quercus species. Characterization through UV–VIS spectroscopy and XRD confirmed the formation of complex crystal nanoplates, where anisotropic aluminum phosphate with a triclinic crystal structure coexists with monoclinic structures, both forms of aluminum phosphate tridymite. Electron microscopy (TEM and SEM) revealed the hierarchical organization of these nanoplates, distinguishing them from the more commonly observed spherical nanoparticles. Fourier-transform infrared (FTIR) spectroscopy further validated the incorporation of phytic acid into the nanoplate structure. This innovative method not only advances sustainable nanomaterial synthesis but also demonstrates the potential for unique optical and structural properties in aluminum phosphate nanoplates, paving the way for future applications in specialized fields.
Application of Ultrasound Homogenization in Milk Ice Cream Mixes
Authors: Kot Anna , Kamińska-Dwórznicka Anna
Published: September 2024

This study investigated the influence of ultrasound homogenization on the physical properties of milk ice cream mixes. A frequency of 20 kHz and an exposure time of 5 min was applied during the ultrasound homogenization to conduct experiments. Stability, particle size, rheological, and microscopic analyses were performed. Moreover, chosen stabilizers were used such as iota carrageenan or its hydrolyzates in combination with locust bean gum and xanthan gum. All parameters were checked before and after maturation at 4 °C/24 h. Based on the obtained results, it was noticed that the ultrasound homogenization contributed to a lower TSI value, which means that there is better stability during the maturation of milk ice cream mixes. In all of the mentioned samples, the TSI value was around 2 or less. Another pivotal finding connected with the particle sizes showed that simultaneously after and before maturation, the values of median D50 were lower in the samples after the mechanical homogenization than after the ultrasound. The rheological properties showed that all of the samples had pseudoplastic non-Newton behavior on the grounds that the value of the n index was lower than 1. Additionally, the consistency values in samples after the ultrasound treatment were lower than in samples after the mechanical homogenization and did not exceed 0.0018 × 10−3·Pasn after 24 h of maturation.
Characteristics of Energy Evolution and Failure Mechanisms in Sandstone Subject to Triaxial Cyclic Loading and Unloading Conditions
Authors: Zhang Jinrui , Luo Yi , Gong Hangli , Zhang Xianqi , Zhao Shankun
Published: September 2024

This study investigates the energy dynamics of sandstone subjected to failure in conditions typical of deep underground construction. Research was conducted using both standard triaxial compression and cyclic loading–unloading techniques at six distinct confining pressures, with the objective of elucidating the deformation and failure processes of rock materials. The tests demonstrated that, regardless of the stress path, sandstone primarily fails through shear under different confining pressures, which also reduces the formation of secondary cracks. The energy transformation observed during cyclic loading and unloading processes exhibits a distinctive peak-like distribution, marked by an inflection point that indicates changes in energy distribution. In the initial stages of the loading cycle, the energy profile of the rock increases, characterized by a condition in which the energy stored elastically exceeds the energy dissipated. Nevertheless, subsequent to reaching peak stress, there is a rapid transmutation of elastic strain energy into other forms, culminating in a pronounced elevation in the ratio of dissipated energy, which ultimately achieves a state of equilibrium influenced by the confining pressures. The study introduces the energy consumption ratio (Ke) as a metric for assessing rock damage accumulation and stability, noting a critical pattern where Ke decreases and then spikes at the rock’s failure point, with K = 1 identified as the critical threshold for failure. This comprehensive analysis illuminates the intricate relationship between energy distribution patterns and the stability of rock structures, thereby enhancing our understanding of failure mechanisms from an energetic perspective.
Dual-Layer Reinforcement Learning for Quadruped Robot Locomotion and Speed Control in Complex Environments
Authors: Zhang Yilin , Zeng Jiayu , Sun Huimin , Sun Honglin , Hashimoto Kenji
Published: September 2024

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods.
Color Brightness Recognition of Extremely Severe Amblyopia Children in an Indoor Environment
Authors: Li Yuhang , Zhu Xiaodong , Gu Yan
Published: September 2024

This study aims to investigate how indoor lighting (natural and artificial) and distances (3 m and 5 m) affect color recognition in visually impaired children. Ten participants from a special education school were selected to identify the brightness of five colors at varying lighting and distance circumstances. Each color was presented at six different brightness levels, classified into the low-brightness, the standard-color, and the high-brightness groups. Participants were directed to assess the top three brightness levels they considered most attractive, and each rating was assigned a weighted score. The findings revealed that: (1) Visually impaired children can recognize color brightness in both natural and artificial lighting situations. In indoor conditions, the low-brightness group exhibited greater recognition ability compared to the high-brightness group. Purple did not exhibit a clear pattern, as colors from the high-brightness, the low-brightness, and the standard-color groups were all preferred. (2) Significant differences were observed in the brightness recognition among visually impaired children at distances of 3 m and 5 m. Recognition for low-brightness colors improved with distance, contrasting high-brightness scores that declined. However, there was no significant variation in the perception of green with distance changes.
Potential Role of Tarantula Venom Peptides in Targeting Human Death Receptors: A Computational Study
Authors: Quiambao Janus Isaiah R. , Fowler Peter Matthew Paul T. , Tayo Lemmuel L.
Published: September 2024

Animal venom has been gaining traction as a potential source of therapeutics for various diseases. Spiders encompass a wide variety of venom-producing species, of which tarantulas of the family Theraphosidae are widely known across the globe. Research towards tarantula venom therapeutics has led to its potential application as antinociceptives. Death receptors are cellular receptors that induce apoptosis—the body’s natural suicide mechanism—to destroy malfunctioning cells. These are particularly of interest in cancer research, as this mechanism is tampered with, resulting in cancer cell proliferation. In this study, the viability of venom toxins from the Theraphosidae family of spiders to induce apoptosis by binding to human death receptors is investigated by carrying out anti-cancer screening, molecular docking, ADMET evaluation, then molecular dynamics and thermodynamic analysis twice, first to ascertain the best receptor–peptide systems per receptor, and secondly to more comprehensively describe binding stability and thermodynamics. Results point to favorable receptor–peptide interactions due to similarities in equilibrium behavior with the death ligand–death receptor systems, along with favorable end-state binding energies and ADMET analysis results. Further inquiry is recommended to assess the real-life efficacy and viability of theraphotoxins as apoptosis therapeutics and further improve on their ability to induce apoptosis.
Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems
Authors: Böhnisch Nils , Braun Carsten , Marzocca Pier , Muscarello Vincenzo
Published: September 2024

This paper presents initial findings from aeroelastic studies conducted on a wing-propeller model, aimed at evaluating the impact of aerodynamic interactions on wing flutter mechanisms and overall aeroelastic performance. The flutter onset is assessed using a frequency-domain method. Mid-fidelity tools based on the time-domain approach are then exploited to account for the complex aerodynamic interaction between the propeller and the wing. Specifically, the open-source software DUST and MBDyn are leveraged for this purpose. The investigation covers both windmilling and thrusting conditions. During the trim process, adjustments to the collective pitch of the blades are made to ensure consistency across operational points. Time histories are then analyzed to pinpoint flutter onset, and corresponding frequencies and damping ratios are identified. The results reveal a marginal destabilizing effect of aerodynamic interaction on flutter speed, approximately 5%. Notably, the thrusting condition demonstrates a greater destabilizing influence compared to the windmilling case. These comprehensive findings enhance the understanding of the aerodynamic behavior of such systems and offer valuable insights for early design predictions and the development of streamlined models for future endeavors.
The Impact of Prompting Techniques on the Security of the LLMs and the Systems to Which They Belong
Authors: Ivănușcă Teodor , Irimia Cosmin-Iulian
Published: September 2024

Large language models have demonstrated impressive capabilities. The recent research conducted in the field of prompt engineering showed that their base performance is just a glimpse of their full abilities. Enhanced with auxiliary tools and provided with examples of how to solve the tasks, their adoption into our applications seems trivial. In this context, we ask an uncomfortable question. Are the models secure enough to be adopted in our systems, or do they represent Trojan horses? The idea of prompt injection and jailbreak attacks does not seem to bother the adopters too much. Even though there are a lot of studies that look into the benefits of the prompting techniques, none address their possible downside in regard to the security. We want take a step further and investigate the impact of the most popular prompting techniques on this aspect of large language models and implicitly the systems to which they belong. Using three of the most deployed GPT models to date, we conducted a few of the most popular attacks in different setup scenarios and demonstrate that prompting techniques can have a negative impact on the security of the LLMs. More than that, they also expose other system components that otherwise would have been less exposed. In the end, we try to come up with possible solutions and present future research perspectives.
Multi-Objective Optimization of Energy-Efficient Multi-Stage, Multi-Level Assembly Job Shop Scheduling
Authors: Dong Yingqian , Liao Weizhi , Xu Guodong
Published: September 2024

The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters; thus, the convergence and diversity of the algorithmic search are enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving the EEMsMlAJSP is verified by comparing it with other algorithms.
Innovating Patent Retrieval: A Comprehensive Review of Techniques, Trends, and Challenges in Prior Art Searches
Authors: Ali Amna , Tufail Ali , De Silva Liyanage Chandratilak , Abas Pg Emeroylariffion
Published: September 2024

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval.
The E(G)TL Model: A Novel Approach for Efficient Data Handling and Extraction in Multivariate Systems
Authors: Vesjolijs Aleksejs
Published: September 2024

This paper introduces the EGTL (extract, generate, transfer, load) model, a theoretical framework designed to enhance the traditional ETL processes by integrating a novel ‘generate’ step utilizing generative artificial intelligence (GenAI). This enhancement optimizes data extraction and processing, presenting a high-level solution architecture that includes innovative data storage concepts: the Fusion and Alliance stores. The Fusion store acts as a virtual space for immediate data cleaning and profiling post-extraction, facilitated by GenAI, while the Alliance store serves as a collaborative data warehouse for both business users and AI processes. EGTL was developed to facilitate advanced data handling and integration within digital ecosystems. This study defines the EGTL solution design, setting the groundwork for future practical implementations and exploring the integration of best practices from data engineering, including DataOps principles and data mesh architecture. This research underscores how EGTL can improve the data engineering pipeline, illustrating the interactions between its components. The EGTL model was tested in the prototype web-based Hyperloop Decision-Making Ecosystem with tasks ranging from data extraction to code generation. Experiments demonstrated an overall success rate of 93% across five difficulty levels. Additionally, the study highlights key risks associated with EGTL implementation and offers comprehensive mitigation strategies.
High-Resolution Daily PM2.5 Exposure Concentrations in South Korea Using CMAQ Data Assimilation with Surface Measurements and MAIAC AOD (2015–2021)
Authors: Kang Jin-Goo , Lee Ju-Yong , Lee Jeong-Beom , Lim Jun-Hyun , Yun Hui-Young , Choi Dae-Ryun
Published: September 2024

Particulate matter (PM) in the atmosphere poses significant risks to both human health and the environment. Specifically, PM2.5, particulate matter with a diameter less than 2.5 micrometers, has been linked to increased rates of cardiovascular and respiratory diseases. In South Korea, concerns about PM2.5 exposure have grown due to its potential for causing premature death. This study aims to estimate high-resolution exposure concentrations of PM2.5 across South Korea from 2015 to 2021. We integrated data from the Community Multiscale Air Quality (CMAQ) model with surface air quality measurements, the Weather Research Forecast (WRF) model, the Normalized Difference Vegetation Index (NDVI), and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) satellite data. These data, combined with multiple regression analyses, allowed for the correction of PM2.5 estimates, particularly in suburban areas where ground measurements are sparse. The simulated PM2.5 concentration showed strong correlations with observed values R (ranging from 0.88 to 0.94). Spatial distributions of annual PM2.5 showed a significant decrease in PM2.5 concentrations from 2015 to 2021, with some fluctuation due to the COVID-19 pandemic, such as in 2020. The study produced highly accurate daily average high-resolution PM2.5 exposure concentrations.
Exact Solutions to Fractional Schrödinger–Hirota Equation Using Auxiliary Equation Method
Authors: Tian Guangyuan , Meng Xianji
Published: September 2024

In this paper, we consider the fractional Schrödinger–Hirota (FSH) equation in the sense of a conformable fractional derivative. Through a traveling wave transformation, we change the FSH equation to an ordinary differential equation. We obtain several exact solutions through the auxiliary equation method, including soliton, exponential and periodic solutions, which are useful to analyze the behaviors of the FSH equation. We show that the auxiliary equation method improves the speed of the discovery of exact solutions.
Bivariate Length-Biased Exponential Distribution under Progressive Type-II Censoring: Incorporating Random Removal and Applications to Industrial and Computer Science Data
Authors: Fayomi Aisha , Almetwally Ehab M. , Qura Maha E.
Published: September 2024

In this paper, we address the analysis of bivariate lifetime data from a length-biased exponential distribution observed under Type II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. We derive the likelihood function for the progressive Type II censoring scheme with random removals and apply it to the bivariate length-biased exponential distribution. The parameters of the proposed model are estimated using both likelihood and Bayesian methods for point and interval estimators, including asymptotic confidence intervals and bootstrap confidence intervals. We also employ different loss functions to construct Bayesian estimators. Additionally, a simulation study is conducted to compare the performance of censoring schemes. The effectiveness of the proposed methodology is demonstrated through the analysis of two real datasets from the industrial and computer science domains, providing valuable insights for illustrative purposes.
Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator
Authors: Son Yuseong , Jeong Sihyeon , Hong Youngtaek , Lee Jina , Jeon Byunghwan , Choi Hyunji , Kim Jaehwan , Shim Hackjoon
Published: September 2024

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework’s superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach’s efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method’s critical role in optimizing performance, and robustness to input variations underscores its practical utility.
A 3D Epithelial–Mesenchymal Co-Culture Model of the Airway Wall Using Native Lung Extracellular Matrix
Authors: de Hilster Roderick H. J. , Reinders-Luinge Marjan A. , Schuil Annemarie , Borghuis Theo , Harmsen Martin C. , Burgess Janette K. , Hylkema Machteld N.
Published: September 2024

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease characterized by ongoing inflammation, impaired tissue repair, and aberrant interplay between airway epithelium and fibroblasts, resulting in an altered extracellular matrix (ECM) composition. The ECM is the three-dimensional (3D) scaffold that provides mechanical support and biochemical signals to cells, now recognized not only as a consequence but as a potential driver of disease progression. To elucidate how the ECM influences pathophysiological changes occurring in COPD, in vitro models are needed that incorporate the ECM. ECM hydrogels are a novel experimental tool for incorporating the ECM in experimental setups. We developed an airway wall model by combining lung-derived ECM hydrogels with a co-culture of primary human fibroblasts and epithelial cells at an air–liquid interface. Collagen IV and a mixture of collagen I, fibronectin, and bovine serum albumin were used as basement membrane-mimicking coatings. The model was initially assembled using porcine lung-derived ECM hydrogels and subsequently with COPD and non-COPD human lung-derived ECM hydrogels. The resulting 3D construct exhibited considerable contraction and supported co-culture, resulting in a differentiated epithelial layer. This multi-component 3D model allows the investigation of remodelling mechanisms, exploring ECM involvement in cellular crosstalk, and holds promise as a model for drug discovery studies exploring ECM involvement in cellular interactions.
FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts
Authors: Bai Shurui , Deng Zhuo , Yang Jingyan , Gong Zheng , Gao Weihao , Shao Lei , Li Fang , Wei Wenbin , Ma Lan
Published: September 2024

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.
Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features
Authors: Bian Congchao , Hu Can , Cao Ning
Published: September 2024

Magnetic resonance imaging (MRI) diagnosis, enhanced by deep learning methods, plays a crucial role in medical image processing, facilitating precise clinical diagnosis and optimal treatment planning. Current methodologies predominantly focus on feature extraction from the image domain, which often results in the loss of global features during down-sampling processes. However, the unique global representational capacity of MRI K-space is often overlooked. In this paper, we present a novel MRI K-space-based global feature extraction and dual-path attention fusion network. Our proposed method extracts global features from MRI K-space data and fuses them with local features from the image domain using a dual-path attention mechanism, thereby achieving accurate MRI segmentation for diagnosis. Specifically, our method consists of four main components: an image-domain feature extraction module, a K-space domain feature extraction module, a dual-path attention feature fusion module, and a decoder. We conducted ablation studies and comprehensive comparisons on the Brain Tumor Segmentation (BraTS) MRI dataset to validate the effectiveness of each module. The results demonstrate that our method exhibits superior performance in segmentation diagnostics, outperforming state-of-the-art methods with improvements of up to 63.82% in the HD95 distance evaluation metric. Furthermore, we performed generalization testing and complexity analysis on the Automated Cardiac Diagnosis Challenge (ACDC) MRI cardiac segmentation dataset. The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. Collectively, these results suggest that our proposed method holds significant potential for practical clinical applications.