bluuebunny/crossref_metadata_2025 huggingface dataset:
A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier
Authors: Chen Xin , Zhang Hao , Zhang Shenghao , Feng Jiapeng , Xia Hui , Rao Peng , Ai Jianliang
Published: January 2024

Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets.
Evaluation and Modelling of the Coastal Geomorphological Changes of Deception Island since the 1970 Eruption and Its Involvement in Research Activity
Authors: Torrecillas Cristina , Zarzuelo Carmen , de la Fuente Jorge , Jigena-Antelo Bismarck , Prates Gonçalo
Published: January 2024

Deception Island is an active volcano with a submerged caldera open to the sea called Port Foster. Several post-caldera-collapsed volcanic events, as well as hydrodynamics, have changed its inner coastline, shaping new volcanic deposits. A hydrodynamic model is presented to predict accretion and erosion trends in this bay, which could have an impact on the mobility of researchers and tourists. New historical orthophotos and spatio-temporal differences between digital elevation and bathymetric models were used for validation purposes. The model reveals that the south-facing coast is more susceptible to erosion, while the east- or west-facing coast experiences sedimentation. A visual study for the periods 1970–2003 and 2003–2020 in Port Foster obtained similar annual erosion/accretion lineal rates (0.3–2 m/year) in the areas not affected by the last eruptive period, as well as increases of 0.023 km2/year and 0.028 km2/year of the inner bay and coastal sedimentation rates of 0.007 km2/year and 0.002 km2/year, respectively. Only part of the significant total volume loss is received within the bay, including its own erosion, and accumulates on the bay bottom. This is largely because the volume input is composed of snow, and it is also due to the transfer of material outside to balance the figures.
A New Determining Method for Ionospheric F2-Region Peak Electron Density Height
Authors: Wang Jian , Yu Qiao , Shi Yafei , Yang Cheng , Ji Shengyun , Zheng Yu
Published: January 2024

The height of the F2 peak electron density (hmF2) is an essential parameter in studying ionospheric electrodynamics and high-frequency wireless communication. Based on ionosphere ray propagation theory, the physical relationship between M3000F2 and hmF2 is derived and visualized. Furthermore, based on the above physical theory and the machine learning method, this paper proposes a new model for determining hmF2 using propagation factor at a distance of 3000 km from the ionospheric F2 layer, time, and season. This proposed model is easy to understand and has the characteristics of clear principles, simple structure, and easy application. Furthermore, we used six stations in east Asia to verify this model and compare it with the other three models of the International Reference Ionosphere (IRI) model. The results show that the proposed model (PRO) has minor error and higher accuracy. Specifically the RMSE of the BSE, AMTB, SHU, and the PRO models were 20.35 km, 31.51 km, 13.59 km, and 5.68 km, respectively, and the RRMSE of the BSE, AMTB, SHU, and PRO models were 8.17%, 11.88%, 4.96%, and 2.12%, respectively. In addition, the experimental results show that the PRO model can better predict the trend of the hmF2 inflection point. This method can be further extended to add data sources and provide new ideas for studying the hmF2 over global regions.
Fine-Grained Boundary Conditions in Field-Based Routing
Authors: Sung Jihoon , Kyung Yeunwoong
Published: January 2024

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement.
Innovating Household Food Waste Management: A User-Centric Approach with AHP–TRIZ Integration
Authors: Wang Shuyun , Park Hyunyim , Xu Jifeng
Published: January 2024

Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users’ actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users’ genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments.
LSTM-Based GNSS Localization Using Satellite Measurement Features Jointly with Pseudorange Residuals
Authors: Sbeity Ibrahim , Villien Christophe , Denis Benoît , Belmega Elena Veronica
Published: January 2024

In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has led to many challenges when it comes to choosing the most-accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting linkwise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we used a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise-power-density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a long short-term memory (LSTM) deep neural network capable of exploiting the hidden correlations between these features relevant to positioning, leading to the predictions of efficient measurement weights. Our extensive experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution, which is able to outperform traditional measurement weighting and selection strategies from the state-of-the-art. In addition, we included detailed illustrations based on representative sessions to provide a concrete understanding of the significant gains of our approach, particularly in strongly GNSS-challenged operating conditions.
Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network
Authors: Zaman Wasim , Ahmad Zahoor , Kim Jong-Myon
Published: January 2024

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump’s inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time–frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.
Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards
Authors: Ayele Eyuel Debebe , Gonzalez Javier Ferreira , Teeuw Wouter B.
Published: January 2024

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.
Multiphysics Modeling of Electrochemical Impedance Spectroscopy Responses of SAM-Modified Screen-Printed Electrodes
Authors: Franchin Lara , Bonaldo Stefano
Published: January 2024

In this work, we present a multiphysics modeling approach capable of simulating electrochemical impedance spectroscopy (EIS) responses of screen-printed electrodes (SPEs) modified with self-assembled monolayers of 11-Mercaptoundecanoic acid (MUA). Commercially available gold SPEs are electrochemically characterized through experimental cyclic voltammetry and EIS measurements with 10 mM [Fe(CN)6]3−/4− redox couple in phosphate buffered saline before and after the surface immobilization of MUA at different concentrations. We design the multiphysics model through COMSOL Multiphysics® based on the 3D geometry of the devices under test. The model includes four different physics considering the metal/solution interface electrochemical phenomena, the ion and electron potentials and currents, and the measurement set-up. The model is calibrated through a set of experimental measurements, allowing the tuning of the parameters used by the model. We use the calibrated model to simulate the EIS response of MUA-modified SPEs, comparing the results with experimental data. The simulations fit the experimental curves well, following the variation of MUA concentration on the surface from 1 µM to 100 µM. The EIS parameters, retrieved through a CPE-modified Randles’ circuit, confirm the consistency with the experimental data. Notably, the simulated surface coverage estimates and the variation of charge transfer resistance due to MUA-immobilization are well matched with their experimental counterparts, reporting only a 2% difference and being consistent with the experimental electrochemical behavior of the SPEs.
Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
Authors: Hosseinzadeh Salaheddin , Ashawa Moses , Owoh Nsikak , Larijani Hadi , Curtis Krystyna
Published: January 2024

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.
Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications
Authors: Hassan Mahmood Ul , Al-Awady Amin A. , Ali Abid , Iqbal Muhammad Munwar , Akram Muhammad , Jamil Harun
Published: January 2024

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).
Confining Pressure Forecasting of Shield Tunnel Lining Based on GRU Model and RNN Model
Authors: Wang Min , Ye Xiao-Wei , Jia Jin-Dian , Ying Xin-Hong , Ding Yang , Zhang Di , Sun Feng
Published: January 2024

The confining pressure has a great effect on the internal force of the tunnel. During construction, the confining pressure which has a crucial impact on tunnel construction changes due to the variation of groundwater level and applied load. Therefore, the safety of tunnels must have the magnitude of confining pressure accurately estimated. In this study, a complete tunnel confining pressure time axis was obtained through high-frequency field monitoring, the data are segmented into a training set and a testing set. Using GRU and RNN models, a confining pressure prediction model was established, and the prediction results were analyzed. The results indicate that the GRU model has a fast-training speed and higher accuracy. On the other hand, the training speed of the RNN model is slow, with lower accuracy. The dynamic characteristics of soil pressure during tunnel construction require accurate prediction models to maintain the safety of the tunnel. The comparison between GRU and RNN models not only highlights the advantages of the GRU model but also emphasizes the necessity of balancing speed accuracy in tunnel construction confining pressure prediction modeling. This study is helpful in improving the understanding of soil pressure dynamics and developing effective prediction tools to promote safer and more reliable tunnel construction practices.
Integrated Simulation and Calibration Framework for Heating System Optimization
Authors: Djebko Kirill , Weidner Daniel , Waleska Marcel , Krey Timo , Rausch Sven , Seipel Dietmar , Puppe Frank
Published: January 2024

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess “better or worse” system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.
Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population
Authors: Romeo August , Leonovych Oleksii , Solé Puig Maria , Supèr Hans
Published: January 2024

(1) Background: Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. (2) Methods: We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. (3) Results: Both tracking methods effectively captured vergence eye movements and demonstrated robust cognitive vergence responses, where participants exhibited larger vergence eye movement amplitudes in response to targets versus distractors. (4) Conclusions: In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could lay the groundwork for future research aimed at creating an affordable screening tool for mental health care.
Enhanced Sensitivity in Optical Sensors through Self-Image Theory and Graphene Oxide Coating
Authors: Cunha Cristina , Monteiro Catarina , Vaz António , Silva Susana , Frazão Orlando , Novais Susana
Published: January 2024

This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.0 software. Subsequently, the sensor was fabricated to a length corresponding to the second self-image point (29.12 mm) and coated with an 80 µm/mL graphene oxide film using the Layer-by-Layer technique. The sensor characterization in refractive index demonstrated a wavelength sensitivity of 200 ± 6 nm/RIU. Comparative evaluations of uncoated and graphene oxide-coated sensors applied to measure glucose in solutions ranging from 25 to 200 mg/dL showed an eightfold sensitivity improvement with one bilayer of Polyethyleneimine/graphene. The final graphene oxide-based sensor exhibited a sensitivity of 10.403 ± 0.004 pm/(mg/dL) and demonstrated stability with a low standard deviation of 0.46 pm/min and a maximum theoretical resolution of 1.90 mg/dL.
Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations
Authors: Sampurno Rizky Mulya , Liu Zifu , Abeyrathna R. M. Rasika D. , Ahamed Tofael
Published: January 2024

Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance segmentation were utilized in this research—YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg—for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time. The performance of YOLOv8n-seg was also acceptable when it was deployed on a resource-constrained device that is appropriate for robotic weeders. The results indicated that the proposed deep learning-based detection accuracy and inference speed can be used for object recognition via edge devices for robotic operation during intrarow weeding operations in orchards.
Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces
Authors: Martín-Martín Alberto , Padial-Allué Rubén , Castillo Encarnación , Parrilla Luis , Parellada-Serrano Ignacio , Morán Alejandro , García Antonio
Published: January 2024

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.
A Piezoelectric–Piezoresistive Coupling Electric Field Sensor for Large Dynamic Range AC Electric Field Measurements
Authors: Wang Xiaobo , Luo Bofeng , Zhang Rongbo , Song Yang , Zhao Yaqian , Luo Chengtao
Published: January 2024

We propose a piezoelectric–piezoresistive coupling electric field sensor capable of performing large dynamic range AC electric field measurements. The electric field sensor utilizes direct coupling between piezoelectric (PE) materials and piezoresistive (PR) strain gauges, in conjunction with an external signal conditioning circuit, to measure AC electric fields effectively. We verified the feasibility of the scheme using a finite element simulation, fabricated a prototype of the electric field sensor, and characterized the properties of the prototype. The testing results indicate that the sensor exhibits an ac resolution of 50 V/m and a linear measurable electric field range of 0 to over 200 kV/m, which keeps the linearity at less than 0.94% from 1 Hz to over 5 kHz. Furthermore, the sensor also has advantages, such as a small size and low power consumption. The sensor can enhance the comprehensive observability and measurability of digital power grids.
Novel Conductive Polymer Composite PEDOT:PSS/Bovine Serum Albumin for Microbial Bioelectrochemical Devices
Authors: Tarasov Sergei E. , Plekhanova Yulia V. , Bykov Aleksandr G. , Kadison Konstantin V. , Medvedeva Anastasia S. , Reshetilov Anatoly N. , Arlyapov Vyacheslav A.
Published: January 2024

A novel conductive composite based on PEDOT:PSS, BSA, and Nafion for effective immobilization of acetic acid bacteria on graphite electrodes as part of biosensors and microbial fuel cells has been proposed. It is shown that individual components in the composite do not have a significant negative effect on the catalytic activity of microorganisms during prolonged contact. The values of heterogeneous electron transport constants in the presence of two types of water-soluble mediators were calculated. The use of the composite as part of a microbial biosensor resulted in an electrode operating for more than 140 days. Additional modification of carbon electrodes with nanomaterial allowed to increase the sensitivity to glucose from 1.48 to 2.81 μA × mM−1 × cm−2 without affecting the affinity of bacterial enzyme complexes to the substrate. Cells in the presented composite, as part of a microbial fuel cell based on electrodes from thermally expanded graphite, retained the ability to generate electricity for more than 120 days using glucose solution as well as vegetable extract solutions as carbon sources. The obtained data expand the understanding of the composition of possible matrices for the immobilization of Gluconobacter bacteria and may be useful in the development of biosensors and biofuel cells.
The Effect of Generation Z’s Understanding of Free Will and Fatalism on Their Political Choice: A Field Study
Authors: Aksakal İbrahim , Avcı Müjdat
Published: January 2024

The aim of this study is to determine whether the fatalistic tendencies of young adult university students predict their political preferences. For this purpose, the fatalism tendency scale consisting of four sub-dimensions was applied, and the predictive power of the obtained data on political preferences was examined. The study sample consisted of 630 young adult university students (male 294, female 336) between the ages of 18 and 26. Independent variables are fatalism sub-dimensions (predetermination, personal control, luck, and superstition), and dependent variables are political leaders and political party preferences. Multinomial logistic regression analysis was used to determine the effect of independent variables on dependent variables. The analysis results revealed that the sub-dimensions of fatalism, predetermination, luck, and superstition significantly predicted voters’ preferences for political leaders. No significant relationship could be detected between the change in participants’ personal control tendency and their preference for political leaders. In the second stage of the analysis, it was examined whether the tendency towards fatalism affected the choice of political party. It was observed that the sub-dimensions of predetermination, luck, and superstition were significant predictors of political party preferences. There was no significant relationship between change in personal control tendency and political party preference. The research results revealed that if the tendency towards fatalism changes, there are also significant changes in political preferences.
Exploring Neighbourhood Integration Dynamics of Sri Lankan Entrepreneurs in Rione Sanità, Naples
Authors: Fraudatario Maria Camilla
Published: January 2024

Integration is always at the core of migration studies and is examined from various theoretical perspectives. While integration models are valuable for understanding how national political systems influence the integration of foreigners into society, the real challenge of integration manifests at the local level. From a neighbourhood-based approach, this article addresses the integration trajectories of Sri Lankan entrepreneurs in Rione Sanità, Naples, which is a socio-economically deprived neighbourhood hosting a substantial segment of foreign populations and has been the target of significant urban regeneration initiatives over the past decade. Sri Lankans established travel agencies, fiscal assistance centres, restaurants, takeaways, and retailers in a transformative context. This article highlights how entrepreneurial initiatives are shaped by the mutual connection linking immigrants with the place where they found economic and relational opportunities. The results serve as a crucial starting point for better understanding the long-term outcomes of the socio-economic integration at the neighbourhood level.
Experimental Investigation into Lightweight High Strength Concrete with Shale and Clay Ceramsite for Offshore Structures
Authors: Li Pei , Li Jie , Fan Ling , Mi Shendong , Li Junyi , Liu Haoqi , Peng Shuquan , Huang Wanqi
Published: January 2024

To develop lightweight high-strength concrete (LWHSC) for offshore structures in a harsh seawater environment, LWHSC with shale and clay ceramsites was designed. LWHSC was experimentally investigated in terms of density, compressive strength, and durability in a coastal environment. Then, its feasibility for offshore structures was also assessed. The results show that the compressive strength and oven dry density of LWHSC appropriately improve with increases in cement content, while they are reduced by the replacement of shale ceramsite with clay ceramsite. The compressive strength of LWHSC also increases first and then decreases with an increase in the pre-wetting of shale and clay ceramsites. Their optional pre-wetting time is about 0.5 h. LWHSC exhibits a higher brittleness compared with conventional concrete. LWHSC has increases in the resistances of freeze–thaw, carbonization, water penetration, and chloride penetration when the shale and clay ceramsite light aggregates decrease in the concrete. The LWHSC prepared in this paper is suitable for the harsh seawater environment of offshore oil platforms but is limited to the southern region where there is no requirement for the freeze–thaw resistance of concrete. The results of this study can provide some reference for the application of LWHSC in offshore structures and other similar aspects of engineering.
Influencing Factors of Safety Management System Implementation on Traditional Shipping
Authors: Wahid Ahmad , Jinca Muhammad Yamin , Rachman Taufiqur , Malisan Johny
Published: January 2024

Traditional shipping, often referred to as ‘Pelra’, displays unique characteristics in contrast with conventional ships. This study hypothesizes that the implementation of a Safety Management System (SMS) in Pelra is influenced by both technical and non-technical factors. The primary objective of this research is to ascertain the degree of influence exerted by each factor on the implementation of SMSs in Pelra. Structural Equation Modeling was utilized to analyze expert opinions gathered for this study. The findings indicate that both technical and non-technical factors have direct and interrelated impacts on the safety management of Pelra. Notably, non-technical factors, particularly the authority and responsibility of the company and the ship’s crew, along with technical aspects such as the ship’s construction and stability, emerge as predominant influences. These factors act as control variables, guiding the prioritization of actions in SMS implementation. The insights gained from this research can inform policymakers in developing SMS regulations for traditional ships. A detailed examination of safety issues within the most influential factors is undertaken to reshape policies and program directives, aiming to enhance traditional shipping safety. The adoption of SMSs implies increased responsibilities and associated costs for ship owners and crew, necessitating consensus and commitment among all stakeholders, including the government, ship owners, and crew, for effective SMSs policy implementation in Pelra.
Substantiation of the Risk Neutralization Mechanism in the Financial Security Management of Agricultural Enterprises
Authors: Davydenko Nadiia , Wasilewska Natalia , Titenko Zoya , Wasilewski Mirosław
Published: January 2024

In the context of the Ukrainian economy reforming and ensuring that economic activity is conducted in accordance with current global economic trends, special attention should be paid to solving the problem of neutralizing risks in the financial security management of agricultural enterprises. The purpose of this article is to substantiate the risk neutralization mechanism in the management of financial security for enterprises in the agrarian sector. In writing this article, we used scientific methods such as modeling (to determine the impact of a certain set of factors on the level of enterprises’ financial security), analysis and synthesis (to find out the reasons that cause changes in the studied indicators), tabular and graphical (to present the study results), and abstract and logical (to make theoretical and methodological generalizations). The study results presented in this paper are important for developing offers for neutralizing risks in the financial security management of agricultural enterprises.
Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review
Authors: Al Naqbi Humaid , Bahroun Zied , Ahmed Vian
Published: January 2024

In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI’s evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.
Knowledge Management Practices as an Opportunity for the Achievement of Sustainable Development in Social Enterprises of Medellín (Colombia)
Authors: Marulanda-Grisales Natalia , Herrera-Pulgarín José Julián , Urrego-Marín María Lucelly
Published: January 2024

In the context of the city of Medellín (Colombia), which has been declared a National Science, Technology, and Innovation District, Knowledge Management Practices (KMPs) have been gaining importance because they improve the commercial entrepreneurial ecosystem by articulating tacit and explicit knowledge. The study investigates the role of KMPs in Social Entrepreneurship (SE), and how this relationship generates products and services that meet social needs, with articulation between tacit and explicit knowledge, which start from the experiences of entrepreneurs and join shared interests in ecosystems and public policies of social entrepreneurship. This study employs a non-experimental design based on a survey and a deep interview for 40 SE initiatives; we then developed a Pearson’s bivariate correlation review and a narrative design. The results reveal that SE initiatives aimed at novel market niches and management strategies that articulate multiple sectors and social actors that aim for a practical scope of the purposes of entrepreneurship concerning the 2030 Agenda of the United Nations. The findings of this study suggest that KMPs in SE create a series of perspectives that seek to achieve greater competitiveness and sustainability in front of the market, all from innovative proposals of social value articulated with environmental care.
Energy, Environmental, and Economic Sustainability of Saffron Cultivation: Insights from the First European (Italian) Case Study
Authors: Mehmeti Andi , Candido Vincenzo , Canaj Kledja , Castronuovo Donato , Perniola Michele , D’Antonio Paola , Cardone Loriana
Published: January 2024

Saffron (Crocus sativus L.) stands as a valuable agricultural commodity, witnessing an increasing market inclination toward environmentally sustainable and eco-friendly products. The current literature on the environmental impact and profitability of saffron cultivation is limited, underscoring a notable gap in comprehending the sustainability aspects of this crop. This study utilized a comprehensive multi-model approach to assess the sustainability of annual saffron cultivation, representing the first global detailed evaluation, conducted within a European context (Southern Italy). Energy analysis, physical and monetized life cycle assessment (LCA), and life cycle costing (LCC) were used for a cradle-to-farm gate assessment. One hectare of cultivated saffron, one saffron production yield (stigma, corm, and flower), and 1 kg of stigma yield were used as functional units. The total energy input was 65,073 MJ ha−1, being 33% direct, 67% indirect, 72% renewable, and 28% non-renewable. The majority (55%) of energy is derived from corm production. For 1 kg of saffron the energy efficiency, specific energy, and productivity were 2.98, 4.64 MJ kg−1, and 0.22 kg MJ−1, respectively, while these values dropped significantly for 1 kg of stigma. The multi-indicator LCA analysis using the ReCiPe 2016 model revealed significant contributions to various environmental impact categories. Results align with prior research, pinpointing fertilization and mechanical operations as the primary drivers of diverse environmental impacts. A noticeable carbon intensity was estimated, with a relevant contribution from corm production and human labor, aspects overlooked in previous LCA studies. Saffron cultivation maintains economic viability, with production costs at EUR 98,435 per ha−1 and a net return margin of EUR 172,680 per ha−1, bolstered by the high market price and by-product revenue. Monetization of LCA results revealed that external costs were EUR 15,509 per ha−1, being only 14% of the total cost. Investments in improving yield and resource efficiency have the potential to increase the eco-efficiency of saffron cultivation.
Uncertainly Analysis of Prior Distribution in Accelerated Degradation Testing Bayesian Evaluation Method Based on Deviance Information Criterion
Authors: Zou Tianji , Liu Kai , Wu Wenbo , Wang Ke , Lv Congmin
Published: January 2024

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering.
Water Whiplash in Mediterranean Regions of the World
Authors: Madrigal Citlalli , Bedri Rama , Piechota Thomas , Li Wenzhao , Tootle Glenn , El-Askary Hesham
Published: January 2024

The presence of weather and water whiplash in Mediterranean regions of the world is analyzed using historical streamflow records from 1926 to 2023, depending on the region. Streamflow from the United States (California), Italy, Australia, Chile, and South Africa is analyzed using publicly available databases. Water whiplash—or the rapid shift of wet and dry periods—are compared. Wet and dry periods are defined based on annual deviations from the historical record average, and whiplash occurs when there is an abrupt change that overcomes an accommodated deficit or surplus. Of all the stations, there are more dry years (56%) than wet years (44%) in these regions, along with similarities in the variances and shifts in extremes (i.e., whiplash). On average, 35% of the years were defined as water whiplash years in all countries, with the highest levels in the US (California), where 42–53% of the years were whiplash years. The influence of the El Niño–Southern Oscillation (ENSO) influences Chile and South Africa strongest during the first quarter of the year. This study found that smaller extreme wet periods and larger and less extreme dry periods are prevalent in Mediterranean regions. This has implications for water management as adaptation to climate change is considered.