bluuebunny/crossref_metadata_2025 huggingface dataset:
Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network
Authors: Shi Chongwei , Zhang Qun , Lin Tao , Liu Zhidong , Li Shiliang
Published: January 2023

Micro-motion jamming is a new jamming method to inverse synthetic aperture radar (ISAR) in recent years. Compared with traditional jamming methods, it is more flexible and controllable, and is a great threat to ISAR. The prerequisite of taking relevant anti-jamming measures is to recognize the patterns of micro-motion jamming. In this paper, a method of micro-motion jamming pattern recognition based on complex-valued convolutional neural network (CV-CNN) is proposed. The micro-motion jamming echo signals are serialized and input to the network, and the result of recognition is output. Compared with real-valued convolutional neural network (RV-CNN), it can be found that the proposed method has a higher recognition accuracy rate. Additionally, the recognition accuracy rate is analyzed with different signal-to-noise ratio (SNR) and number of training samples. Simulation results prove the effectiveness of the proposed recognition method.
An Improved Lightweight User Authentication Scheme for the Internet of Medical Things
Authors: Kim Keunok , Ryu Jihyeon , Lee Youngsook , Won Dongho
Published: January 2023

The Internet of Medical Things (IoMT) is used in the medical ecosystem through medical IoT sensors, such as blood glucose, heart rate, temperature, and pulse sensors. To maintain a secure sensor network and a stable IoMT environment, it is important to protect the medical IoT sensors themselves and the patient medical data they collect from various security threats. Medical IoT sensors attached to the patient’s body must be protected from security threats, such as being controlled by unauthorized persons or transmitting erroneous medical data. In IoMT authentication, it is necessary to be sensitive to the following attack techniques. (1) The offline password guessing attack easily predicts a healthcare administrator’s password offline and allows for easy access to the healthcare worker’s account. (2) Privileged-insider attacks executed through impersonation are an easy way for an attacker to gain access to a healthcare administrator’s environment. Recently, previous research proposed a lightweight and anonymity preserving user authentication scheme for IoT-based healthcare. However, this scheme was vulnerable to offline password guessing, impersonation, and privileged insider attacks. These attacks expose not only the patients’ medical data such as blood pressure, pulse, and body temperature but also the patients’ registration number, phone number, and guardian. To overcome these weaknesses, in the present study we propose an improved lightweight user authentication scheme for the Internet of Medical Things (IoMT). In our scheme, the hash function and XOR operation are used for operation in low-spec healthcare IoT sensor. The automatic cryptographic protocol tool ProVerif confirmed the security of the proposed scheme. Finally, we show that the proposed scheme is more secure than other protocols and that it has 266.48% better performance than schemes that have been previously described in other studies.
Semantic-Structure-Aware Multi-Level Information Fusion for Robust Global Orientation Optimization of Autonomous Mobile Robots
Authors: Xiang Guofei , Dian Songyi , Zhao Ning , Wang Guodong
Published: January 2023

Multi-camera-based simultaneous localization and mapping (SLAM) has been widely applied in various mobile robots under uncertain or unknown environments to accomplish tasks autonomously. However, the conventional purely data-driven feature extraction methods cannot utilize the rich semantic information in the environment, which leads to the performance of the SLAM system being susceptible to various interferences. In this work, we present a semantic-aware multi-level information fusion scheme for robust global orientation estimation. Specifically, a visual semantic perception system based on the synthesized surround view image is proposed for the multi-eye surround vision system widely used in mobile robots, which is used to obtain the visual semantic information required for SLAM tasks. The original multi-eye image was first transformed to the synthesized surround view image, and the passable space was extracted with the help of the semantic segmentation network model as a mask for feature extraction; moreover, the hybrid edge information was extracted to effectively eliminate the distorted edges by further using the distortion characteristics of the reverse perspective projection process. Then, the hybrid semantic information was used for robust global orientation estimation; thus, better localization performance was obtained. The experiments on an intelligent vehicle, which was used for automated valet parking both in indoor and outdoor scenes, showed that the proposed hybrid multi-level information fusion method achieved at least a 10-percent improvement in comparison with other edge segmentation methods, the average orientation estimation error being between 1 and 2 degrees, much smaller than other methods, and the trajectory drift value of the proposed method was much smaller than that of other methods.
Experimental Investigation on Hardware and Triggering Effect in Tip-Timing Measurement Uncertainty
Authors: Capponi Lorenzo , Tocci Tommaso , Marrazzo Marco , Marsili Roberto , Rossi Gianluca
Published: January 2023

Non-destructive testing for structural health monitoring is becoming progressively important for gas turbine manufacturers. As several techniques for diagnostics and condition-based maintenance have been developed over the years, the tip-timing approach is one of the preferred approaches for characterizing the dynamic behavior of turbine blades using non-contact probes. This experimental work investigates the uncertainty of the time-of-arrival of a Blade Tip-Timing measurement system, a fundamental requirement for numerical and aeromechanical modeling validation. The study is applied to both the measurement setup and the data processing procedure of a generic commercial measurement system. The influence of electronic components and signal processing on the tip-timing uncertainty is determined under different operating conditions.
A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams
Authors: Grujić Katarina
Published: January 2023

Real-time closed-loop control of metallurgical processes is still in its infancy, mostly based on simple models and limited sensor data and challenged by extreme temperature and harsh process conditions. Contact-free thermal imaging-based measurement approaches thus appear to be particularly suitable for process monitoring. With the potential to generate vast amounts of accurate data in real time and combined with artificial intelligence methods to enable real-time analysis and integration of expert knowledge, thermal spectral imaging is identified as a promising method offering more robust and accurate identification of key parameters, such as surface temperature, morphology, composition, and flow rate.
Variable Baseline and Flexible Configuration Stereo Vision Using Two Aerial Robots
Authors: Sumetheeprasit Borwonpob , Rosales Martinez Ricardo , Paul Hannibal , Ladig Robert , Shimonomura Kazuhiro
Published: January 2023

In this work, a new method for aerial robot remote sensing using stereo vision is proposed. A variable baseline and flexible configuration stereo setup is achieved by separating the left camera and right camera on two separate quadrotor aerial robots. Monocular cameras, one on each aerial robot, are used as a stereo pair, allowing independent adjustment of the pose of the stereo pair. In contrast to conventional stereo vision where two cameras are fixed, having a flexible configuration system allows a large degree of independence in changing the configuration in accordance with various kinds of applications. Larger baselines can be used for stereo vision of farther away targets while using a vertical stereo configuration in tasks where there would be a loss of horizontal overlap caused by a lack of suitable horizontal configuration. Additionally, a method for the practical use of variable baseline stereo vision is introduced, combining multiple point clouds from multiple stereo baselines. Issues from using an inappropriate baseline, such as estimation error induced by insufficient baseline, and occlusions from using too large a baseline can be avoided with this solution.
Time-Domain Numerical Simulation and Experimental Study on Pulsed Eddy Current Inspection of Tubing and Casing
Authors: Yu Xingxing , Zhu Ying , Cao Yan , Xiong Juan
Published: January 2023

Fundamental theory and methods are investigated of inspecting tubing and casing simultaneously using pulsed eddy current testing by numerical simulations and experiments. The distribution and variation of eddy current field are given in the finite element simulation for the inspection of undamaged and corroded casing and tubing combinations, with tubing outer diameter 73.8 mm, wall thickness 5.7 mm, corrosion depth 1.25 mm, 2.5 mm, 3.75 mm, and casing outer diameter 141.5 mm, wall thickness 7.7 mm, corrosion depth 1.25 mm, 2.5 mm, and 3.75 mm, respectively. The results show that eddy current field propagates around and to the depth after the direct section of the exciting current is cut off and the intensity center of eddy current field shifts gradually from the inner side of the tubing to the casing, which forms the basis of analyzing inspection mechanism. Corrosion at a particular depth is related to a particular optimum time slice of the induced voltage (namely with deepest concave) and a highest sensitivity is obtained at this slice. The time associated with this slice is in accordance with the time when the intensity center of eddy current reaches the corrosion. Corrosion at different depths has different voltage time slices starting to show signal of defect, which can be used to estimate the depth of the defect in order to judge the defect coming from tubing or casing. Furthermore, sinking degree of the time slice reflects the size of the defect. All machined defects can be recognized in the experiments and the optimum time slice appears at 0.01 s and 0.008 s after the excitation current is cut off for the tubing corrosion of 1.25 mm and 2.5 mm, respectively. The optimum time slice appears at the last moment of cut-off period, 0.625, for the casing corrosion. Experimental results agree well with the simulations and show the existence of the optimum correspondence between depth of corrosion and starting time of the defect signal of time slice, relations between sinking degree of the time slice, and corrosion size.
Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms
Authors: Patalas-Maliszewska Justyna , Pajak Iwona , Krutz Pascal , Pajak Grzegorz , Rehm Matthias , Schlegel Holger , Dix Martin
Published: January 2023

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity.
Starch-g-Acrylic Acid/Magnetic Nanochitin Self-Healing Ferrogels as Flexible Soft Strain Sensors
Authors: Heidarian Pejman , Kouzani Abbas Z.
Published: January 2023

Mechanically robust ferrogels with high self-healing ability might change the design of soft materials used in strain sensing. Herein, a robust, stretchable, magneto-responsive, notch insensitive, ionic conductive nanochitin ferrogel was fabricated with both autonomous self-healing and needed resilience for strain sensing application without the need for additional irreversible static chemical crosslinks. For this purpose, ferric (III) chloride hexahydrate and ferrous (II) chloride as the iron source were initially co-precipitated to create magnetic nanochitin and the co-precipitation was confirmed by FTIR and microscopic images. After that, the ferrogels were fabricated by graft copolymerisation of acrylic acid-g-starch with a monomer/starch weight ratio of 1.5. Ammonium persulfate and magnetic nanochitin were employed as the initiator and crosslinking/nano-reinforcing agents, respectively. The ensuing magnetic nanochitin ferrogel provided not only the ability to measure strain in real-time under external magnetic actuation but also the ability to heal itself without any external stimulus. The ferrogel may also be used as a stylus for a touch-screen device. Based on our findings, our research has promising implications for the rational design of multifunctional hydrogels, which might be used in applications such as flexible and soft strain sensors, health monitoring, and soft robotics.
Odor Source Localization in Obstacle Regions Using Switching Planning Algorithms with a Switching Framework
Authors: Luong Duc-Nhat , Kurabayashi Daisuke
Published: January 2023

Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot’s balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%.
Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes
Authors: Offermann Julia , Wilkowska Wiktoria , Maidhof Caterina , Ziefle Martina
Published: January 2023

An aged population, increasing care needs, and a lack of (in)formal caregivers represent major challenges for our society today. Addressing these challenges fuels efforts and developments in innovative technologies leading to various existing AAL applications aiming at improving autonomy, independence, and security in older age. Here, the usage of video-based AAL technologies is promising as detailed information can be obtained and analyzed. Simultaneously, this type of technology is strongly connected with privacy concerns due to fears of unauthorized data access or inappropriate use of recorded data potentially resulting in rejection and non-use of the applications. As privacy-preserving visualizations of video data can diminish those concerns, this empirical study examines the acceptance and privacy perceptions of video-based AAL technology applying different visualization modes for privacy preservation (n = 161). These visualization modes differed in their degrees of visibility and identifiability, covering different levels of privacy preservation (low level: “Blurred” mode; medium level: “Pixel” and “Grey” modes; high level: “Avatar” mode) and are specifically evaluated based on realistic video sequences. The results of our study indicate a rather low acceptance of video-based AAL technology in general. From the diverse visualization modes, the “Avatar” mode is most preferred as it is perceived as best suitable to protect and preserve the users’ privacy. Beyond that, distinct clusters of future users were identified differing in their technology evaluation as well as in individual characteristics (i.e., privacy perception, technology commitment). The findings support the understanding of potential users’ needs for a successful future design, development, and implementation of video-based, but still privacy-preserving AAL technology.
Weak Magnetic Internal Signal Characteristics of Pipe Welds under Internal Pressure
Authors: Liu Bin , Fu Yanduo , He Luyao , Geng Hao , Yang Lijian
Published: January 2023

Weak magnetic detection technology is an effective method to identify stress-induced damage to ferromagnetic materials, and it especially possesses great application potential in long-distance oil and gas pipeline weld crack detection. In the process of pipeline operation, due to internal pressure and external loads, local stress concentration may be generated, and partial stress concentration may lead to local cracks and expansion of the pipe. In order to improve the accuracy of magnetic signal analysis for ferromagnetic materials under internal pressure, the causes of magnetic signal generation at pipeline welds were analyzed from a microscopic perspective. The distributions of magnetic signals at pipeline welds, weld cracks, and base metal cracks under different internal pressures were numerically analyzed. The variation trends of magnetic signal characteristics, such as peak values of axial and radial components, gradient K, maximum gradient Kmax, and gradient energy factor S(K), were analyzed. In addition, experiments were carried out to verify the numerical data. It was revealed that with the elevation of internal pressure, the peak values of the axial and radial components, gradient K, maximum gradient Kmax, and gradient energy factor S(K) linearly increased. However, the magnitude and average change of S(K) were larger, which can more directly indicate variations of magnetic signals. The radial growth rate νy of S(K) was 3.24% higher than the axial growth rate νx, demonstrating that the radial component of the magnetic signal was more sensitive to variations of stress. This study provided a theoretical and experimental basis for detection of stress-induced damage to long-distance oil and gas pipelines.
Classification of Liquid Ingress in GFRP Honeycomb Based on One-Dimension Sequential Model Using THz-TDS
Authors: Xu Xiaohui , Huo Wenjun , Li Fei , Zhou Hongbin
Published: January 2023

Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid ingress. Here, we report a fast and automatic classification approach for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures through terahertz time-domain spectroscopy (THz-TDS). We propose an improved one-dimensional convolutional neural network (1D-CNN) model, and compared it with long short-term memory (LSTM) and ordinary 1D-CNN models, which are classification networks based on one dimension sequenced signals. The automated liquid classification results show that the LSTM model has the best performance for the time-domain signals, while the improved 1D-CNN model performed best for the frequency-domain signals.
Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review
Authors: Dhirani Lubna Luxmi , Mukhtiar Noorain , Chowdhry Bhawani Shankar , Newe Thomas
Published: January 2023

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.
Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing
Authors: Huang Yan-Yun , Wang Pi-Chung
Published: January 2023

Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of mobile devices in mobile edge computing by adaptively clustering devices to improve the transmission efficiency. To address the optimization problem in a distributed manner, the decision problem of clustering and computation offloading for mobile devices is formulated as a potential game. We introduce the construction of the potential game and show the existence of Nash equilibrium in the game with a finite enhancement ability. Then, we propose a distributed algorithm of clustering and computation offloading based on game theory. We conducted a simulation to evaluate the proposed algorithm. The numerical results from our simulation show that our algorithm can improve offloading efficiency for mobile devices in mobile edge computing by improving transmission efficiency. By offloading more tasks to edge servers, both the energy efficiency of mobile devices and the responsiveness of computation-intensive applications can be improved simultaneously.
Comparison of Various Smoothness Metrics for Upper Limb Movements in Middle-Aged Healthy Subjects
Authors: Bayle Nicolas , Lempereur Mathieu , Hutin Emilie , Motavasseli Damien , Remy-Neris Olivier , Gracies Jean-Michel , Cornec Gwenaël
Published: January 2023

Backgound: Metrics for movement smoothness include the number of zero-crossings on the acceleration profile (N0C), the log dimensionless jerk (LDLJ), the normalized averaged rectified jerk (NARJ) and the spectral arc length (SPARC). Sensitivity to the handedness and movement type of these four metrics was compared and correlations with other kinematic parameters were explored in healthy subjects. Methods: Thirty-two healthy participants underwent 3D upper limb motion analysis during two sets of pointing movements on each side. They performed forward- and backward-pointing movements at a self-selected speed to a target located ahead at shoulder height and at 90% arm length, with and without a three-second pause between forward and backward movements. Kinematics were collected, and smoothness metrics were computed. Results: LDLJ, NARJ and N0C found backward movements to be smoother, while SPARC found the opposite. Inter- and intra-subject coefficients of variation were lowest for SPARC. LDLJ, NARJ and N0C were correlated with each other and with movement time, unlike SPARC. Conclusion: There are major differences between smoothness metrics measured in the temporal domain (N0C, LDLJ, NARJ), which depend on movement time, and those measured in the frequency domain, the SPARC, which gave results opposite to the other metrics when comparing backward and forward movements.
Prediction of Actual from Climatic Precipitation with Data Collected from Northern Poland: A Statistical Approach
Authors: Barańczuk Jacek , Zeleňáková Martina , Abd-Elhamid Hany F. , Barańczuk Katarzyna , Gharbia Salem S. , Blišťan Peter , Meulenberg Cécil J. W. , Kumer Peter , Golus Włodzimierz , Markowski Maciej
Published: January 2023

Water is a basic element of the natural environment and the most important component in human water management. Rainfall is the main source of water. Therefore, determining the amount of precipitation reaching the ground using sensors is crucial information. Precise precipitation data are necessary for better modeling quality, as the observation data from weather stations are used as basics for weather model assessment. The authors compared precipitation from the Hellmann rain gauge (climatic precipitation, 1.0 m above the ground surface) measured throughout the year and the GGI 3000 rain gauge (actual precipitation on the ground level) measured from April to October. Measurement sequences from the years 2011–2020 were considered. The data for analysis were obtained from a weather station located in northern Poland. The authors analyzed the relationships between data from the two sensors. A comparative study showed that the measurements of actual precipitation are higher and there are strong relationships between actual and climatic rainfall (r = 0.99). Using the introduced coefficient it is possible to determine the full–year actual precipitation with high probability, taking into account the precipitation with a correction from the winter half-year and the actual precipitation from the summer half-year, which is of great importance in the calculation of the water balance.
Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure
Authors: Wallace Lance , Ott Wayne
Published: January 2023

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.
Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction
Authors: Dritsas Elias , Trigka Maria
Published: January 2023

Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.
Acknowledgment to the Reviewers of Seeds in 2022
Authors:   Seeds Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Contemporary European Welfare State Transformations and the Risk of Erosion of Social Rights: A Normative Analysis of the Social Investment Approach
Authors: Busilacchi Gianluca , Giovanola Benedetta
Published: January 2023

Over the last decades, there has been a huge debate on the transformations of the European Welfare State. The issue of its financial sustainability together with the emergence of new social risks has put under pressure the traditional model of social protection and created the conditions for a change in the gist of the welfare state provisions. In this context, the social investment approach has become an emerging reference paradigm to tackle new social risks and meet the need to recalibrate the European welfare state and ensure its economic sustainability. However, despite this success, social investment still seems to be a rather ambiguous concept, too vague to result in precise and univocal policy prescriptions and open to the risk of a stretch of its interpretation by neoliberal politics, to erode social rights. In this paper we propose a theoretical framework to better clarify the normative ground, the moral foundation and political justification of the social investment approach and to understand whether it can avoid the risk of the erosion of social rights.
Acknowledgment to the Reviewers of Social Sciences in 2022
Authors: Social Sciences Editorial Office Social Sciences Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Effect of Increasing the Number of Substitutions on Physical Performance during Periods of Congested Fixtures in Football
Authors: García-Aliaga Abraham , Martín-Castellanos Adrián , Marquina Nieto Moisés , Muriarte Solana Diego , Resta Ricardo , López del Campo Roberto , Mon-López Daniel , Refoyo Ignacio
Published: January 2023

(I) This study aimed to evaluate the impact on physical demands induced by FIFA’s new rule implemented based on the number of substitutions caused by COVID-19. (II) Sixty-six matches were analysed in peak periods (microcycles of three matches in a week) in the competition period before and after the pandemic. The variables collected were organised by team (22 from LaLigaTM SmartBank 2019–2020) for a total of 132 team records and 1077 player performance reports using a multi-camera tracking system and Mediacoach® software. Physical performance variables were analysed in the first half, second half and whole match, thus determining the individual and collective performances of the team. (III) This study shows how, despite the increase in substitutions allowed with the new rule, physical performance increased in some variables in the congested periods (e.g., total distance run and distance run in the first and second halves). Additionally, the players’ physical performance involved in a substitution was greater than it was for players who completed the game. (IV) The new substitution rule helps to maintain and even improve physical performance. This measure could improve intensity levels in both individual and team performance. It could even safeguard the physical integrity of the players by reducing the risk of injury, as fewer players have to play the full match.
An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems
Authors: Ahmadianfar Iman , Halder Bijay , Heddam Salim , Goliatt Leonardo , Tan Mou Leong , Sa’adi Zulfaqar , Al-Khafaji Zainab , Homod Raad Z. , Rashid Tarik A. , Yaseen Zaher Mundher
Published: January 2023

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems.
Employer Branding in the Fashion Industry: CSR Actions by Fashion SMEs
Authors: Szegedi Krisztina , Németh Tamás , Körtvési Dorina
Published: January 2023

Little is known about SMEs’ perceptions of CSR, sustainability, and business ethics, particularly in the fashion industry. We have even less information on the relationship between SMEs’ CSR actions and employer branding. This important knowledge gap is addressed in this study. We intend to focus on how small and medium-sized enterprises that are operating and considered sustainable in the fashion industry interpret the concept of sustainability, corporate social responsibility (CSR), and business ethics, which CSR elements appear in relation to employees, and how they contribute to employer branding. In the course of our qualitative research, we conducted semistructured, in-depth interviews with the owners and managers of 10 European businesses, bearing sustainability in mind. Our results show that the organisational culture and the reputation perceived by a wide range of stakeholders are the most essential elements of employer branding, which promotes employees’ commitment to sustainable fashion enterprises.
Research on Industrial and Commercial User-Side Energy Storage Planning Considering Uncertainty and Multi-Market Joint Operation
Authors: Wang Xuejie , Zhao Huiru , Xie Guanglong , Lin Keyao , Hong Juhua
Published: January 2023

With the continuous development of the Energy Internet, the demand for distributed energy storage is increasing. However, industrial and commercial users consume a large amount of electricity and have high requirements for energy quality; therefore, it is necessary to configure distributed energy storage. Based on this, a planning model of industrial and commercial user-side energy storage considering uncertainty and multi-market joint operation is proposed. Firstly, the total cost of the user-side energy storage system in the whole life cycle is taken as the upper-layer objective function, including investment cost, operation, and maintenance cost. The lower layer takes the economy and environment of energy storage operation as the goal, and considers the ancillary service market revenue, demand response constraints, and operational constraints. Secondly, considering the uncertainty of the power market price, and based on the robust optimization theory, the robust transformation is carried out to effectively deal with the impact of uncertain variables on the system operation. Finally, the model is verified in a typical IEEE 30-node system. The results show that the uncertainty of renewable energy will affect the optimal location and capacity of energy storage. From the results of energy storage location, energy storage will be configured in the important transmission nodes and renewable energy power generation access nodes in the power system.
Long-Term Variability of Vegetation Cover and Its Driving Factors and Effects over the Zuli River Basin in Northwest China
Authors: Huang Chenlu , Xu Juan , Shan Linxin
Published: January 2023

Vegetation information is a critical factor in regional environment management under climate change. In this study, a typical arid and semi-arid watershed on the Loess Plateau, the Zu Li River Basin (ZRB), was selected to study the long-term changes in vegetation cover and its drivers and impacts. Unlike existing normalized vegetation index (NDVI) products, which have coarse spatial resolution and short time horizons, this study used the 30 m Landsat dataset analyzed in the Google Earth Engine (GEE) to generate high-resolution and long-term NDVI data, which are the most ideal for monitoring vegetation dynamics using long-time-series data products. The results showed that the annual mean maximum NDVI (normalized vegetation index) in the ZRB increased during 1987–2021, with a significant (p < 0.05) increasing trend in most areas. Upstream vegetation cover increased more than midstream and downstream, but the increase was smaller. Precipitation in the ZRB area was significantly (p < 0.05) correlated with the NDVI series, except for the upstream pass area, where human activities played an important role. NDVI was significantly (p < 0.05) negatively correlated with runoff coefficient and sand content, indicating that vegetation cover was an important reason for the decrease in runoff coefficient and sand content.
Why International Conciliation Can Resolve Maritime Disputes: A Study Based on the Jan Mayen Case
Authors: Dong Limin
Published: January 2023

The settlement of maritime disputes is an important issue faced by many countries. Choosing an appropriate settlement method is the primary task of policy makers. International conciliation, which results from careful and systematic evaluation, could become the primary choice of dispute settlement. During conciliation, parties have dominant and final decision-making power over the settlement of disputes, and there is flexibility in the application of law and procedural rules. The Conciliation Commission provides independent third-party advice, and the political and time costs of dispute settlements are relatively low. These are core advantages of conciliation that attract the attention of decision makers. The willingness and diplomatic relations of disputing parties, existence of external pressure, economization of delimitation, and capacity of the Commission are key factors that affect the success of conciliation. The roles these factors play depend on their controllability and the strength of the disputing parties. The effects of dispute settlement with the assistance of the Conciliation Commission are systemic. The successful settlement of maritime disputes between Norway and Iceland in the context of the Cold War not only demonstrated that conciliation is of great value in resolving maritime disputes and promoting inter-state relations but also had considerable uniqueness. Many questions regarding international conciliation cannot be clearly answered by the Jan Mayen Case. Increased state practice and further in-depth research are needed.
What Are ILK in Relation to Science? Using the ‘Ethic of Equivocation’ to Co-Produce New Knowledge for Conservation
Authors: Root-Bernstein Meredith , du Plessis Pierre , Guerrero-Gatica Matías , Narayan Trupthi , Roturier Samuel , Wheeler Helen C.
Published: January 2023

Attention to epistemological relationships between Indigenous and local knowledges (ILK) and conservation science is increasing. Some approaches for doing so have been developed, but in general, serious engagement between ILK and science still feels experimental and does not have broad uptake. Here we address some of the main issues that arise when considering the epistemological relationships between ILK and conservation or ecological science. A key issue is the relationship between ILK and science that sets the expectations for how they may form dialogues, collaborations, or co-production. After reviewing several perspectives on this relationship, and their limitations, we suggest that a focus on shared meanings within an ‘ethic of equivocation’ is a productive path forward. In an ethic of equivocation, neither ILK nor science validates the other, yet meaning can be created. We explain and develop the concepts of meanings and equivocation in the ILK-science context. We, thus, argue for a broad and rich understanding of the joint roles of ILK and science, which goes beyond treating ILK as a data source. We argue that diverse styles of reasoning exist in science, and scientists may already treat observations, models, and collaborations in the ways that we discuss, providing ready-made analogies for thinking about ILK. We also discuss how to avoid abuses of power while engaging with ILK to co-produce new knowledge.
Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis
Authors: Hu Rong , Lau Yui-Yip , Wang Ruiqian
Published: January 2023

In order to improve the degree of security and facilitation of the business environment; customs administrations are constantly working to strengthen their own institutional innovation and governance in customs control. As such, this paper establishes an evaluation index of international customs supervision competitiveness based on the eight indexes extracted from the World Customs Organisation (WCO) Revised Kyoto Convention and selects 21 representative national customs using the principal component analysis (PCA) method to assess their competitiveness against SPSSAU quantitatively. Based on the data from the World Economic Forum, World Bank, OECD, WCO Annual Report, and Transparency International, the Dutch customs have relatively the best performance in the range of comprehensive competitiveness, and customs authorities in Germany, New Zealand, the United Kingdom, the United States, Mexico, Australia, the Netherlands, and Singapore also have relatively-best performance under different indexes. Taking China Customs as an example, the gaps between China Customs and the ones with the best performance are also analyzed. In response to the problems identified by the analysis, recommendations are made in the areas of process facilitation, technology application, international cooperation, economic development, taxation management, and capacity building to improve the competitiveness of customs control.
Characterizing Harbor Dredged Sediment for Sustainable Reuse as Construction Material
Authors: Bortali Meryem , Rabouli Mohamed , Yessari Madiha , Hajjaji Abdelowahed
Published: January 2023

An unprecedented rate of construction has profoundly increased the risk of scarcity of natural resources and threatened ecosystem sustainability. To establish an effective sustainable development policy, it is imperative to promote the use of responsible production channels, including waste recycling. Reuse of harbor dredged sediment is commonly investigated as a valuable alternative to non-renewable natural resources needed for construction. Sediment characterization is decisive in the valorization process, aiming to identify potential recycling paths. Existing research efforts, however, have rarely investigated case studies in developing countries. Moreover, they have tended to focus on the technical aspects, ignoring economic feasibility, which carries important implications. This paper fills this gap first by meticulously selecting laboratory tests for characterization within the means available in developing countries and second by conducting a cost-benefit analysis. The port of Safi, Morocco, was chosen for the implementation of the adopted approach. Results showed that dredged sediment is a sand readily reusable as a construction aggregate. Several applications are possible, the most interesting one being concrete works, as a substitute for conventional sand. While treatment by washing and dehydrating proved necessary, cost-benefit analysis confirmed the profitability of recycling. Hence, beneficial reuse of dredged sediment as construction material is technically and economically feasible.
Evaluation of Sustainable Rural Tourism Development with an Integrated Approach Using MDS and ANP Methods: Case Study in Ciamis, West Java, Indonesia
Authors: Saputro Ketut Eko Ari , Hasim  , Karlinasari Lina , Beik Irfan Syauqi
Published: January 2023

Indonesia is an agricultural country, and most of its territory is in the form of villages that have the potential to be developed as rural tourism centers. One area that has the potential to be developed into rural tourism centers is Panjalu, Indonesia. However, the sustainable development of rural tourism in Panjalu, Indonesia, still needs to be improved, so the community cannot enjoy the benefits of this rural tourism. This study aimed to analyze the sustainability strategy of rural tourism development in Panjalu, Ciamis, Indonesia. This study combined two methods, consisting of the Multi-Dimensional Scaling (MDS) and the Analytic Network Process (ANP). The MDS results show that rural tourism sustainability in Panjalu, Indonesia is included in the sufficient category, with a sustainability index of 57,998; 66,278; and 51,862, for ecological, socio-cultural and economic criteria, respectively. The MDS also analyzes which attributes are sensitive to the sustainability status. The most sensitive attributes for each criterion include Biodiversity for ecological criteria (RMS 2.224), Local Community Involvement for the social-culture criteria (1.147), and Livelihood Diversification for the economic criteria (1.245). These most sensitive attributes are, then, used as a reference for developing sustainable rural tourism development strategies. As a result, the strategic priority ranking for rural tourism in Panjalu, Indonesia is rural tourism based on culture and community development.