bluuebunny/crossref_metadata_2025 huggingface dataset:
Bottlebrush Elastomers with Crystallizable Side Chains: Monolayer-like Structure of Backbones Segregated in Intercrystalline Regions
Authors: Nikitina Evgeniia A. , Dashtimoghadam Erfan , Sheiko Sergei S. , Ivanov Dimitri A.
Published: January 2024

Bottlebrush (BB) elastomers with water-soluble side chains and tissue-mimetic mechanical properties are promising for biomedical applications like tissue implants and drug depots. This work investigates the microstructure and phase transitions of BB elastomers with crystallizable polyethylene oxide (PEO) side chains by real-time synchrotron X-ray scattering. In the melt, the elastomers exhibit the characteristic BB peak corresponding to the backbone-to-backbone correlation. This peak is a distinct feature of BB systems and is observable in small- or medium-angle X-ray scattering curves. In the systems studied, the position of the BB peak ranges from 3.6 to 4.8 nm in BB elastomers. This variation is associated with the degree of polymerization of the polyethylene oxide (PEO) side chains, which ranges from 19 to 40. Upon crystallization of the side chains, the intensity of the peak decays linearly with crystallinity and eventually vanishes due to BB packing disordering within intercrystalline amorphous gaps. This behavior of the bottlebrush peak differs from an earlier study of BBs with poly(ε-caprolactone) side chains, explained by stronger backbone confinement in the case of PEO, a high-crystallinity polymer. Microstructural models based on 1D SAXS correlation function analysis suggest crystalline lamellae of PEO side chains separated by amorphous gaps of monolayer-like BB backbones.
Application of Polymeric CO2 Thickener Polymer-Viscosity-Enhance in Extraction of Low-Permeability Tight Sandstone
Authors: Fu Hong , Song Kaoping , Pan Yiqi , Song Hanxuan , Meng Senyao , Liu Mingxi , Bao Runfei , Hao Hongda , Wang Longxin , Fu Xindong
Published: January 2024

The conventional production technique employed for low-permeability tight reservoirs exhibits limited productivity. To solve the problem, an acetate-type supercritical carbon dioxide (scCO2) thickener, PVE, which contains a large number of microporous structures, was prepared using the atom transfer radical polymerization (ATRP) method. The product exhibited an ability to decrease the minimum miscibility pressure of scCO2 during a solubility test and demonstrated a favorable extraction efficiency in a low-permeability tight core displacement test. At 15 MPa and 70 °C, PVE-scCO2 at a concentration of 0.2% exhibits effective oil recovery rates of 5.61% for the 0.25 mD core and 2.65% for the 5 mD core. The result demonstrates that the incorporation of the thickener PVE can effectively mitigate gas channeling, further improve oil displacement efficiency, and inflict minimal damage to crude oil. The mechanism of thickening was analyzed through molecular simulation. The calculated trend of thickening exhibited excellent agreement with the experimental measurement rule. The simulation results demonstrate that the contact area between the polymer and CO2 increases in direct proportion to both the number of thickener molecules and the viscosity of the system. The study presents an effective strategy for mitigating gas channeling during scCO2 flooding and has a wide application prospect.
Estimation of Digital Porosity of Electrospun Veils by Image Analysis
Authors: Cuahuizo-Huitzil Guadalupe , Olivares-Xometl Octavio , Arellanes-Lozada Paulina , Laguna Cortés José Oscar , Arriola Morales Janette , Santacruz-Vázquez Claudia , Santacruz-Vázquez Verónica
Published: January 2024

The present work reports on an empirical mathematical expression for predicting the digital porosity (DP) of electrospun nanofiber veils, employing emulsions of poly(vinyl alcohol) (PVOH) and olive and orange oils. The electrospun nanofibers were analyzed by scanning electron microscopy (SEM), observing orientation and digital porosity (DP) in the electrospun veils. To determine the DP of the veils, the SEM micrographs were transformed into a binary system, and then the threshold was established, and the nanofiber solid surfaces were emphasized. The relationship between the experimental results and those obtained with the empirical mathematical expression displayed a correlation coefficient (R2) of 0.97 by employing threshold II. The mathematical expression took into account experimental variables such as the nanofiber humidity and emulsion conductivity prior to electrospinning, in addition to the corresponding operation conditions. The results produced with the proposed expression showed that the prediction of the DP of the electrospun veils was feasible with the considered thresholds.
Water-Soluble Star Polymer as a Potential Photoactivated Nanotool for Lysozyme Degradation
Authors: Mezzina Lidia , Nicosia Angelo , Barone Laura , Vento Fabiana , Mineo Placido Giuseppe
Published: January 2024

The development of nanotools for chemical sensing and macromolecular modifications is a new challenge in the biomedical field, with emphasis on artificial peptidases designed to cleave peptide bonds at specific sites. In this landscape, metal porphyrins are attractive due to their ability to form stable complexes with amino acids and to generate reactive oxygen species when irradiated by light of appropriate wavelengths. The issues of hydrophobic behavior and aggregation in aqueous environments of porphyrins can be solved by using its PEGylated derivatives. This work proposes the design of an artificial photo-protease agent based on a PEGylated mercury porphyrin, able to form a stable complex with l-Tryptophan, an amino acid present also in the lysozyme structure (a well-known protein model). The sensing and photodegradation features of PEGylated mercury porphyrin were exploited to detect and degrade both l-Trp and lysozyme using ROS, generated under green (532 nm) and red (650 nm) light lasers. The obtained system (Star3600_Hg) and its behavior as a photo-protease agent were studied by means of several spectroscopies (UV-Vis, fluorescence and circular dichroism), and MALDI-TOF mass spectrometry, showing the cleavage of lysozyme and the appearance of several short-chain residues. The approach of this study paves the way for potential applications in theranostics and targeted bio-medical therapies.
Properties of Heat-Treated Wood Fiber–Polylactic Acid Composite Filaments and 3D-Printed Parts Using Fused Filament Fabrication
Authors: Chien Yu-Chen , Yang Teng-Chun
Published: January 2024

Wood fibers (WFs) were treated at a fixed heat temperature (180 °C) for 2−6 h and added to a polylactic acid (PLA) matrix to produce wood−PLA composite (WPC) filaments. Additionally, the effects of the heat-treated WFs on the physicomechanical properties and impact strength of the WPC filaments and 3D-printed WPC parts using fused filament fabrication (FFF) were examined. The results revealed that heat-treated WFs caused an increase in crystallinity and a significant reduction in the number of pores on the failure cross section of the WPC filament, resulting in a higher tensile modulus and lower elongation at break. Additionally, the printed WPC parts with heat-treated WFs had higher tensile strength and lower water absorption compared to untreated WPC parts. However, most of the mechanical properties and impact strength of 3D-printed WPC parts were not significantly influenced by adding heat-treated WFs. As described above, at the fixed fiber addition amount, adding heat-treated WFs improved the dimensional stability of the WPC parts and it enabled a high retention ratio of mechanical properties and impact strength of the WPC parts.
“But Now I Consydre Thy Necesse”: Augustine’s Doctrine of Jewish Witness and the Restoration of Racial Hierarchies in the Croxton Play of the Sacrament
Authors: Schalski Ella
Published: January 2024

This paper examines the depiction of Jewish and Christian merchants in the medieval English Host miracle play, the Croxton Play of the Sacrament. This play is a critical illustration of religious racialization, effectively demonstrating the perpetuation of anti-Jewish stereotypes and legitimizing violence. Positioned within a broader scholarly debate, particularly in relation to Augustine’s doctrine of Jewish witness, the play portrays Jews as allegorical figures that validate Christian theological constructs. This paper delves into the representation and linguistic depiction of Jewish characters in the play, emphasizing their systematic dehumanization and instrumentalization in Christian narratives. A significant focus is placed on the coerced conversion of Jewish characters, which forces them into the archetype of the “Wandering Jew”, thereby highlighting motifs of symbolic aggression and unending diaspora. This paper also confronts contemporary scholarly perspectives that view the play as challenging religious boundaries, positing that such interpretations overlook the ingrained racialization and marginalization of Jewish identity during the European Middle Ages. It argues that the play’s transient disruption of power dynamics ultimately reinforces prevailing social hierarchies, thereby solidifying deep-seated anti-Jewish sentiments.
Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles
Authors: McCraine Daniel , Samiappan Sathishkumar , Kohler Leon , Sullivan Timo , Will David J.
Published: January 2024

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.
On the 2D Beampattern Optimization of Sparse Group-Constrained Robust Capon Beamforming with Conformal Arrays
Authors: Dai Yan , Sun Chao , Liu Xionghou
Published: January 2024

To overcome the problems of the high sidelobe levels and low computational efficiency of traditional Capon-based beamformers in optimizing the two-dimensional (elevation–azimuth) beampatterns of conformal arrays, in this paper, we propose a robust Capon beamforming method with sparse group constraints that is solved using the alternating-direction method of multipliers (ADMM). A robustness constraint based on worst-case performance optimization (WCPO) is imposed on the standard Capon beamformer (SCB) and then the sparse group constraints are applied to reduce the sidelobe level. The constraints are two sparsity constraints: the group one and the individual one. The former was developed to exploit the sparsity between groups based on the fact that the sidelobe can be divided into several different groups according to spatial regions in two-dimensional beampatterns, rather than different individual points in one-dimensional (azimuth-only) beampatterns. The latter is considered to emphasize the sparsity within groups. To solve the optimization problem, we introduce the ADMM to obtain the closed-form solution iteratively, which requires less computational complexity than the existing methods, such as second-order cone programming (SOCP). Numerical examples show that the proposed method can achieve flexible sidelobe-level control, and it is still effective in the case of steering vector mismatch.
Development of a Large-Range XY-Compliant Micropositioning Stage with Laser-Based Sensing and Active Disturbance Rejection Control
Authors: Kassa Ashenafi Abrham , Shirinzadeh Bijan , Tran Kim Sang , Lai Kai Zhong , Tian Yanling , Qin Yanding , Wei Huaxian
Published: January 2024

This paper presents a novel design and control strategies for a parallel two degrees-of-freedom (DOF) flexure-based micropositioning stage for large-range manipulation applications. The motion-guiding beam utilizes a compound hybrid compliant prismatic joint (CHCPJ) composed of corrugated and leaf flexures, ensuring increased compliance in primary directions and optimal stress distribution with minimal longitudinal length. Additionally, a four-beam parallelogram compliant prismatic joint (4BPCPJ) is used to improve the motion decoupling performance by increasing the off-axis to primary stiffness ratio. The mechanism’s output compliance and dynamic characteristics are analyzed using the compliance matrix method and Lagrange approach, respectively. The accuracy of the analysis is verified through finite element analysis (FEA) simulation. In order to examine the mechanism performance, a laser interferometer-based experimental setup is established. In addition, a linear active disturbance rejection control (LADRC) is developed to enhance the motion quality. Experimental results illustrate that the mechanism has the capability to provide a range of 2.5 mm and a resolution of 0.4 μm in both the X and Y axes. Furthermore, the developed stage has improved trajectory tracking and disturbance rejection capabilities.
Space Robot Sensor Noise Amelioration Using Trajectory Shaping
Authors: Kuck Emily , Sands Timothy
Published: January 2024

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.
Simultaneous Two- and Three-Photon Deep Imaging of Autofluorescence in Bacterial Communities
Authors: Fernández Alma , Classen Anton , Josyula Nityakalyani , Florence James T. , Sokolov Alexei V. , Scully Marlan O. , Straight Paul , Verhoef Aart J.
Published: January 2024

The intrinsic fluorescence of bacterial samples has a proven potential for label-free bacterial characterization, monitoring bacterial metabolic functions, and as a mechanism for tracking the transport of relevant components through vesicles. The reduced scattering and axial confinement of the excitation offered by multiphoton imaging can be used to overcome some of the limitations of single-photon excitation (e.g., scattering and out-of-plane photobleaching) to the imaging of bacterial communities. In this work, we demonstrate in vivo multi-photon microscopy imaging of Streptomyces bacterial communities, based on the excitation of blue endogenous fluorophores, using an ultrafast Yb-fiber laser amplifier. Its parameters, such as the pulse energy, duration, wavelength, and repetition rate, enable in vivo multicolor imaging with a single source through the simultaneous two- and three-photon excitation of different fluorophores. Three-photon excitation at 1040 nm allows fluorophores with blue and green emission spectra to be addressed (and their corresponding ultraviolet and blue single-photon excitation wavelengths, respectively), and two-photon excitation at the same wavelength allows fluorophores with yellow, orange, or red emission spectra to be addressed (and their corresponding green, yellow, and orange single-photon excitation wavelengths). We demonstrate that three-photon excitation allows imaging over a depth range of more than 6 effective attenuation lengths to take place, corresponding to an 800 micrometer depth of imaging, in samples with a high density of fluorescent structures.
Deep Learning-Based Technique for Remote Sensing Image Enhancement Using Multiscale Feature Fusion
Authors: Zhao Ming , Yang Rui , Hu Min , Liu Botao
Published: January 2024

The present study proposes a novel deep-learning model for remote sensing image enhancement. It maintains image details while enhancing brightness in the feature extraction module. An improved hierarchical model named Global Spatial Attention Network (GSA-Net), based on U-Net for image enhancement, is proposed to improve the model’s performance. To circumvent the issue of insufficient sample data, gamma correction is applied to create low-light images, which are then used as training examples. A loss function is constructed using the Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indices. The GSA-Net network and loss function are utilized to restore images obtained via low-light remote sensing. This proposed method was tested on the Northwestern Polytechnical University Very-High-Resolution 10 (NWPU VHR-10) dataset, and its overall superiority was demonstrated in comparison with other state-of-the-art algorithms using various objective assessment indicators, such as PSNR, SSIM, and Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, in high-level visual tasks such as object detection, this novel method provides better remote sensing images with distinct details and higher contrast than the competing methods.
Effects of Contextual Variables on Match Load in a Professional Soccer Team Attending to the Different Season Periods
Authors: Guimarães Rodrigo dos Santos , García-Calvo Tomás , Raya-González Javier , Ponce-Bordón José C. , Fatela Pedro , Lobo-Triviño David
Published: January 2024

This study aimed to analyze the effects of contextual variables (i.e., match location and match outcome) and season periods on match load (i.e., internal and external load) in professional Brazilian soccer players. Thirty-six professional players from the same soccer team participated in this study. The season was split into four phases: matches 1–16 (i.e., Phase 1 = P1); matches 17–32 (i.e., Phase 2 = P2); matches 33–48, (i.e., Phase 3 = P3); matches 49–65 (i.e., Phase 4 = P4). Considering match outcome, when the team wins, Cognitive load, Emotional load, and Affective load were significantly higher in away vs. home matches (p < 0.05). Considering season phases, in P3, Mental Fatigue was significantly higher in drawing than in losing matches (p < 0.05). Additionally, considering the match outcome, when the team lost, Total Distance (TD)/min and TD > 19 km·h−1/min were significantly lower in P1 than P2 (p < 0.001), P3 (p < 0.001), and P4 (p < 0.001). These results suggest to strength and conditioning coaches the need to consider the outcome and location of the previous game when planning the week, as well as the phase of the season they are in to reduce fatigue and injury risk.
A Small Highly Sensitive Glucose Sensor Based on a Glucose Oxidase-Modified U-Shaped Microfiber
Authors: Chen Tingkuo , Jiang Haiming , Xie Kang , Xia Hongyan
Published: January 2024

Diabetes patients need to monitor blood glucose all year round. In this article, a novel scheme is proposed for blood glucose detection. The proposed sensor is based on a U-shaped microfiber prepared using hydrogen-oxygen flame-heating technology, and then 3-aminopropyltriethoxysilane (APTES) and glucose oxidase (GOD) are successively coated on the surface of the U-shaped microfiber via a coating technique. The glucose reacts with the GOD of the sensor surface to produce gluconic acid, which changes the effective refractive index and then shifts the interference wavelength. The structure and morphology of the sensor were characterized via scanning electron microscope (SEM) and confocal laser microscopy (CLM). The experimental results show that the sensitivity of the sensor is as high as 5.73 nm/(mg/mL). Compared with the glucose sensor composed of the same material, the sensitivity of the sensor increased by 329%. The proposed sensor has a broad application prospect in blood glucose detection of diabetic patients due to the advantages of miniaturization, high sensitivity, and good stability.
An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)
Authors: Haouassi Samia , Wu Di
Published: January 2024

Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing model (ADMC2-net)that successfully incorporates both RGB and HSV color spaces to maintain color properties. This model consists of two parallel densely connected sub-models (RGB and HSV) followed by a new efficient attention module. This attention module comprises pixel-attention and channel-attention mechanisms to get more haze-relevant features. Experimental results analyses can validate that our proposed model (ADMC2-net) can achieve superior results on synthetic and real-world datasets and outperform most of state-of-the-art methods.
Enhanced Out-of-Stock Detection in Retail Shelf Images Based on Deep Learning
Authors: Šikić Franko , Kalafatić Zoran , Subašić Marko , Lončarić Sven
Published: January 2024

The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections. To develop the method, we utilized an OOS detection dataset that contains a commonly used fully empty OOS class and a novel class that represents the frontal OOS. We present a new image augmentation procedure in which some existing OOS instances are enlarged by duplicating and mirroring themselves over nearby products. An object-detection model is first pre-trained using only augmented shelf images and, then, fine-tuned on the original data. During the inference, the detected OOS instances are post-processed based on their aspect ratio. In particular, the detected instances are discarded if their aspect ratio is higher than the maximum or lower than the minimum instance aspect ratio found in the dataset. The experimental results showed that the proposed method outperforms the existing DL-based OOS-detection methods and detects fully empty and frontal OOS instances with 86.3% and 83.7% of the average precision, respectively.
Buck Converter with Cubic Static Conversion Ratio
Authors: Botila Delia-Anca , Pop-Calimanu Ioana-Monica , Lascu Dan
Published: January 2024

The paper introduces a step-down converter that exhibits a static conversion ratio of cubic nature, providing an output voltage which is much closer to the input voltage, and at the same duty cycle, compared to a wide class of one-transistor buck-type topologies. Although the proposed topology contains many components, its control is still simple, as it employs only one transistor. A dc analysis is performed, the semiconductor stresses are derived in terms of input and output voltages and output power, revealing that the semiconductor voltage stresses remain acceptable and anyway lower than in other cubic buck topology. All detailed design equations are provided. The state-space approach is used to analyze the converter in the presence of conduction losses and a procedure for calculating the individual power dissipation is provided. The feasibility of the proposed cubic buck topology is first validated by computer simulation and finally confirmed by an experimental 12 V–10 W prototype.
Simple Scalable Multimodal Semantic Segmentation Model
Authors: Zhu Yuchang , Xiao Nanfeng
Published: January 2024

Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a more effective strategy involves leveraging multiple modalities, which is because different sensors of the autonomous driving system bring diverse information, and the complementary features among different modalities enhance the robustness of the semantic segmentation modal. Contrary to the intuitive belief that more modalities lead to better accuracy, our research reveals that adding modalities to traditional semantic segmentation models can sometimes decrease precision. Inspired by the residual thinking concept, we propose a multimodal visual perception model which is capable of maintaining or even improving accuracy with the addition of any modality. Our approach is straightforward, using RGB as the main branch and employing the same feature extraction backbone for other modal branches. The modals score module (MSM) evaluates channel and spatial scores of all modality features, measuring their importance for overall semantic segmentation. Subsequently, the modal branches provide additional features to the RGB main branch through the features complementary module (FCM). Leveraging the residual thinking concept further enhances the feature extraction capabilities of all the branches. Through extensive experiments, we derived several conclusions. The integration of certain modalities into traditional semantic segmentation models tends to result in a decline in segmentation accuracy. In contrast, our proposed simple and scalable multimodal model demonstrates the ability to maintain segmentation precision when accommodating any additional modality. Moreover, our approach surpasses some state-of-the-art multimodal semantic segmentation models. Additionally, we conducted ablation experiments on the proposed model, confirming that the application of the proposed MSM, FCM, and the incorporation of residual thinking contribute significantly to the enhancement of the model.
Relationship of Magnetic Domain and Permeability for Clustered Soft Magnetic Narrow Strips with In-Plane Inclined Magnetization Easy Axis on Distributed Magnetic Field
Authors: Nakai Tomoo
Published: January 2024

A unique functionality was reported for a thin-film soft magnetic strip with a certain angle of inclined magnetic anisotropy. It can switch magnetic domain by applying a surface normal field with a certain distribution on the element. The domain switches between a single domain and a multi-domain. Our previous study shows that this phenomenon appears even in the case of the adjacent configuration of multiple narrow strips. It was also reported that the magnetic permeability for the alternating current (AC) magnetic field changes drastically in the frequency range from 10 kHz to 10 MHz as a function of the strength of the distributed magnetic field. In this paper, the correspondence of AC permeability and the magnetic domain as a function of the intensity of the distributed field is investigated. It was confirmed that the extension of the area of the Landau–Lifshitz-like multi-domain on the clustered narrow strips was observed as a function of the intensity of the distributed magnetic field, and this domain extension was matched with the permeability variation. The result leads to the application of this phenomenon to a tunable inductor, electromagnetic shielding, or a sensor for detecting and memorizing the existence of a distributed magnetic field generated by a magnetic nanoparticle in the vicinity of the sensor.
A Review of IoT Firmware Vulnerabilities and Auditing Techniques
Authors: Bakhshi Taimur , Ghita Bogdan , Kuzminykh Ievgeniia
Published: January 2024

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.
IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop
Authors: Klaina Hicham , Picallo Imanol , Lopez-Iturri Peio , Biurrun Aitor , Alejos Ana V. , Azpilicueta Leyre , Socorro-Leránoz Abián B. , Falcone Francisco
Published: January 2024

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.
A Compact Broadside Coupled Stripline 2-D Beamforming Network and Its Application to a 2-D Beam Scanning Array Antenna Using Panasonic Megtron 6 Substrate
Authors: Temga Jean , Shiba Takashi , Suematsu Noriharu
Published: January 2024

This article presents a 4-way 2-D butler matrix (BM)-based beamforming network (BFN) using a multilayer substrate broadside coupled stripline (BCS). To achieve the characteristics of a compact, wide-bandwidth, high-gain phased array, a BCS coupler is implemented using the Megtron 6 substrate. The compact 2-D BFN is formed by combining planarly two horizontal BCS couplers and two vertical BCS couplers. The BFN is proposed without a crossover and without a phase shifter, generating phase responses of ±90° in the x- and y-directions, respectively. The proposed BFN exhibits a wide operating band of 66.7% (3–7 GHz) and a compact physical area of just 0.25 λ0 × 0.25 λ0 × 0.04 λ0. The planar 2-D BFN is easily integrated with the patch antenna radiation elements to construct a 2-D multibeam array antenna that generates four fixed beams, one in each quadrant, at an elevation angle of 30° from the broadside to the array axis when the element separation is 0.6 λ0. The physical area of the 2-D multibeam array antenna is just 0.8 λ0 × 0.8 λ0 × 0.04 λ0. The prototypes of the BCS coupler, the 2-D BFN, and the 2-D multibeam array antenna were fabricated and measured. The measured and simulated results were in good agreement. A gain of 9.1 to 9.9 dBi was measured.
Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models
Authors: Yoo Joo Hun , Jeong Harim , An Ji Hyun , Chung Tai-Myoung
Published: January 2024

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.
Life-Cycle Assessment in Hydrangea Cultivation in Colombia and Their Cleaner Production Strategies
Authors: González María Alejandra , Arenas Cindy Natalia , Ríos José Adrián , Miranda Juliana , Bello Ana Patricia , Botero Jaime , Betancur Mariluz
Published: January 2024

In the subregion of La Paz Province in eastern Antioquia, Colombia, hydrangea floriculture has boosted economic and social development, generating high value in the territory; however, there are still environmental gaps to be resolved to make it a sustainable activity. This study analyzed some environmental aspects of cultivation based on life-cycle assessment under ISO 14040/14044 standards. The functional unit defined was 1 kg of hydrangea stems grown on a small farm of 0.45 ha, and the limits selected were gate-to-gate. The environmental impacts were evaluated using the CML methodology, Umberto LCA + 10.0.03 software, and the Ecoinvent 3.8 database. The most significant contributors to these impacts were ozone depletion, terrestrial ecotoxicity, and photochemical oxidant formation, which come from the application and pretreatment of chemical fertilizers and pesticides, plant residue generation, and fossil resource materials, such as polymers and fuels. In addition, two cleaner production initiatives were considered: composting plant residues for fertilizer (the use of 100% and 50% composting) and integrated pest management with biological control and natural agents to reduce pesticide use (30% and 50% of chemical pesticides). And the integration of both actions, with 50% composting and 30% substitution of chemical pesticides, was considered. The environmental impacts were reduced by 19.63% and 9.97%, respectively, for composting, 6.62% and 11.03%, respectively, for biological control, and 16.59% for the integration of actions. The two alternatives allowed for improving the crop, contributing to the minimization of environmental impacts, optimizing the use of inputs and fertilizers, and contributing to the sustainable development of floriculture.
Institutional Quality, Trust in Institutions, and Waste Recycling Performance in the EU27
Authors: Pronti Andrea , Zoboli Roberto
Published: January 2024

This paper addresses the role of institutional quality and trust in institutions for the performance in waste recycling of the EU27 countries. While survey-based works have highlighted the role of these factors for waste recycling attitudes and performance at the micro level, econometric analyses of recycling in Europe at country and regional levels have mostly looked into the role of waste policies, and not the role of institutional factors, in driving progresses in waste recycling. This paper tries to fill this gap through a panel econometrics analysis of recycling rates of municipal solid waste at the national level for the EU27 countries for the period 2005–2020. The proxies for institutional quality and trust in institutions, as the variables of interest, are introduced into a model that includes controls on a set of socio-economic variables, and on a set of EU waste policy variables, in particular the Waste Framework Directive (WFD) and its revision, and the first Circular Economy Action Plan of 2015 (CEAP 2015). Our results support the hypothesis that the quality of institutions can influence waste recycling performance. Moreover, our results provide evidence on the negative role of institutional trust on recycling rate at country level. Similarly, we find that the EU WFD and CEAP 2015 have been significant in driving recycling performances; the latter finding, however, is a necessary condition in appropriate institutional and socio-economic environments at the national level.
The Role of Fulfilment and Disillusion in the Relationship between Burnout and Career Satisfaction in Italian Healthcare Workers
Authors: Nonnis Marcello , Agus Mirian , Corona Francesca , Aru Nicola , Urban Antonio , Cortese Claudio Giovanni
Published: January 2024

Healthcare workers’ stress, burnout, and job dissatisfaction were exacerbated by the COVID-19 pandemic, whose mid-term negative effects are still present. The aim of this work was to consider the effects of burnout (psychophysical exhaustion, relational deterioration, and professional inefficacy) on the career satisfaction of healthcare workers. These relationships were evaluated by considering the direct and mediated effects of disillusion. For these purposes, the Link Burnout Questionnaire (LBQ) and the Career Satisfaction Scale (Occupational Stress Indicator—OSI) were administered to 295 healthcare professionals working at three Italian public hospitals in Cagliari, Italy. The analyses were conducted using the Partial Least Square Structural Equation Model (PLS-SEM) method. Outcomes highlighted that healthcare workers were in a condition of work distress, high risk of burnout, and career dissatisfaction. Also recognized was the direct negative effect of burnout dimensions on career satisfaction. Finally, the mediated negative effect of disillusion in the relationship between burnout and career satisfaction was identified. This study underlines the relevance of the dimension of disillusion, and its “bright side” fulfilment, in healthcare workers, specifically referring to the disregarded relationship between burnout and career satisfaction.
Numerical Investigation for the Effect of Joint Persistence on Rock Slope Stability Using a Lattice Spring-Based Synthetic Rock Mass Model
Authors: Al-E’Bayat Mariam , Guner Dogukan , Sherizadeh Taghi , Asadizadeh Mostafa
Published: January 2024

This study underscores the profound influence of rock joints, both persistent and non-persistent with rock bridges, on the stability and behavior of rock masses—a critical consideration for sustainable engineering and natural structures, especially in rock slope stability. Leveraging the lattice spring-based synthetic rock mass (LS-SRM) modeling approach, this research aims to understand the impact of persistent and non-persistent joint parameters on rock slope stability. The Slope Model, a Synthetic Rock Mass (SRM) approach-based code, is used to investigate the joint parameters such as dip angle, spacing, rock bridge length, and trace overlapping. The results show that the mobilizing zones in slopes with non-persistent joints were smaller and shallower compared to slopes with fully persistent joints. The joint dip angle was found to heavily influence the failure mode in rock slopes with non-coplanar rock bridges. Shallow joint dip angles led to tensile failures, whereas steeper joint dip angles resulted in shear-tensile failures. Slopes with wider joint spacings exhibited deeper failure zones and a higher factor of safety, while longer rock bridge lengths enhanced slope stability and led to lower failure zones. The overlapping of joint traces has no apparent impact on slope stability and failure mechanism. This comprehensive analysis contributes valuable insights into sustainable rock engineering practices and the design of resilient structures in natural environments.
Determination of the Occurrence of Negative Impacts during Lowering of Sinking Wells Using the Fuzzy TOPSIS Method
Authors: Dachowski Ryszard , Gałek-Bracha Katarzyna
Published: January 2024

Sinking wells belong to underground facilities. Large-diameter sinking wells are often constructed as retention basins and reservoirs for wastewater treatment plants, which is determined by the Urban Wastewater Treatment Directive. The directive obliges Member States to implement the principles of sustainable development, promoting sustainable sewage collection and treatment systems. When designing and constructing facilities using sinking well technology, contact between the structure and the ground must be considered. During the lowering of a sinking well into the ground medium, a number of negative impacts may occur and affect the sinking process, including excessive ground settlement outside the well, damage or destruction of adjacent objects, tilt of the well casing from the vertical, and uncontrolled sinking of the well casing. The aim of this paper is to determine the occurrence of negative impacts during the lowering of sinking wells. Determining the incidence of negative impacts could help to avoid pre-failure and emergency situations relating to sinking wells. A fuzzy TOPSIS method was used to determine the above.
Cradle-to-Gate Life Cycle Assessment (LCA) of GaN Power Semiconductor Device
Authors: Vauche Laura , Guillemaud Gabin , Lopes Barbosa Joao-Carlos , Di Cioccio Léa
Published: January 2024

Wide Band Gap (WBG) semiconductors have the potential to provide significant improvements in energy efficiency over conventional silicon (Si) semiconductors. While the potential for energy efficiency gains is widely researched, the relation to the energy and resource use during manufacturing processes remains insufficiently studied. In order to appraise the performance of the technology thoroughly, issues such as raw material scarcity, toxicity and environmental impacts need to be investigated in detail. However, sparse Life Cycle Assessment (LCA) data are available for the two currently most widespread WBG semiconductor materials, gallium nitride (GaN or GaN/Si) and silicon carbide (SiC). This paper, for the first time, presents a cradle-to-gate life cycle assessment for a GaN/Si power device. To allow for a full range of indicators, life cycle assessment method EF 3.1 was used to analyze the results. The results identify environmental hotspots associated with different materials and processes: electricity consumption for the processes and clean room facilities, direct emissions of greenhouse gases, gold (when used), and volatile organic chemicals. Finally, we compare this result with publicly available data for Si, GaN and SiC power devices.