bluuebunny/crossref_metadata_2025 huggingface dataset:
ConLBS: An Attack Investigation Approach Using Contrastive Learning with Behavior Sequence
Authors: Li Jiawei , Zhang Ru , Liu Jianyi
Published: December 2023

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attacks and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify attack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and can achieve superior effectiveness compared to existing methods and models.
Digital Twin of a Gear Root Crack Prognosis
Authors: Matania Omri , Bechhoefer Eric , Bortman Jacob
Published: December 2023

Digital twins play a significant role in Industry 4.0, offering the potential to revolutionize machinery maintenance. In this paper, we introduce a new digital twin designed to address the open problem of predicting gear root crack propagation. This digital twin uses signal processing and model fitting to continuously monitor the condition of the root crack and successfully estimate the remaining time until immediate maintenance is required for the physical asset. The functionality of this new digital twin is demonstrated through the experimental data obtained from a planetary gear, where comparisons are made between the actual and estimated severity of the fault, as well as the remaining time until maintenance. It is shown that the digital twin addresses the open problem of predicting gear root crack propagation.
Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
Authors: Yang Xu , Fang Hai , Gao Yuan , Wang Xingjie , Wang Kan , Liu Zheng
Published: December 2023

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented “end-edge-cloud” multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.
Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition
Authors: Novak Rok , Robinson Johanna Amalia , Kanduč Tjaša , Sarigiannis Dimosthenis , Džeroski Sašo , Kocman David
Published: December 2023

Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML’s potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.
Guided Acoustic Waves in Polymer Rods with Varying Immersion Depth in Liquid
Authors: Lutter Klaus , Backer Alexander , Drese Klaus Stefan
Published: December 2023

Monitoring tanks and vessels play an important part in public infrastructure and several industrial processes. The goal of this work is to propose a new kind of guided acoustic wave sensor for measuring immersion depth. Common sensor types such as pressure sensors and airborne ultrasonic sensors are often limited to non-corrosive media, and can fail to distinguish between the media they reflect on or are submerged in. Motivated by this limitation, we developed a guided acoustic wave sensor made from polyethylene using piezoceramics. In contrast to existing sensors, low-frequency Hanning-windowed sine bursts were used to excite the L(0,1) mode within a solid polyethylene rod. The acoustic velocity within these rods changes with the immersion depth in the surrounding fluid. Thus, it is possible to detect changes in the surrounding media by measuring the time shifts of zero crossings through the rod after being reflected on the opposite end. The change in time of zero crossings is monotonically related to the immersion depth. This relative measurement method can be used in different kinds of liquids, including strong acids or bases.
CVII: Enhancing Interpretability in Intelligent Sensor Systems via Computer Vision Interpretability Index
Authors: Mohammadi Hossein , Thirunarayan Krishnaprasad , Chen Lingwei
Published: December 2023

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across domains. The CVII is rigorously evaluated using a range of computer vision models applied to the COCO (Common Objects in Context) dataset, a widely recognized benchmark in the field. The findings established a robust correlation between image interpretability, model selection, and CVII scores. This research makes a substantial contribution to enhancing interpretability for human comprehension, as well as within intelligent sensor applications. By promoting transparency and reliability in AI-driven decision-making, the CVII framework empowers its stakeholders to effectively harness the full potential of AI technologies.
Self-Attention Mechanism-Based Head Pose Estimation Network with Fusion of Point Cloud and Image Features
Authors: Chen Kui , Wu Zhaofu , Huang Jianwei , Su Yiming
Published: December 2023

Head pose estimation serves various applications, such as gaze estimation, fatigue-driven detection, and virtual reality. Nonetheless, achieving precise and efficient predictions remains challenging owing to the reliance on singular data sources. Therefore, this study introduces a technique involving multimodal feature fusion to elevate head pose estimation accuracy. The proposed method amalgamates data derived from diverse sources, including RGB and depth images, to construct a comprehensive three-dimensional representation of the head, commonly referred to as a point cloud. The noteworthy innovations of this method encompass a residual multilayer perceptron structure within PointNet, designed to tackle gradient-related challenges, along with spatial self-attention mechanisms aimed at noise reduction. The enhanced PointNet and ResNet networks are utilized to extract features from both point clouds and images. These extracted features undergo fusion. Furthermore, the incorporation of a scoring module strengthens robustness, particularly in scenarios involving facial occlusion. This is achieved by preserving features from the highest-scoring point cloud. Additionally, a prediction module is employed, combining classification and regression methodologies to accurately estimate head poses. The proposed method improves the accuracy and robustness of head pose estimation, especially in cases involving facial obstructions. These advancements are substantiated by experiments conducted using the BIWI dataset, demonstrating the superiority of this method over existing techniques.
Influence of Tools and Cutting Strategy on Milling Conditions and Quality of Horizontal Thin-Wall Structures of Titanium Alloy Ti6Al4V
Authors: Kurpiel Szymon , Cudok Bartosz , Zagórski Krzysztof , Cieślik Jacek , Skrzypkowski Krzysztof , Brostow Witold
Published: December 2023

Titanium and nickel alloys are used in the creation of components exposed to harsh and variable operating conditions. Such components include thin-walled structures with a variety of shapes created using milling. The driving factors behind the use of thin-walled components include the desire to reduce the weight of the structures and reduce the costs, which can sometimes be achieved by reducing the machining time. This situation necessitates, among other things, the use of new machining methods and/or better machining parameters. The available tools, geometrically designed for different strategies, allow working with similar and improved cutting parameters (increased cutting speeds or higher feed rates) without jeopardizing the necessary quality of finished products. This approach causes undesirable phenomena, such as the appearance of vibrations during machining, which adversely affect the surface quality including the surface roughness. A search is underway for cutting parameters that will minimize the vibration while meeting the quality requirements. Therefore, researching and evaluating the impact of cutting conditions are justified and common in scientific studies. In our work, we have focused on the quality characteristics of horizontal thin-walled structures from Ti6Al4V titanium alloys obtained in the milling process. Our experiments were conducted under controlled cutting conditions at a constant value of the material removal rate (2.03 cm3⁄min), while an increased value of the cut layer was used and tested for use in finishing machining. We used three different cutting tools, namely, one for general purpose machining, one for high-performance machining, and one for high-speed machining. Two strategies were adopted: adaptive face milling and adaptive cylindrical milling. The output quantities included the results of acceleration vibration amplitudes, and selected surface topography parameters of waviness (Wa and Wz) and roughness (Ra and Rz). The lowest values of the pertinent quantities were found for a sample machined with a high-performance tool using adaptive face milling. Surfaces typical of chatter vibrations were seen for all samples.
Providing Consumer Credit to Low-Income Populations in Brazil—The Case of Complexo da Penha
Authors: Santanna Danielle
Published: December 2023

The financial inclusion (FI) literature claims that expanding access of the poor to consumer credit in the formal financial system is an instrument to alleviate poverty. This view has been increasingly challenged by the financialization literature, which highlights unsustainable indebtedness of low-income borrowers following the introduction of FI policies. While a welcome contribution and antidote to mainstream assessments, much of the financialization literature on Brazil has, to date, focused on a macro-oriented analysis. As a major testing ground for FI, a better understanding of these dynamics in Latin America’s largest country deepens our understanding of the socio-economic consequences of inserting poor populations into the financial system. This paper contributes to the financialization literature by drawing on interviews conducted in a Rio de Janeiro slum to understand how debt dynamics operate in everyday life, along with its structural conditioners. It shows that FI policies provide limited consumption smoothing and instead increase the structural vulnerability of low-income households. Wage and welfare support along with greater oversight and control of financial products supplied to low-income borrowers are warranted.
Salt Stress Highlights the Relevance of Genotype × Genotype Interaction in the Nitrogen-Fixing Symbiosis between Sinorhizobium meliloti and Alfalfa
Authors: Bellabarba Agnese , Decorosi Francesca , Fagorzi Camilla , El Hadj Mimoune Amina , Buccioni Arianna , Santoni Margherita , Pacini Gaio Cesare , Bekki Abdelkader , Azim Khalid , Hafidi Majida , Mazzoncini Marco , Mengoni Alessio , Pini Francesco , Viti Carlo
Published: December 2023

Sustainable-forage production is globally increasing, especially in marginal areas where the edaphic conditions for plant growth are not optimal. Soil salinization influences the symbiotic interaction between alfalfa and rhizobia. The efficiency of different symbiotic pairs (Sinorhizobium meliloti—Medicago sativa) was evaluated in relation to NaCl application (100 mM) on two different alfalfa cultivars (Marina and Etrusca) and 21 S. meliloti strains isolated in Algeria. At 100 mM NaCl, it was observed that there was a higher variability of plant dry weight compared to the control. The strains able to improve plant growth at 100 mM NaCl were different and specific for each alfalfa cultivar, highlighting that (symbiont) G × (host) G interaction is magnified under stressed (saline) conditions (E). Three strains were then identified as candidate inoculants for M. sativa cv Marina and used for an in-field experiment with induced stress (no irrigation), together with S. meliloti GR4 (a highly competitive strain). In-field experiments, showed a high variability, and a significant difference of plant biomass was observed only for those inoculated with S. meliloti GR4. Obtained results suggest that multiple traits should be considered for inoculant-strain selection, and for an efficient translation from lab to field, it requires extensive comprehension of the mechanisms driving G × G × E interaction.
The Impact of the Digital Economy on Food System Resilience: Insights from a Study across 190 Chinese Towns
Authors: Wang Haifeng , Li Guangsi , Hu Yunzhi
Published: December 2023

This study explores the impact of the digital economy on the resilience of the food system, employing data from 190 towns in China and a difference-in-differences (DID) model. The results indicate that, between 2011 and 2020, the progress of the digital economy was instrumental in driving continuous improvements in the food system’s resilience in China. This conclusion stands firm after accounting for endogenous issues and conducting comprehensive robustness tests. According to a mechanism test, the digital economy can impact the resilience of the food system through three avenues: digital technology, digital finance, and human capital. Further research indicates that the influence of the digital economy on the resilience of the food system varies across regions and different food functional zones. These findings offer fresh insights and empirical evidence into the linkages between the digital economy and food system resilience. Such insights may bolster the food system’s resilience in developing countries and promote sustainable food development.
Full-Scale Design, Implementation and Testing of an Innovative Photovoltaic Cooling System (IPCoSy)
Authors: Bugeja Ryan , Mule’ Stagno Luciano , Niarchos Ioannis
Published: December 2023

The field efficiency of silicon-based solar cells is dependent on various factors including temperature. An increase in temperature results in a reduced efficiency of a magnitude dependent on the solar cell’s temperature coefficient. Furthermore, an increase in solar cell temperatures beyond levels specified by the manufacturer will result in a reduced lifetime and an increased probability of potential induced degradation and even failure. Researchers have created different cooling technologies to keep the solar cells’ operating temperatures to a minimum. However, no cooling technology in the literature is adequate for both land and offshore PV installations. A patented Innovative Photovoltaic Cooling System (IPCoSy) is presented in this paper. Previously published results have confirmed the cooling effect and feasibility using small-scale prototypes. This paper presents the design challenges and results of the full-scale implementation. The full-scale prototypes are the same size as commercially available photovoltaic modules, making them easier to integrate in the current market. Therefore, this research presents the results of testing full-scale prototypes while addressing challenges related to structural integrity and fluid dynamics. The findings of this research showed that the positive effects of this cooling technology range from more than a 9% increase in PV electrical energy yield, and thermal efficiencies of up to 56%. Finally, the outcome of this research will contribute towards the United Nations’ sustainable development goal of affordable and clean energy through direct operational efficiency improvements in PV systems, as well as the enhanced tapping of solar energy for renewable thermal energy production.
Long-Term Monitoring Campaign of LED Street Lighting Systems: Focus on Photometric Performances, Maintenance and Energy Savings
Authors: Valetti Lodovica , Piccablotto Gabriele , Taraglio Rossella , Pellegrino Anna
Published: December 2023

The renovation of public lighting installations by replacing the traditional systems with LED technologies and introducing smart lighting control systems is a policy widely adopted to contain energy consumption and expenditure. Additionally, the long-term monitoring of the depreciation of the new lighting systems is a crucial issue. The aim of this study is to report the results of in-field measurements of new LED lighting systems in the city of Turin (Italy). A method was defined to assess: (i) energy performance (through data from the remote-control system); (ii) photometric performance (through in-field measurement campaigns); and (iii) depreciation of the photometric performance over a period of approximately 5 years. Results demonstrated that the new LED systems allow us to achieve an average energy saving of 51% compared to the ex-ante condition, improving the photometric performances and compiling the standard requirements by lowering the over-illumination levels. Moreover, the measured depreciation of the LED systems over time was compared with the predicted depreciation, estimated based on the calculation method proposed in Standards BS 5489-1:2020 and ISO/CIE TS 22012:2019. The results obtained showed that the measured depreciation of the photometric performance was closer to the predicted depreciation trend according to BS 5489-1:2020 (variations between 0% and 4%), while greater variations (between 17% and 23%) emerged considering the ISO/CIE TS 22012:2019.
Influence of the Decision to Select Renovated Elements in a Heritage Building on Further Stages of the Aging Process of the Building—A Case Study
Authors: Kurowska Aleksandra , Nowogońska Beata
Published: December 2023

The successful use of buildings requires experience, interdisciplinary knowledge and skills. Wrong decisions made by building owners or managers to implement repair projects have a negative impact on the aging of buildings, which is closely related to sustainable development. Renovation projects in more degraded buildings must be carried out to a greater extent, which has a negative impact on the environment in the form of more building waste. This article presents the results of research on the impact of refurbishment decisions on the technical conditions of buildings. The development of an optimal building renovation strategy will result in a safe and comfortable building. A comparative analysis of the impact of the choice of repair building elements on further aging changes in the building was carried out. The reason for the subject is the frequent consideration by managers of the hierarchy of building elements that improve the technical condition of the building, the reason for which is the lack of financial resources to carry out overall renovation works. To date, two refurbishment strategies have been adopted, as well as assumptions about the building’s material and structural solutions and the building’s service life. Theoretical assumptions were applied to determine the repair needs of the Bialkow palace. The method shows the superiority of the impact of structural elements over non-structural elements in improving the technical condition of a building. Developing a renovation plan with the implementation of the presented strategy will significantly reduce the technical condition of the entire building with a moderate budget, limiting the occurrence of a building disaster and the serious carbon footprint associated with the demolition of the property.
How Does Green Finance Policy Affect the Capacity Utilization Rate of Polluting Enterprises?
Authors: Cai Xing , Chen Guoran , Wang Fei
Published: December 2023

Effectively addressing overcapacity is the main task of China’s deepening supply-side reform and represents an intrinsic requirement for achieving sustainable economic development. Green finance policy, as a kind of environmental regulation policy that influences the behavior of polluting enterprises, can not only effectively facilitate the green transformation of production methods but also have a significant effect on the capacity utilization rate of enterprises. We use the promulgation of the Guidance on Building a Green Financial System in 2016 as a quasinatural experiment and the differences-in-differences (DID) method to study the effect of green finance policies on the capacity utilization rates of polluting enterprises based on data from 2012 to 2020 on A-share listed companies on the Shanghai Stock Exchanges (SSE) and Shenzhen Stock Exchanges (SZSE). We obtained the following results: (1) The implementation of green finance policies markedly improved polluting enterprises’ capacity utilization rate, which was supported by a sequence of robustness tests; (2) The mechanism test revealed that green finance policies serve to rectify information asymmetry and constrain improper government interventions through credit resource allocation mechanisms, thereby inhibiting overinvestments in polluting enterprises and ultimately increasing the capacity utilization rate. Additionally, green finance policies can improve product quality and diversity by incentivizing polluting enterprises’ technological innovation, enabling products to better meet market demand, and ultimately improving the capacity utilization rate; (3) The results of the heterogeneity analysis indicate that, for state-owned and large-scale polluting enterprises, green finance policies play a stronger role in increasing the capacity utilization rate. We have enriched the research related to the policy effects of green finance and the impact of environmental regulation on the capacity utilization rate, thus providing a useful reference for China to utilize green finance policies to address overcapacity, promote the transition to a more environmentally sustainable economic society, and achieve sustainable development.
Synthesis and Evaluation of Geopolymer Mixtures Containing Chronologically Aged Basic Oxygen Furnace Slags
Authors: Tukaziban Aizhan , Shon Chang-Seon , Zhang Dichuan , Kim Jong Ryeol , Kim Ji-Hyun , Chung Chul-Woo
Published: December 2023

Applying industrial by-products as a substitution for conventional construction materials (natural resources) is a superior solution for the environment in terms of waste management and reduction in greenhouse emissions and for the construction industry in terms of cost and expenditure. Applying basic oxygen furnace slag (BOFS), one of the metallurgical industry by-products, as a construction material can be a high-potential and promising idea. However, the utilization of BOFS in construction applications is considerably limited because of its inherent characteristics leading to volumetric expansion behavior caused by the chemical reaction between free lime (f-CaO) and water. This study used geopolymer technology to stabilize the expansive behavior of chronologically aged BOFS aggregates. The compressive strength, expansion behavior, and drying shrinkage characteristics of a normal ordinary Portland cement (OPC) mixture and a geopolymer mixture containing siliceous river sand and chronologically aged BOFS aggregates were investigated. The test results showed that the compressive strength of geopolymer mixtures containing chronologically aged BOFS aggregate achieved 64.02 MPa, and the expansion behavior of geopolymer mixtures was improved compared with normal OPC mixtures containing the same BOFS aggregates, reaching 0.02% and 0.44%, respectively. However, due to the air-curing method, geopolymer mixtures had higher drying shrinkage values than normal OPC mixtures. Therefore, further studies should be conducted to investigate how to control the drying shrinkage of geopolymer mixtures containing chronologically aged BOFS aggregate.
Improvement of Printability Properties of High-Protein Food from Mealworm (Tenebrio molitor) Using Guar Gum for Sustainable Future Food Manufacturing
Authors: Chancharoen Wares , Kaewkumpha Yossaphol , Chansataporn Wanassanan , Ngamkajornwiwat Potiwat , Wannakee Jirapat
Published: December 2023

Increasing the availability of alternative protein from insects is important for solving food shortages. Not only are insects a rich source of protein, but using insect as ingredients could reduce food waste. Insects are thus a potentially valuable ingredient for food industries and even sustainable food. The three-dimensional production of food for future food has gained attention owing to its potential to reduce autonomous food production and produce sustainable food. This study investigated the printability and rheological properties of a high-protein food system derived from mealworms and guar gum used to improve printability. The stability and rheological properties were analyzed for various printing parameters. The results indicate that the yield stress of the mealworm paste dramatically increased (39 to 1096 Pa) with even a small guar gum concentration resulting in an increase (0 to 1.75%). Increasing the guar gum concentration thus resulted in a mealworm paste that had a more significant value of hardness and cohesiveness but reduced adhesiveness (p < 0.05). In conclusion, the addition of guar gum increased viscosity, and caused the paste to exhibit a shear thinning behavior and ability to support itself and was thus more stable. In summary, introducing guar gum resulted in a mealworm paste with rheological properties more suitable for printing in terms of printability and stability.
Lab Investigation Using a Box Model and Image Analysis of a Contaminant Back-Diffusion Process from Low-Permeability Layers
Authors: Viotti Paolo , Luciano Antonella , Mancini Giuseppe , Tatti Fabio
Published: December 2023

Contaminants stored in low-permeability soils can continue to threaten the adjacent groundwater system even after the aquifer is considered remediated. The redistribution of contaminants from low-to-high-permeability aquifer zones (Back-Diffusion) can generate a long-term plume tail, commonly considered one of the main obstacles to effective groundwater remediation. In this paper, a laboratory test was performed to reproduce the redistribution process from low-permeability silt lenses (k ≈ 1 × 10−7 m/s) to high-permeability sand aquifers (k ≈ 1 × 10−3 m/s). The target of the experimental and numerical approach was finalized to verify what influence the shape and position of the lenses could have, with respect to the bulk flow, on the time necessary to complete the depletion of the dissolved substances present in the lenses. For this purpose, an image analysis procedure was used to estimate the diffusive flux of contaminants released by these low-permeability zones in different boundary conditions. The results obtained in the laboratory test were used to calibrate a numerical model implemented to simulate the Back-Diffusion process. Once calibrated, the numerical model was used to simulate further scenarios to evaluate the influence of the location and shape of the low-permeability lenses on the time necessary to diminish its contaminant content when subjected to a steady-state flow. The numerical model was also used to investigate the effect of different groundwater velocities on the depletion time of the process. The results show that the shape and position of the lens have an important impact on the time necessary to empty the lens, and an increase in the velocity field in the bulk medium (flow rate rising from 1.6 l/h to 2.5 l/h) does not correspond to diminishing total depletion times, as the process is mainly governed by diffusive transport inside the lens. This appears to be significant when the remediation approach relies on pumping technology. Future research will verify the behavior of the released plume in a strongly heterogeneous porous medium.
Deformation Characteristics of Combined Heavy Metals-Contaminated Soil Treated with nZVI through the Modified Slurry Consolidation Method
Authors: Fan Chen , Chen Yongzhan , Dong Qinxi , Wei Jing , Zou Meng
Published: December 2023

Nanoscale zero-valent iron (nZVI) has been widely applied to remediate heavy metal-contaminated soils and water. Its in situ treatment of combined heavy metal contaminated soil, followed by backfilling or other sustainable reutilizations, attracted attention to the treated soil’s deformation characteristics. In this study, soil samples were prepared using the modified slurry consolidation method to simulate the natural settling of backfilled soil and optimize the reactivity between nZVI and contaminants in soil. The deformation characteristics of natural soil, contaminated soil, and soil treated with varying dosages of nZVI (0.2%, 0.5%, 1%, 2%, and 5%) were investigated. Moreover, the plasticity indexes and particle-size distribution of the samples were examined through Atterberg limits and laser-diffraction particle-size analysis. After a 4 d slurry consolidation process, a typical result indicated the immobilization efficiency of all three heavy metal ions achieved over 90% with 2% nZVI. The presence of three heavy metal ions decreased the Atterberg limits and increased the compression index, permeability, and consolidation coefficient of the soil. Conversely, the introduction of nZVI increased plasticity and resulted in higher permeability, stable secondary consolidation, and less swell. Microscopically, with an increase in the dosage of nZVI, the soil aggregates transformed from a weak chemical bond with insoluble precipitates/iron oxides to larger aggregates consisting of nZVI/-soil aggregates, thereby enhancing the soil skeleton. This study shows improved permeability and deformation characteristics in nZVI-treated combined heavy metal-contaminated soil, offering valuable insights for practical nanomaterials’ in-situ treatment in engineering applications.
Existence and Uniqueness of Solutions of Hammerstein-Type Functional Integral Equations
Authors: Tunç Cemil , Alshammari Fehaid Salem , Akyildiz Fahir Talay
Published: December 2023

The authors deal with nonlinear and general Hammerstein-type functional integral equations (HTFIEs). The first objective of this work is to apply and extend Burton’s method to general and nonlinear HTFIEs in a Banach space via the Chebyshev norm and complete metric. The second objective of the paper is to extend and improve some earlier results to nonlinear HTFIEs. The authors prove two new theorems with regard to the existence and uniqueness of solutions (EUSs) of HTFIEs via a technique called progressive contractions, which belongs to T. A. Burton, and the Chebyshev norm and complete metric.
Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer
Authors: Tian Fengqing , Zhang Donghua , Yuan Ying , Fu Guangchun , Li Xiaomin , Chen Guanghua
Published: December 2023

Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.
Investigation of Special Type-Π Smarandache Ruled Surfaces Due to Rotation Minimizing Darboux Frame in E3
Authors: Solouma Emad , Al-Dayel Ibrahim , Khan Meraj Ali , Abdelkawy Mohamed
Published: December 2023

This study begins with the construction of type-Π Smarandache ruled surfaces, whose base curves are Smarandache curves derived by rotation-minimizing Darboux frame vectors of the curve in E3. The direction vectors of these surfaces are unit vectors that convert Smarandache curves. The Gaussian and mean curvatures of the generated ruled surfaces are then separately calculated, and the surfaces are required to be minimal or developable. We report our main conclusions in terms of the angle between normal vectors and the relationship between normal curvature and geodesic curvature. For every surface, examples are provided, and the graphs of these surfaces are produced.
Certain Results on Subclasses of Analytic and Bi-Univalent Functions Associated with Coefficient Estimates and Quasi-Subordination
Authors: Badiwi Elaf Ibrahim , Atshan Waggas Galib , Alkiffai Ameera N. , Lupas Alina Alb
Published: December 2023

The purpose of the present paper is to introduce and investigate new subclasses of analytic function class of bi-univalent functions defined in open unit disks connected with a linear q-convolution operator, which are associated with quasi-subordination. We find coefficient estimates of h2, h3 for functions in these subclasses. Several known and new consequences of these results are also pointed out. There is symmetry between the results of the subclass fq,∑ μ(ζ,n,ρ,σ,ϑ,γ,δ,φ) and the results of the subclass ℵ∑q,δλ,ζ,n,ρ,σ,ϑ,φ.
TT¯ Deformation: A Lattice Approach
Authors: Jiang Yunfeng
Published: December 2023

Integrable quantum field theories can be regularized on the lattice while preserving integrability. The resulting theories on the lattice are integrable lattice models. A prototype of such a regularization is the correspondence between a sine-Gordon model and a six-vertex model on a light-cone lattice. We propose an integrable deformation of the light-cone lattice model such that in the continuum limit we obtain the TT¯-deformed sine-Gordon model. Under this deformation, the cut-off momentum becomes energy dependent and the underlying Yang–Baxter integrability is preserved. Therefore, this deformation is integrable but non-local: similar to the TT¯ deformation of quantum field theory.
Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis
Authors: Jain Siddhant , Geraci Joseph , Ruda Harry E.
Published: December 2023

The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis model to simultaneously excel at high-quality sampling, achieve mode convergence with diverse sample representation, and perform rapid sampling. In this paper, we explore the potential of Quantum Boltzmann Machines (QBMs) for image synthesis, leveraging the D-Wave 2000Q quantum annealer. We undertake a comprehensive performance assessment of QBMs in comparison to established generative models in the field: Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Our evaluation is grounded in widely recognized scoring metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Scores. The results of our study indicate that QBMs do not significantly outperform the conventional models in terms of the three evaluative criteria. Moreover, QBMs have not demonstrated the capability to overcome the challenges outlined in the Trilemma of Generative Learning. Through our investigation, we contribute to the understanding of quantum computing’s role in generative learning and identify critical areas for future research to enhance the capabilities of image synthesis models.
Generating Mathematical Expressions for Estimation of Atomic Coordinates of Carbon Nanotubes Using Genetic Programming Symbolic Regression
Authors: Anđelić Nikola , Baressi Šegota Sandi
Published: December 2023

The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming Symbolic Regression (GPSR) method on a publicly available dataset. The primary aim is to assess if the resulting Mathematical Equations (MEs) from GPSR can accurately estimate calculated atomic coordinates obtained through DFT. Given the numerous hyperparameters in GPSR, a Random Hyperparameter Value Search (RHVS) method is devised to pinpoint the optimal combination of hyperparameter values, maximizing estimation accuracy. Two distinct approaches are considered. The first involves applying GPSR to estimate calculated coordinates (uc, vc, wc) using all input variables (initial atomic coordinates u, v, w, and integers n, m specifying the chiral vector). The second approach applies GPSR to estimate each calculated atomic coordinate using integers n and m alongside the corresponding initial atomic coordinates. This results in the creation of six different dataset variations. The GPSR algorithm undergoes training via a 5-fold cross-validation process. The evaluation metrics include the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and the depth and length of generated MEs. The findings from this approach demonstrate that GPSR can effectively estimate CNT atomic coordinates with high accuracy, as indicated by an impressive R2≈1.0. This study not only contributes to the advancement of accurate estimation techniques for atomic coordinates but also introduces a systematic approach for optimizing hyperparameters in GPSR, showcasing its potential for broader applications in materials science and computational chemistry.
Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response
Authors: Shoaib Faizan , Chow Yang-Wai , Vlahu-Gjorgievska Elena , Nguyen Chau
Published: December 2023

Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%.
Ultrastructural Changes in the Midgut of Brazilian Native Stingless Bee Melipona scutellaris Exposed to Fungicide Pyraclostrobin
Authors: Domingues Caio E. C. , Inoue Lais V. B. , Gregorc Aleš , Ansaloni Leticia S. , Malaspina Osmar , Mathias da Silva Elaine C.
Published: December 2023

Melipona scutellaris is a Brazilian stingless bee that is important for pollinating wild flora and agriculture crops. Fungicides have been widely used in agriculture, and floral residues can affect forager bees. The goal of our study was to evaluate the effects of sublethal concentrations of pyraclostrobin on the midgut ultrastructure of M. scutellaris forager workers. The bees were collected from three non-parental colonies and kept under laboratory conditions. The bees were orally exposed continuously for five days to pyraclostrobin in syrup at concentrations of 0.125 ng a.i./µL (FG1) and 0.005 ng a.i./µL (FG2). The control bees (CTL) were fed a no-fungicide sucrose solution, and the acetone solvent control bees (CAC) received a sucrose solution containing acetone. At the end of the exposure, the midguts were sampled, fixed in Karnovsky solution, and routinely processed for transmission electron microscopy. Ultrastructural analysis demonstrated that both the fungicide concentrations altered the midgut, such as cytoplasmic vacuolization (more intense in FG1), the presence of an atypical nuclear morphology, and slightly dilated mitochondrial cristae in the bees from the FG1 and FG2 groups (both more intense in FG1). Additionally, there was an alteration in the ultrastructure of the spherocrystals (FG1), which could be the result of cellular metabolism impairment and the excretion of toxic metabolites in the digestive cells as a response to fungicide exposure. The results indicate that ingested pyraclostrobin induced cytotoxic effects in the midgut of native stingless bees. These cellular ultrastructural responses of the midgut are a prelude to a reduced survival rate, as observed in previous studies.