bluuebunny/crossref_metadata_2025 huggingface dataset:
Mammalian Cell Cytotoxicity, Antibacterial Activity and the Properties of Methylenebis(Hydroxybenzoic Acid) and Its Related Zinc(II) Complex
Authors: Ahmed Ayman H. , Althobaiti Ibrahim O. , Aljohani Marwah , Gad Ehab S. , Asiri Yazeed M. , Hussein Omar A.
Published: January 2024

Formaldehyde, sulfuric acid and salicylic acid were combined to create a 3,3′-methylenebis(2-hydroxybenzoic acid) (MHB) ligand, which was subsequently permitted to bind with zinc(II) ions. The ligand and its zinc(II) complex (Zn–MHB) have been described by a combination of elemental analyses, spectral analyses (UV–Vis, IR, MS and NMR), XRD, TEM, as well as TGA measurement. The ligand has been suggested to coordinate to the zinc center in a tetradentate manner forming the binuclear tetrahedral complex. An X-ray analysis indicated a considerable difference between MHB (crystalline) and Zn–MHB (amorphous). The UV–Vis spectra were used to determine the optical properties such as bandgap, refractive index, optical conductivity and penetration depth. The possibility of employing the samples for optoelectronic applications was indicated from the band gap values which underlie the range of semiconductors. TEM revealed the spherical shapes and mutation of ligand particles into the nano-scale by complexation. The antimicrobial potential of the MHB towards Gram-positive and Gram-negative bacterial growths has been investigated. The results suggested that it would be possible to employ MHB to prevent bacterial development, particularly that of salmonella typhimurium. The cytotoxicity of the MHB was assessed against two types of mammalian cells: VERO (the kidney of an African green monkey) and HFB4 (human skin melanocytes). Lower sensitivity was observed in VERO cells.
The First Attempt to Assess the Taxonomic Diversity of the Nepticulidae of Armenia Resulted in the Discovery of New Species and Cryptic Taxa in the Caucasus
Authors: Stonis Jonas , Dobrynina Viktorija , Remeikis Andrius , Diškus Arūnas , Orlovskytė Svetlana , Kalashian Mark
Published: January 2024

A taxonomic review of the Nepticulidae of Armenia is provided for the first time. Currently, the previously little-known fauna of the pygmy moths of Armenia consists of eight genera and 31 species, including seven new species described and illustrated in the current paper as follows: Stigmella armi Stonis, Dobrynina & Remeikis, sp. nov.; Stigmella garnica Stonis, Dobrynina & Remeikis, sp. nov.; Stigmella inopinoides Dobrynina, sp. nov.; Stigmella magicis Stonis & Dobrynina, sp. nov.; Stigmella ararati Stonis, Dobrynina & Remeikis, sp. nov.; Trifurcula vardenisi Stonis, Dobrynina & Remeikis, sp. nov.; and Etainia caucasi Remeikis, sp. nov. The most common Nepticulidae species in Armenia, Stigmella muricatella (Klimesch), still remains a taxonomic puzzle. However, the molecular sequences of the Armenian specimens and a specimen of S. muricatella from Greece were found to be similar, though clearly distinct from the widespread S. rolandi van Nieukerken due to numerous hypothetical mutational steps. Mitotype analysis revealed that the Ectoedemia specimens collected in Armenia do not belong to the expected widespread E. spinosella (Joannis) but are more similar to the less-known E. mahalebella (Klimesch). Moreover, some specimens from Armenia identified by us as Simplimorpha promissa (Staudinger) and Ectoedemia (Zimmermannia) longicaudella Klimesch may actually represent possible cryptic taxa, allopatric subspecies or species. However, from a practical point of view, the description of such difficult-to-diagnose taxa is inappropriate. Thus, we have refrained from describing such new taxa based solely on observed molecular differences.
Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo
Authors: Surkov Yuriy I. , Serebryakova Isabella A. , Kuzinova Yana K. , Konopatskova Olga M. , Safronov Dmitriy V. , Kapralov Sergey V. , Genina Elina A. , Tuchin Valery V.
Published: January 2024

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650–800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%.
Incidence of Rotavirus-Related Hospitalizations in an Italian Southern Region from 2015 to 2021
Authors: Di Martino Giuseppe , Cedrone Fabrizio , D’Addezio Michela , Odio Camillo , Di Giovanni Pamela , Trebbi Edoardo , Tognaccini Livia , Romano Ferdinando , Staniscia Tommaso
Published: January 2024

(1) Background: Rotavirus is one of the leading causes of severe diarrhea and dehydration in infants and young children worldwide. The economic and social burden of rotavirus-related hospitalizations, particularly among children, remains a pressing concern for healthcare systems across the globe. Healthcare infrastructure and access to medical care can vary significantly within the region. Differences in the availability of healthcare facilities and the quality of care may influence the management and outcomes of rotavirus cases. (2) Methods: This was a retrospective study performed in the Abruzzo region, Italy. The study considered all hospitalization due to rotavirus gastroenteritis that occurred in the Abruzzo region from the year 2015 to 2021. Data were extracted from the hospital discharge records. The trend in hospital admissions, hospitalization costs and length of stay were evaluated and analyzed. (3) Results: A total of 664 admissions were reported during the study period. The incident rate grew till year 2019 with an annual percentage change of +13.9% (95%CI 12.6–15.2, p < 0.001). During the pandemic years, the incident rate showed a significant decrease with an annual percentage change of 12.5% (95%CI 15.5–9.3, p = 0.004). The length of stay of admissions was significantly higher among patients aged less than 1 year. (4) Conclusions: Rotavirus admission represents a heavy burden even in a high-income country such as Italy. These findings have the potential to inform targeted public health interventions, including vaccination strategies, and improve the overall well-being of children.
Desmoid Fibromatosis of the Anterior Abdominal Wall in Pregnancy: A Case Report and Review of the Literature
Authors: Zubor Pavol , Henriksen Caroline Marie , Økstad Maren Elvenes , Cerskuviene Erika , Visnovsky Jozef , Kajo Karol , Valkov Andrey , Lind Kristen Olav
Published: January 2024

A desmoid tumor (DT) is a rare benign neoplasm arising from muscle aponeurosis, associated mostly with trauma or pregnancy. DT has an infiltrative and locally aggressive growth pattern and usually does not metastasize. However, it has a high recurrence and complication rate. When it occurs in pregnancy, the pregnancy and delivery is taken as an individual case for optimal management by physicians and midwifes, who need to be cautious in finding the optimal delivery mode for the patient, which depends on the tumor size, location, behavior, and past history. The authors report a case of 29-year-old pregnant woman who previously underwent systemic oncological treatment for a large abdominal wall desmoid tumor and became pregnant afterwards. The history of DT presented a follow-up and delivery challenge. Observational management was chosen with an elective cesarean section at week 38 + 4 of pregnancy with uncomplicated postpartum follow-up. The authors detail the clinical management and chosen therapeutic approach; chemotherapy can be a choice in the treatment options for patients with DTs, although the majority of DTs are treated surgically with subsequent mesh plastic. Moreover, the authors provide a systematic review of the literature focused on the treatment management of DTs in pregnant women during pregnancy and the postpartum period, as pregnancy-associated desmoid tumors are a specific condition, where the optimal management is not well established, despite some guidelines for non-pregnant patients.
Predictive State Observer-Based Aircraft Distributed Formation Tracking Considering Input Delay and Saturations
Authors: Sun Liguo , Liu Xiaoyu , Tan Wenqian , Deng Yi , Jiao Junkai , Zhao Mengjie
Published: January 2024

This paper investigates a fully distributed time-varying formation tracking problem for a group of fixed-wing aircraft. The fixed-wing aircraft formation control system consists of an outer-loop trajectory control subsystem and an inner-loop attitude control subsystem. For fixed-wing aircraft, it is crucial to consider the time delay of the engine response, the model uncertainties, the tracking capability of the attitude commands in the inner loop, and other agility performances of the aircraft. To address the problems related to the input time delay and model uncertainties, a predictive extended state observer-based fully distributed time-varying formation tracking control (PESO-TVFTC) protocol is proposed. To satisfy the constraints set by the attitude tracking quickness and the trajectory tracking smoothness, the low gain feedback technique is introduced in the protocol to keep the control inputs for the outer loop within the desired saturation constraints. Through theoretical analysis, it is proved that the multiple aircraft systems can achieve time-varying formation tracking consensus under specific initial conditions and feasibility conditions, and it is shown that the upper bounds of the PESO gains are restricted by the time delay. Numerical simulations are used to demonstrate the effectiveness of and the improvements in the proposed method.
Estimating Linear Dynamic Panels with Recentered Moments
Authors: Bao Yong
Published: January 2024

This paper proposes estimating linear dynamic panels by explicitly exploiting the endogeneity of lagged dependent variables and expressing the crossmoments between the endogenous lagged dependent variables and disturbances in terms of model parameters. These moments, when recentered, form the basis for model estimation. The resulting estimator’s asymptotic properties are derived under different asymptotic regimes (large number of cross-sectional units or long time spans), stable conditions (with or without a unit root), and error characteristics (homoskedasticity or heteroskedasticity of different forms). Monte Carlo experiments show that it has very good finite-sample performance.
Photographs of Play: Narratives of Teaching
Authors: Garvis Susanne , Keary Anne , McCallum Natalie
Published: January 2024

Research affirms positive results between children’s play and academic success. While play is heavily featured within the early childhood curriculum, limited studies have explored how play is actually implemented within early years settings. The aim of this study was to enhance children’s play and learning by using photographs to explore play-related pedagogical practices of early childhood teachers in Australia over time. Using visual narrative inquiry, three families participated (mother and child) and shared school photos of the mother in an early childhood context and the current child in an early childhood context. The photos were analysed with a visual analysis approach to understand play and teaching across the two time periods. Key findings revealed what has changed and what has become part of the foundation of play in Australian early childhood education. Findings are important to expand the current understanding of play and the historical perspectives of play.
Using Wikipedia to Develop 21st Century Skills: Perspectives from General Education Students
Authors: Remmik Marvi , Siiman Ann , Reinsalu Riina , Vija Maigi , Org Andrus
Published: January 2024

Purpose: The aim of the study was to find out how and why students use Wikipedia and what their attitudes are towards Wikipedia as a source of information for learning. Methodology: The article is based on a quantitative study in which 381 Estonian school children participated in filling out an online survey. The questionnaire included both multiple-choice and open-ended questions. Findings: Statistical analyses and responses to open-ended questions showed that students often use Wikipedia as a primary source of information, but that their use of the site for learning tasks is guided by teachers’ attitudes and perceptions towards Wikipedia. Students perceive Wikipedia as a quick and convenient source of information but are uncertain about its reliability. Under teachers’ guidance, they have learnt to search for information and to be source-critical, while more attention is needed to develop academic literacy, including both text comprehension and text composition. Value: As there is still very little research on how Wikipedia is used for learning purposes in general education schools, the results of the study contribute to further discussion on the potential of Wikipedia as an innovative teaching tool for different subjects.
Evaluating the Impact of the WEI4S Instructional Approach on Middle School Students’ Algebraic Problem-Solving Skills
Authors: Özcan Zeynep Çiğdem
Published: January 2024

This study investigates the effectiveness of an instructional approach called “Worked-out Examples Involving 4 Stages” (WEI4S) for enhancing middle school students’ algebraic problem-solving skills and reducing CL. Based on cognitive load theory (CLT), WEI4S combines four stages (analyse, explain, investigate, and apply) to facilitate deep comprehension. This study involved 36 novice seventh-grade students identified as having challenges solving algebraic word problems. Participants were randomly assigned to different groups, including the experimental group that received the WEI4S intervention and a control group that participated in problem-solving activities. Pre- and post-tests, as well as a transfer test, were administered to measure algebra performance and CL, and students’ opinions about the intervention were collected. The findings revealed that students exposed to WEI4S showed significant improvements in algebra performance, decreased intrinsic CL, and no negative changes in terms of extrinsic and germane load compared to the control group. Overall, the WEI4S approach offers a promising avenue for effective mathematics instruction by providing a structured framework that encourages active engagement and problem-solving skills.
Assessing Teachers’ Capabilities to Work with Models and Evaluate Results in the Context of a Complex and Authentic STEM Problem
Authors: Greubel André , Siller Hans-Stefan , Hennecke Martin
Published: January 2024

Since the practice turn, the contemporary education landscape has been shifting from mere knowledge dissemination to empowering students to solve problems. Special emphasis is given to problems on which students work for an extended period (at least several hours; frequently multiple school days). While working on such problems, it is essential to employ a variety of activities. Two of these are working with models and evaluating models and their results. One topic that has received little attention up to now is the question of to what extent educators are able to apply these skills. This study, fundamentally exploratory in nature, seeks to delve into such an assessment by evaluating the competence of n=20 educators in estimating and evaluating building evacuation duration using digital simulations. Our results show that the participants self-assessed as being able to solve such exercises. However, this was contrasted by our external assessment of the solutions provided by the participants, which showed that the solutions lacked in quality.
Deep Learning-Based Ensemble Approach for Autonomous Object Manipulation with an Anthropomorphic Soft Robot Hand
Authors: Valarezo Añazco Edwin , Guerrero Sara , Rivera Lopez Patricio , Oh Ji-Heon , Ryu Ga-Hyeon , Kim Tae-Seong
Published: January 2024

Autonomous object manipulation is a challenging task in robotics because it requires an essential understanding of the object’s parameters such as position, 3D shape, grasping (i.e., touching) areas, and orientation. This work presents an autonomous object manipulation system using an anthropomorphic soft robot hand with deep learning (DL) vision intelligence for object detection, 3D shape reconstruction, and object grasping area generation. Object detection is performed using Faster-RCNN and an RGB-D sensor to produce a partial depth view of the objects randomly located in the working space. Three-dimensional object shape reconstruction is performed using U-Net based on 3D convolutions with bottle-neck layers and skip connections generating a complete 3D shape of the object from the sensed single-depth view. Then, the grasping position and orientation are computed based on the reconstructed 3D object information (e.g., object shape and size) using U-Net based on 3D convolutions and Principal Component Analysis (PCA), respectively. The proposed autonomous object manipulation system is evaluated by grasping and relocating twelve objects not included in the training database, achieving an average of 95% successful object grasping and 93% object relocations.
Privacy-Preserving Vertical Federated KNN Feature Imputation Method
Authors: Du Wenyou , Wang Yichen , Meng Guanglei , Guo Yuming
Published: January 2024

Federated learning stands as a pivotal component in the construction of data infrastructure. It significantly fortifies the safety and reliability of data circulation links, facilitating credible sharing and openness among diverse subjects. The presence of missing data poses a pervasive and challenging issue in the implementation of federated learning. Current research on imputation missing values predominantly concentrates on centralized methods and horizontal federation scenarios. However, there is a notable absence of exploration in the context of vertical federated application scenarios. In this paper, the problem of missing imputation in vertical federated learning is investigated and a novel vertical federated k-nearest neighbors (KNN) imputation method is proposed. Extensive experiments are conducted using publicly available data sets to compare existing imputation methods, the results demonstrate the effectiveness and progress of our approach.
Dynamic Telemetry and Deep Neural Networks for Anomaly Detection in 6G Software-Defined Networks
Authors: Rzym Grzegorz , Masny Amadeusz , Chołda Piotr
Published: January 2024

With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures. This article introduces a meticulously crafted solution designed explicitly for 6G software-defined networks (SDNs). The approach employs deep neural networks for anomaly detection within network traffic, contributing to a more robust security framework. Furthermore, the paper delves into the realm of network monitoring automation by harnessing dynamic telemetry, providing a specialized and forward-looking strategy to tackle the distinctive challenges inherent in SDN environments. In essence, our proposed solution aims to elevate the security and responsiveness of 6G mobile networks. By addressing the intricate challenges posed by next-generation network architectures, it seeks to fortify these networks against emerging threats and dynamically adapt to the evolving landscape of next-generation technology.
On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features
Authors: Koziel Slawomir , Pietrenko-Dabrowska Anna , Pankiewicz Bogdan
Published: January 2024

Development of present-day antenna systems is an intricate and multi-step process requiring, among others, meticulous tuning of designable (mainly geometry) parameters. Concerning the latter, the most reliable approach is rigorous numerical optimization, which tends to be resource-intensive in terms of computing due to involving full-wave electromagnetic (EM) simulations. The cost-related issues are particularly pronounced whenever global optimization is necessary, typically carried out using nature-inspired algorithms. Although capable of escaping from local optima, population-based algorithms exhibit poor computational efficiency, to the extent of being hardly feasible when directly handling EM simulation models. A popular mitigation approach involves surrogate modeling techniques, facilitating the search process by replacing costly EM analyses with a fast metamodel. Yet, surrogate-assisted procedures feature complex implementations, and their range of applicability is limited in terms of design space dimensionality that can be efficiently handled. Rendering reliable surrogates is additionally encumbered by highly nonlinear antenna characteristics. This paper investigates potential benefits of employing problem-relevant knowledge in the form of response features into nature-inspired antenna optimization. As demonstrated in the recent literature, re-formulating the design task with the use of appropriately selected characteristic locations of the antenna responses permits flattening the functional landscape of the objective function, leading to faster convergence of optimization procedures. Here, we apply this concept to nature-inspired global optimization of multi-band antenna structures, and demonstrate its relevance, both in terms of accelerating the search process but also improving its reliability. The advantages of feature-based nature-inspired optimization are corroborated through comprehensive (based on three antenna structures) comparisons with a population-based search involving conventional (e.g., minimax) design problem formulation.
Prototype for the Application of Production of Heavy Steel Structures
Authors: Bulganbayev Muratbek , Suliyev Rassim , Fonseca Ferreira Nuno
Published: January 2024

This study provides a comprehensive overview of the automated assembly process of large-scale metal structures using industrial robots. Our research reveals that the utilization of industrial robots significantly enhances precision, speed, and cost-effectiveness in the assembly process. The main findings suggest that integrating industrial robots in metal structure assembly holds substantial promise for optimizing manufacturing processes and elevating the quality of the final products. Additionally, the research demonstrates that robotic automation in assembly operations can lead to significant improvements in resource utilization and operational consistency. This automation also offers a viable solution to the challenges of manual labor shortages and ensures a higher standard of safety and accuracy in the manufacturing environment.
Smart Waiting Room: A Systematic Literature Review and a Proposal
Authors: Spoladore Daniele , Mondellini Marta , Mahroo Atieh , Chicchi-Giglioli Irene Alice , De Gaspari Stefano , Di Lernia Daniele , Riva Giuseppe , Bellini Elena , Setola Nicoletta , Sacco Marco
Published: January 2024

In the past decade, Internet of Things (IoT) technologies have become more and more pervasive, allowing the development of Ambient Intelligence and Context Awareness applications. While the smart home is the chosen scenario for such technologies, a variety of environments could potentially benefit from the synergies between IoT and digital applications. Among them, the clinical waiting rooms is an environment facing several challenges, particularly after the COVID-19 pandemic. Clinical waiting rooms are crowded places associated with stress and anxiety, often linked to patients’ idling time. Nonetheless, the rise of IoT-enabled telehealth has focused attention on the possibility of changing the functions of clinical waiting rooms. In this work, we conducted a systematic literature review to identify the existing solutions for “smart waiting rooms”. The review process started with 278 works, and it identified 16 papers relevant to the topic of smart clinical waiting rooms. Moreover, the results of this review underlined that waiting rooms are a neglected research area that could take advantage of IoT and Artificial Intelligence (AI) technologies to enhance patients’ experience and support the diagnostic process. Leveraging the research directions identified in the review process, this work proposes a “Smart Waiting Room” that exploits virtual reality (VR), AI, and IoT technologies to support the diagnostic process (via a cognitive assessment) and the personalization of the patient’s context—taking into account the features of the physical environment. The prototypical smart waiting room constitutes an example of the potentialities of novel technologies applied to healthcare and can contribute to shaping the future of clinical waiting rooms.
Joint Optimization of Task Caching and Computation Offloading for Multiuser Multitasking in Mobile Edge Computing
Authors: Zhu Xintong , Jia Zongpu , Pang Xiaoyan , Zhao Shan
Published: January 2024

Mobile edge computing extends the capabilities of the cloud to the edge to meet the latency performance required by new types of applications. Task caching reduces network energy consumption by caching task applications and associated databases in advance on edge devices. However, determining an effective caching strategy is crucial since users generate numerous repetitive tasks, but edge devices and storage resources are limited. We aimed to address the problem of highly coupled decision variables in dynamic task caching and computational offloading for multiuser multitasking in mobile edge computing systems. This paper presents a joint computation and caching framework with the aim of minimizing delays and energy expenditure for mobile users and transforming the problem into a form of reinforcement learning. Based on this, an improved deep reinforcement learning algorithm, P-DDPG, is proposed to achieve efficient computation offloading and task caching decisions for mobile users. The algorithm integrates a deep and deterministic policy grading and a prioritized empirical replay mechanism to reduce system costs. The simulations show that the designed algorithm performs better in terms of task latencies and lower computing power consumption.
A Comprehensive Analysis of PINNs for Power System Transient Stability
Authors: de Cominges Guerra Ignacio , Li Wenting , Wang Ren
Published: January 2024

The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.
Robust Scheduling of Multi-Skilled Workforce Allocation: Job Rotation Approach
Authors: Szwarc Eryk , Golińska-Dawson Paulina , Bocewicz Grzegorz , Banaszak Zbigniew
Published: January 2024

This paper addresses scheduling challenges in software development organizations, specifically focusing on a novel version of the software project scheduling problem (SPSP). This enhanced model incorporates the dynamics of learning and forgetting phenomena, crucial in maintaining employee competencies, particularly when unexpected events such as absenteeism or shifts in project priorities occur. The paper introduces a new declarative reference model for SPSP, aimed at proactively managing the assignment of versatile programmers to tasks within an portfolio of IT projects, while considering the effects of forgetting. Implemented within a constraints programming environment, this model facilitates decision making in project management for software companies. It serves to find feasible solutions and identify conditions necessary to meet specified expectations. The effectiveness of the proposed SPSP model is demonstrated through numerical examples.
Implementation and Adaptability Analysis of Numerical Simulation for Shale Oil CO2 Huff and Puff
Authors: Zhao Fenglan , Wang Yu , Huang Shijun , Liu Miaomiao , Yang Changhe
Published: January 2024

Carbon dioxide (CO2) is being considered for use to enhance oil recovery and resource utilization and storage, with wide technical adaptability. In this paper, a numerical simulation method is used to study the adaptability of CO2 huff and puff in shale reservoirs. A fluid model introduces the nanoconfinement effect and reflects the nanoconfinement effect using the fluid p–T phase diagram. This method uses local grid refinement and changes the permeability near the grid to characterize the reservoir reconstruction volume (SRV) fracture network while considering the CO2 diffusion effect. The results indicate that by using the incremental recovery rate and oil change rate as references, adaptive charts can be obtained for different Kf/Km and oil saturation. When Kf/Km is 1000 and the increase in the CO2 recovery rate reaches 1.5%, the lower limit of oil saturation is 0.54. When Kf/Km is 1000 and the increase in the CO2 recovery rate reaches 2%, the lower limit of oil saturation is 0.57. When the oil saturation is 0.5 and the CO2 huff and puff oil change rate reaches 0.3, the lower limit of Kf/Km is 700. Finally, when the oil saturation is 0.548 and the CO2 huff and puff oil change rate reaches 0.6, the lower limit of Kf/Km is 10. The research results are significant and can guide the design and application of on-site CO2 throughput test plans.
Revolutionizing Environmental Sustainability: The Role of Renewable Energy Consumption and Environmental Technologies in OECD Countries
Authors: Liu Xi , He Yugang , Wu Renhong
Published: January 2024

This study examines the relationship between economic factors and environmental sustainability in OECD (Organisation for Economic Co-operation and Development) countries from 1990 to 2022, with a particular focus on the impact of renewable energy consumption and environmental technologies on CO2 emissions. The research utilizes empirical data to establish a clear negative correlation between the adoption of renewable energy sources and the level of CO2 emissions, highlighting the effectiveness of renewable energy in reducing the environmental impact of economic activities. This finding supports the theoretical perspective that transitioning to cleaner energy sources is vital for achieving environmental sustainability and aligns with the objectives of the OECD’s environmental sustainability program. Further analysis reveals a significant negative impact of environmental technologies on CO2 emissions, underscoring the importance of technological innovation in environmental conservation efforts. The study also explores the dual influence of GDP growth, urbanization, industrialization, and trade on CO2 emissions, revealing both positive and negative effects across different stages of economic development. Initially, these factors contribute to increased emissions, but as economies mature and integrate more efficient and cleaner technologies, their impact on emissions becomes negative. These findings demonstrate the complex interplay between economic development and environmental sustainability and emphasize the need for policies that encourage renewable energy adoption, support environmental technological innovations, and guide economies towards sustainable practices. The study provides valuable insights for policymakers and stakeholders, advocating for an integrated approach to ensure long-term environmental sustainability in OECD countries.
A Novel Approach to Using Dual-Field Excited Synchronous Generators as Wind Power Generators
Authors: Agarala Ajaysekhar , Bhat Sunil S. , Zychma Daria , Sowa Pawel
Published: January 2024

Integrating wind power generators, whose frequency varies in a wide range due to varying wind speeds, into a grid is a formidable problem. At present, the use of permanent magnet synchronous generators (PMSG) and doubly fed induction generators (DFIG) as wind generators with suitable control is the best possible solution. However, a dual-field excited synchronous generator (DESG), which has two windings on the rotor, can also be used for the same purpose with appropriate control. A new control strategy, which essentially employs the d-axis and q-axis components of the alternator terminal voltage, is suggested here. This strategy essentially results in exciting the two field windings with a slip frequency. This eventually holds the stator frequency constant, irrespective of the rotor speed of the wind generator. The difference between the required frequency and the natural frequency, analogous to the rotor speed of the wind power generator, is the slip frequency. The ring modulator automatically adjusts the slip frequency depending on the actual speed of the generator’s rotor. This paper uses the ANSYS MAXWELL 2022 R1 software to design a DESG and uses a ring modulator as the control function generator for feedback with ANSYS TWIN BUILDER 2022 R1. Simulations are carried out using transient–transient co-simulation by combining both of these software tools for cases of both a constant-speed input and of a variable-speed input to the rotor of the machine. Moreover, a mathematical model of the DESG as a wind generator with the proposed controlled strategy is used to perform the stability analysis of a nine-bus three-machine system, and the results are compared with those of conventional wind generators.
An Investigation of Structural Strength of Nuclear Fuel Spacer Grid
Authors: Adli Naqeeb Hakam , Namgung Ihn
Published: January 2024

This paper compares and discusses the methods for evaluation of the structural integrity of the mid spacer grid of nuclear fuel assembly via a finite element analysis of 3D shell elements. The structural stiffness of the spacer grid is determined by applying either force or deformation as loads onto the spacer grid for both the square load and shear load directions. This study is an extension of a single-cell strength analysis of a spacer. External events such as seismic activities that might happen in a nuclear reactor are able to transfer loads onto nuclear components in random directions, which can be broken down into square and shear loadings. The structural strength indicated by the force reaction against the input displacement load was proven to be smaller such that the same displacement square load is around 260 times greater than the shear load. Due to the weakness in shear stiffness, the maintenance of a spacer grid structure is more vulnerable against out-of-plane loads. This indicates that the shear load needs to be considered in studies of fuel assembly integrity assessment for newly developing fuel design, as well as existing fuel assembly designs.
Theoretical Investigation on Selected 3-Amino-5-[(2,4,6-trinitrophenyl)amino]-1H-1,2,4-triazole Salts with Energetic Anions: A Perspective
Authors: Tamuliene Jelena , Sarlauskas Jonas
Published: January 2024

The current work is dedicated to the search for new high-energy materials (HEMs) with improved characteristics, which are gained through agglomeration with salts. The research was performed by Becke’s three-parameter hybrid functional approach, with non-local correlation provided by Lee, Yang, and Parr, and the cc-pVTZ basis set. The structure, total energy, and heat of formation, presented as binding energy per atom of the most stable compounds formed due to 3-amino-5-[(2,4,6-trinitrophenyl) amino]-1H-1,2,4-triazole (APATO) within selected salts, were obtained to foresee its influence on resistance to shock stimuli, detonation pressure, and velocity of the materials under study. The results obtained allow us to foresee that only agglomeration with precise salts could lead to a significant improvement in the stability of the specific high-energy materials and resistance to shock stimuli. We also show that agglomeration leads to better energetic properties of the above-mentioned compound, although the improvement may be insignificant in some cases.
Wood Basic Density Assessment of Eucalyptus Genotypes Growing under Contrasting Water Availability Conditions
Authors: Elissetche Juan Pedro , Alzamora Rosa M. , Espinoza Yosselin , Emhart Verónica , Pincheira Matías , Medina Alex , Rubilar Rafael
Published: January 2024

Limited research exists on wood properties in E. nitens × E. globulus hybrid genotypes grown in water-limited conditions generated by the climate change influence in Mediterranean areas. The EUCAHYDRO project aimed to assess environmental stress impacts on eucalyptus genotypes, including responses to reduced water availability, and to evaluate wood density and resistance using the non-destructive drill resistance technique. This study focused on 10-year-old E. nitens × E. globulus hybrids and Eucalyptus badjensis Beuzev. & Welch, revealing that low irrigation led to a 13% (p < 0.05) decrease in diameter and a 6.5% (p > 0.05) increase in wood density for the hybrids. On the contrary, E. badjensis demonstrated a noteworthy 22% increase in wood density (p < 0.05), while showing a corresponding 0.6% growth increase (p > 0.05), as measured by the diameter at breast height. Drill resistance measurement results indicated that E. nitens × E. globulus’ amplitude ranged from 20.1% to 26.6%, while E. badjensis’ ranged from 20.8% to 27.2%. The study revealed a high correlation between resistance amplitude and wood density, with coefficients of 0.97 observed for E. nitens × E. globulus and 0.84 for E. badjensis (p < 0.05). These findings fall within the range reported in similar studies involving Eucalyptus spp. The primary focus of the research was to assess the growth potential and wood quality of novel Eucalyptus spp. under conditions of water limitation. The study also explored the utility of drill resistance as a predictive measure for evaluating wood density as an indicator of wood quality.
Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning
Authors: Rust Steffen , Stoinski Bernhard
Published: January 2024

As remote sensing transforms forest and urban tree management, automating tree species classification is now a major challenge to harness these advances for forestry and urban management. This study investigated the use of structural bark features from terrestrial laser scanner point cloud data for tree species identification. It presents a novel mathematical approach for describing bark characteristics, which have traditionally been used by experts for the visual identification of tree species. These features were used to train four machine learning algorithms (decision trees, random forests, XGBoost, and support vector machines). These methods achieved high classification accuracies between 83% (decision tree) and 96% (XGBoost) with a data set of 85 trees of four species collected near Krakow, Poland. The results suggest that bark features from point cloud data could significantly aid species identification, potentially reducing the amount of training data required by leveraging centuries of botanical knowledge. This computationally efficient approach might allow for real-time species classification.
Enhancing Afforestation Practices in Hilly Terrain: A Study on Soil Disturbance by Earth Augers Based on the Discrete Element Method
Authors: Wang Guofu , Zhang Wei , Diao Xingliang , Ji Min , Miao Hu , Chen Meiling
Published: January 2024

Afforestation operations in hilly regions are both arduous and unsafe. The mechanized afforestation method that takes into account soil and water conservation measures is deemed highly important. This paper examines the operational process and the auger’s mechanism of digging below the ground using the discrete element method (DEM). Using this model, soil disturbance parameters and reaction forces are satisfactorily predicted, exhibiting similar trends to experimental observations. This research also examines the influence of key parameters on soil disturbance and distribution patterns and analyzes the conditions and mechanisms of the formation of fish-scale pits to preserve soil and water. A field experiment of pit digging in woodland is carried out to test the performance of the device. The error rates for the actual and simulated values of the efficiency of conveying soil and the distance of throwing soil on plain terrain and slopes were 12.7% and 8.2%, and 8.6% and 15.7%, respectively. Overall, this research provides a theoretical basis for the innovative exploration, development, and optimized design of earth augers in hilly regions.
Terminal-Enhanced Polymerization in the Biosynthesis of Polysialic Acid
Authors: Wang Chongchuan , Chang Huanan , Liu Xiaomeng , Zhao Haiyang , Guo Jianing , Peng Shuo , Wang Wenhao , Zhu Deqiang , Liu Xinli
Published: January 2024

Plasmids are commonly used tools in microbiology and molecular biology and have important and wide-ranging applications in the study of gene function, protein expression, and compound synthesis. The complex relationship between necessary antibiotic addition, compatibility between multiple plasmids, and the growth burden of host bacteria has plagued the wider use of compatibility plasmids. In this study, we constructed the terminal polymerization pathway of PSA by exogenously expressing the neuA, neuD, and neuS genes after the knockdown of Eschesrichia coli BL21 (DE3). Duet series vectors were utilized to regulate the expression level of neuA, neuD, and neuS genes to study the gene expression level, plasmid copy number growth burden, pressure of antibiotic addition, stability of compatible plasmids, and the level of expression stability of exogenous genes, as well as the effect on the biological reaction process. The results showed that the three genes, neuA, neuD, and neuS, were enhanced in the recombinant strain E. coli NA-05, with low copy, medium copy, and high copy, respectively. The effect of PSA synthesis under standard antibiotic pressure was remarkable. The results of this thesis suggest the use of a Duet series of compatible expression vectors to achieve the stable existence and co-expression of multiple genes in recombinant bacteria, which is a good reason for further research.
Identification and Combinatorial Overexpression of Key Genes for Enhancing ε-Poly-L-lysine Biosynthesis in Streptomyces albulus
Authors: Zhang Hongjian , Yang Hao , Zhang Chongyang , Zhu Daojun , Wang Liang , Zhang Jianhua , Chen Xusheng
Published: January 2024

ε-Poly-L-lysine (ε-PL) is a natural and safe food preservative mainly produced by the aerobic, filamentous bacterium Streptomyces albulus. Therefore, it is crucial to breed superior ε-PL-producing strains to enhance fermentation efficiency to reduce production costs. Metabolic engineering is an effective measure for strain modification, but there are few reports on key genes for ε-PL biosynthesis. In this study, metabolic flux analysis was employed to identify potential key genes in ε-PL biosynthesis in S. albulus WG-608. A total of six potential key genes were identified. Three effective key genes (ppc, pyc and pls) were identified for the first time in ε-PL biosynthesis through overexpression experiments. It also presents the first demonstration of the promoting effects of ppc and pyc on ε-PL biosynthesis. Three genes were then co-expressed in S. albulus WG-608 to obtain OE-ppc-pyc-pls, which exhibited an 11.4% increase in ε-PL production compared to S. albulus WG-608, with a 25.5% increase in specific ε-PL production. Finally, the metabolic flux analysis of OE-ppc-pyc-pls compared to S. albulus WG-608 demonstrated that OE-ppc-pyc-pls successfully altered the metabolic flux as expected. This study not only provides a theoretical basis for the metabolic engineering of ε-PL-producing strains but also provides an effective approach for the metabolic engineering of other metabolites.
Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
Authors: Fan Zhengyang , Li Wanru , Laskey Kathryn Blackmond , Chang Kuo-Chu
Published: January 2024

Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.