bluuebunny/crossref_metadata_2025 huggingface dataset:
Religion Counts: Faithful Realism and Historical Representation in George Eliot’s Romola
Authors: Bonfiglio Richard
Published: March 2024

This article explores the importance of faith in the Victorian historical novel, with a particular focus on George Eliot’s Romola (1862–1863), and rethinks past secularist approaches to the genre. Romola was arguably the most meticulously researched historical novel of the nineteenth century. Set in Florence from 1492 to 1498, the novel traces the rise and fall of the Dominican friar, Girolamo Savonarola, as he pursued the spiritual and political reform of the city, leading to his excommunication and martyrdom. Despite the religious setting of the novel, Eliot’s painstaking effort to imagine a realistic historical representation of Florentine society has often been approached in secular terms as a tour de force of the author’s humanist vision of a progressive march towards modernity. Building on recent work in postsecular studies, this essay rethinks the novel’s historical realism in terms of Christian faith. Centering on the spiritual journey of the protagonist, Romola de’ Bardi, the novel presents a faithful depiction of Renaissance Florence by imagining historical representation as an act of faith. Drawing on Jacques Derrida’s essay, “Faith and Knowledge”, the article analyzes how Eliot frames the significance of the novel’s historical representation as an act of faith that one’s life is bound meaningfully to those of others.
Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall
Authors: Nasimi Mitra , Wood Richard L.
Published: March 2024

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.
Spectral Signatures of Macroalgae on Hawaiian Reefs
Authors: Fuller Kimberly , Martin Roberta E. , Asner Gregory P.
Published: March 2024

In Hawaiʻi, native macroalgae or “limu” are of ecological, cultural, and economic value. Invasive algae threaten native macroalgae and coral, which serve a key role in the reef ecosystem. Spectroscopy can be a valuable tool for species discrimination, while simultaneously providing insight into chemical processes occurring within photosynthetic organisms. The spectral identity and separability of Hawaiian macroalgal taxonomic groups and invasive and native macroalgae are poorly known and thus were the focus of this study. A macroalgal spectroscopic library of 30 species and species complexes found in Hawaiʻi was created. Spectral reflectance signatures were aligned with known absorption bands of taxonomic division-specific photosynthetic pigments. Quadratic discriminant analysis was used to explore if taxonomic groups of algae and native versus invasive algae could be classified spectrally. Algae were correctly classified based on taxonomic divisions 96.5% of the time and by species 83.2% of the time. Invasive versus native algae were correctly classified at a rate of 93% and higher, although the number of invasive algal species tested was limited. Analyses suggest that there is promise for the spectral separability of algae investigated in this study by algal taxonomic divisions and native-invasive status. This study created a spectral library that lays the groundwork for testing the spectral mapping of algae using current airborne and forthcoming spaceborne imaging spectroscopy, which could have significant implications for coastal management.
Dual-Channel Transverse Fields Radiofrequency Coils for 1.5 T Magnetic Resonance Imaging
Authors: Giovannetti Giulio , Burov Denis , Alecci Marcello , Rollo Rocco , Galante Angelo
Published: March 2024

This theoretical study presents the design and analytical/numerical optimization of novel dual-channel transverse fields radiofrequency (RF) surface coils for 1.5 T Magnetic Resonance Imaging (MRI). The research explores a planar setup with two channels on a row with aligned spatial orientation of the RF coils, aiming to solve a common design drawback of single-channel transverse field RF coils: the reduced Field Of View (FOV) along the direction of the RF field. A significant challenge in this design is the efficient decoupling of two sets of transverse field RF coils to prevent mutual interference. Our modeling approach integrates thin wire theoretical modeling, magnetostatic computation for strip conductor coils, and their full-wave electromagnetic simulation. Key findings at 64 MHz demonstrate that strategic geometric placement among the two-channel RF coil and the introduction of geometrical asymmetry in the design of the individual RF coils does minimize the mutual inductance, paving the way for effective dual-channel MRI applications. This decoupling approach allows to enhance the FOV, providing a theoretical framework for the development of optimized dual-channel transverse field RF coil configurations. The current design was validated with full-wave numerical study at 64 MHz (1H, 1.5 T), has the potential to be extended at lower or higher frequencies, and the presence of lossy samples needs to be considered in the latter case.
Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods
Authors: Wang Xianxian , Cai Yanchao , Liu Xiucheng , He Cunfu
Published: March 2024

The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction.
Burst-Enhanced Super-Resolution Network (BESR)
Authors: Li Jiaao , Lv Qunbo , Zhang Wenjian , Zhang Yu , Tan Zheng
Published: March 2024

Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leveraging the complementary information between noisy sequences. In this paper, we propose an efficient burst-enhanced super-resolution network (BESR). Specifically, we introduce Geformer, a gate-enhanced transformer, and construct an enhanced CNN-Transformer block (ECTB) by combining convolutions to enhance local perception. ECTB efficiently aggregates intra-frame context and inter-frame correlation information, yielding an enhanced feature representation. Additionally, we leverage reference features to facilitate inter-frame communication, enhancing spatiotemporal coherence among multiple frames. To address the critical processes of inter-frame alignment and feature fusion, we propose optimized pyramid alignment (OPA) and hybrid feature fusion (HFF) modules to capture and utilize complementary information between multiple frames to recover more high-frequency details. Extensive experiments demonstrate that, compared to state-of-the-art methods, BESR achieves higher efficiency and competitively superior reconstruction results. On the synthetic dataset and real-world dataset of BurstSR, our BESR achieves PSNR values of 42.79 dB and 48.86 dB, respectively, outperforming other MFSR models significantly.
Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System
Authors: Fan Shaocan , Deng Zhenmiao
Published: March 2024

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.
Recent Progress on Microfluidics Integrated with Fiber-Optic Sensors for On-Site Detection
Authors: Wang Weibin , Xia Ling , Xiao Xiaohua , Li Gongke
Published: March 2024

This review introduces a micro-integrated device of microfluidics and fiber-optic sensors for on-site detection, which can detect certain or several specific components or their amounts in different samples within a relatively short time. Fiber-optics with micron core diameters can be easily coated and functionalized, thus allowing sensors to be integrated with microfluidics to separate, enrich, and measure samples in a micro-device. Compared to traditional laboratory equipment, this integrated device exhibits natural advantages in size, speed, cost, portability, and operability, making it more suitable for on-site detection. In this review, the various optical detection methods used in this integrated device are introduced, including Raman, ultraviolet–visible, fluorescence, and surface plasmon resonance detections. It also provides a detailed overview of the on-site detection applications of this integrated device for biological analysis, food safety, and environmental monitoring. Lastly, this review addresses the prospects for the future development of microfluidics integrated with fiber-optic sensors.
Deep Reconstruction Transfer Convolutional Neural Network for Rolling Bearing Fault Diagnosis
Authors: Feng Ziwei , Tong Qingbin , Jiang Xuedong , Lu Feiyu , Du Xin , Xu Jianjun , Huo Jingyi
Published: March 2024

Deep transfer learning has been widely used to improve the versatility of models. In the problem of cross-domain fault diagnosis in rolling bearings, most models require that the given data have a similar distribution, which limits the diagnostic effect and generalization of the model. This paper proposes a deep reconstruction transfer convolutional neural network (DRTCNN), which satisfies the domain adaptability of the model under cross-domain conditions. Firstly, the model uses a deep reconstruction convolutional automatic encoder for feature extraction and data reconstruction. Through sharing parameters and unsupervised training, the structural information of target domain samples is effectively used to extract domain-invariant features. Secondly, a new subdomain alignment loss function is introduced to align the subdomain distribution of the source domain and the target domain, which can improve the classification accuracy by reducing the intra-class distance and increasing the inter-class distance. In addition, a label smoothing algorithm considering the credibility of the sample is introduced to train the model classifier to avoid the impact of wrong labels on the training process. Three datasets are used to verify the versatility of the model, and the results show that the model has a high accuracy and stability.
A High-Performance Flexible Hydroacoustic Transducer Based on 1-3 PZT-5A/Silicone Rubber Composite
Authors: Hao Shaohua , Zhong Chao , Wang Likun , Qin Lei
Published: March 2024

In recent years, hydroacoustic transducers made of PZT/epoxy composites have been extensively employed in underwater detection, communication, and recognition for their high energy conversion efficiency. Despite the ease with which these transducers can be formed into complex shapes, their lack of mechanical flexibility limits their versatility across various sizes of underwater vehicles. This study introduces a novel flexible piezoelectric composite hydroacoustic transducer (FPCHT) based on a 1-3 PZT-5A/silicone rubber composite and an island–bridge flexible electrode, which can break the limitations of existing hydroacoustic transducers that do not have flexibility. The finite element method is used to optimize the structural parameters of high-performance 1-3 FPC. A large-sized (187 mm × 47 mm × 5.12 mm) FPC is fabricated using an improved cutting–filling method and packaged into the FPCHT. Compared with the planar rigid PZT/epoxy composite hydroacoustic transducer (RPCHT) of the same size, the TVR (186.5 db) of the FPCHT has increased by about 7 dB, indicating that it has better acoustic radiation performance and electroacoustic conversion efficiency. Furthermore, its electroacoustic performance exhibits excellent stability under different bending states. Therefore, the FPCHT with high electroacoustic performance is an ideal substitute for the existing RPCHT and promotes the development of hydroacoustic transducers towards flexibility and portability.
Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection
Authors: Real Diego , Calvo David , Zornoza Juan de Dios , Manzaneda Mario , Gozzini Rebecca , Ricolfe-Viala Carlos , Lajara Rafael , Albiol Francisco
Published: March 2024

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tube signals serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering the Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as a trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method, is presented.
Excited-State Lifetime of NV Centers for All-Optical Magnetic Field Sensing
Authors: Horsthemke Ludwig , Pogorzelski Jens , Stiegekötter Dennis , Hoffmann Frederik , Langguth Lutz , Staacke Robert , Laube Christian , Knolle Wolfgang , Gregor Markus , Glösekötter Peter
Published: March 2024

We investigate the magnetic field-dependent fluorescence lifetime of microdiamond powder containing a high density of nitrogen-vacancy centers. This constitutes a non-intensity quantity for robust, all-optical magnetic field sensing. We propose a fiber-based setup in which the excitation intensity is modulated in a frequency range up to 100MHz. The change in magnitude and phase of the fluorescence relative to B=0 is recorded where the phase shows a maximum in magnetic contrast of 5.8∘ at 13MHz. A lock-in amplifier-based setup utilizing the change in phase at this frequency shows a 100 times higher immunity to fluctuations in the optical path compared to the intensity-based approach. A noise floor of 20μT/Hz and a shot-noise-limited sensitivity of 0.95μT/Hz were determined.
Secure Computing for Fog-Enabled Industrial IoT
Authors: Alvi Ahmad Naseem , Ali Bakhtiar , Saleh Mohamed Saad , Alkhathami Mohammed , Alsadie Deafallah , Alghamdi Bushra
Published: March 2024

Smart cities are powered by several new technologies to enhance connectivity between devices and develop a network of connected objects which can lead to many smart industrial applications. This network known as the Industrial Internet of Things (IIoT) consists of sensor nodes that have limited computing capacity and are sometimes not able to execute intricate industrial tasks within their stipulated time frame. For faster execution, these tasks are offloaded to nearby fog nodes. Internet access and the diverse nature of network types make IIoT nodes vulnerable and are under serious malicious attacks. Malicious attacks can cause anomalies in the IIoT network by overloading complex tasks, which can compromise the fog processing capabilities. This results in an increased delay of task computation for trustworthy nodes. To improve the task execution capability of the fog computing node, it is important to avoid complex offloaded tasks due to malicious attacks. However, even after avoiding the malicious tasks, if the offloaded tasks are too complex for the fog node to execute, then the fog nodes may struggle to process all legitimate tasks within their stipulated time frame. To address these challenges, the Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT) is proposed for fog nodes. EEOIT proposes a mechanism to detect malicious nodes as well as manage the allocation of computing resources so that IIoT tasks can be completed in the specified time frame. Simulation results demonstrate that EEOIT outperforms other techniques in the literature in an IIoT setting with different task densities. Another significant feature of the proposed EEOIT technique is that it enhances the computation of trustable tasks in the network. The results show that EEOIT entertains more legitimate nodes in executing their offloaded tasks with more executed data, with reduced time and with increased mean trust values as compared to other schemes.
A Road Defect Detection System Using Smartphones
Authors: Kim Gyulim , Kim Seungku
Published: March 2024

We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications.
Influence of Double-Pulse Electrodeposition Parameters on the Performance of Ag/AgCl Electrochemical Electrode for Marine Electric Field
Authors: Wang Chenjuan , Li Yuguo , Wu Yunju
Published: March 2024

This paper describes a Ag/AgCl electrochemical electrode for marine electric field measurements, which was prepared by depositing silver chloride on a silver foil substrate using double-pulse electrodeposition. The impact of positive direction peak current density and deposition time on electrode performance in the preparation of Ag/AgCl electrodes through double-pulse electrodeposition was investigated. Scanning electron microscopy and voltammetry have been used to study the properties of the prepared electrodes. This work reveals the correlation between the electrochemical behavior of electrodes and the physical properties of their surfaces, especially specific surface area and porosity. The study verified the characteristics of Ag/AgCl marine electric field electrodes obtained with different pulse parameters by analyzing the potential stability and noise level of the electrode in salt water. The study’s results have positive significance for improving the accuracy of marine electric field measurements.
The Extraction Mechanism of Zirconium and Hafnium in the MIBK-HSCN System
Authors: Xiong Jing , Li Yang , Zhang Xiaomeng , Wang Yong , Zhang Yanlin , Qi Tao
Published: March 2024

The extraction of zirconium (Zr) and hafnium (Hf) in methyl isobutyl ketone (MIBK)—thiocyanic acid (HSCN) system has been widely used in the production of nuclear-grade zirconium and hafnium in industry, while the extraction mechanism was not adequately studied. In this study, the extraction and stripping equilibrium of Zr and Hf in the MIBK-HSCN system was studied. The results showed that elevated HCl concentration can increase the distribution ratio of SCN− and decrease that of Zr/Hf in organic phase. In the stripping process, HCl concentration and the Organic/Aqueous (O/A) phase ratio played important roles. The mechanism of the extraction reaction was discussed by considering the stoichiometric relationship of possible reaction equations and corresponding equilibrium constants. The results indicated that SCN− could be extracted into MIBK as HSCN·MIBK. Meanwhile, SCN− could also be extracted into MIBK by complexing with metal (Zr or Hf). The molar ratios of MIBK to the complexes of Zr and Hf have been found to be 5.34 and 5.03, respectively. With the increase in the initial concentration of HCl in the aqueous phase, the complexation molar ratios of SCN− to Zr and Hf increased first and then decreased, and so do the extraction equilibrium constants, which might be due to the extraction competition of HSCN and metal complexes.
Exploring the Symbiotic Relationship between Digital Transformation, Infrastructure, Service Delivery, and Governance for Smart Sustainable Cities
Authors: Das Dillip Kumar
Published: March 2024

Infrastructure, service delivery, governance, and digital transformation stand as indispensable cornerstones, playing pivotal roles in the establishment of intelligent and sustainable urban centers. While the extant literature has underscored the significance of each of these elements, their interconnected and symbiotic relationship demands a more profound exploration. Grounded in a systematic review of the existing literature and relevant case studies, this paper explored the intricate interplay between digital transformation, infrastructure development, service delivery, and governance in contemporary society, all in the pursuit of cultivating smart sustainable cities. It contends that by collaboratively working together, these four pillars possess the transformative potential to turn cities into smart and sustainable cities. Digital transformation emerges as the catalyst, propelling innovation and efficiency, while infrastructure forms the bedrock for the seamless delivery of services. Effective governance, in turn, ensures alignment with the evolving needs of citizens. In essence, this study underscores the transformative power of combined action, asserting that the interdependent elements within can transform cities beyond merely having smart or sustainable status to become smart sustainable cities. This paradigm shift harmonizes technological advancements with the foundational goals of sustainable development, steering towards a holistic and inclusive urban future.
Social Justice Profiles: An Exploratory Study towards an Empirically Based Multi-Dimensional Classification of Countries Regarding Fairness of Participation in Higher Education
Authors: Boyadjieva Pepka , Haralampiev Kaloyan , Ilieva-Trichkova Petya
Published: March 2024

The aim of this article is to suggest a better—theoretically and empirically grounded—understanding of the complex character of social justice in higher education. Theoretically, this article conceptualises social justice in higher education as mediating participation in, completion of and outcomes from higher education. It introduces the concept of composite capability for achieving higher education that captures capabilities to participate in, complete and gain outcomes from higher education. This study also develops a methodology for building an empirically based classification of countries regarding social justice in participation in higher education, taking into account the assessed inequality in students’ pathways to higher education as well as inequality in their social conditions, associated with students’ social origin. In so doing, it develops three indices: the index of inequalities in students’ pathways, the index of inequalities in students’ social conditions and the index of participation in higher education. Using microdata from the EUROSTUDENT VII survey (2019–2021) for 12 European countries, it applies the developed methodology to classify countries, for which data are available, by the degree of fairness in participation in higher education. This study’s results demonstrate the social embeddedness of social justice in higher education in different economic and political contexts.
The Public Health Crisis Conceptual Model: Historical Application to the World’s First Nuclear Bomb Test
Authors: Couig Mary Pat , Lavin Roberta , Rogers Heidi Honegger , Nugent Sara Bandish
Published: March 2024

Background/purpose: The Public Health Crisis Conceptual Model was developed to identify and address healthcare and human services needs related to a disaster. The purpose of this study was to historically apply this model to the counties and populations most affected by the first nuclear test in 1945, with a focus on community and local priorities, and to further describe this model and validate its usefulness. If the model had been applied in 1945, what might have been different with respect to research, epidemiological studies, and reparations? Methods: A historical, descriptive case study approach was used, with a focus on community and local priorities. Results: While it was deemed necessary to maintain secrecy surrounding the Trinity test during wartime efforts, scientists and the military knew of the potential dangers of radioactive fallout. However, they neglected to inform exposed New Mexicans after the information about the nature of the bombings of Hiroshima and Nagasaki had been made public. Conclusions: Research and epidemiological studies could have been implemented years before they were. Resources were not and have not been distributed equitably to those exposed to fallout from the Trinity test site. Using the Public Health Crisis Conceptual Model will help ensure that community and local priorities are an integral component of future disaster-related research.
Analysis of Injuries in the Swiss U20 Elite Ice Hockey Season 2019/2020—A Retrospective Survey
Authors: Ruff Jonas , Taeymans Jan , Blasimann Angela , Rogan Slavko
Published: March 2024

(1) Background: In Switzerland, there is little data on injury characteristics in elite ice hockey players aged under 20 years (U20 Elite juniors). This study aimed to determine the injury rate and type of injury in Swiss U20 ice hockey players. (2) Methods: The present study was carried out in a retrospective, non-experimental design using an online questionnaire provided to the 314 elite players of the 12 Swiss U20 Elite ice hockey teams. The injury rate, rate ratios, injury location, type and severity of injury, and injury mechanism were reported. (3) Results: Seventy-three athletes from 11 teams volunteered (response rate = 24%). A total of 30 out of 45 recorded injuries led to time loss in practice and competition. Injury occurred once or twice during the 2019/2020 season. For each player, the injury rate was 0.66 per 1000 practice hours and 2.98 per 1000 competition hours (injury rate ratio = 4.5). The head/neck region was the most common injury location (45.5%). (4) Conclusions: Knowledge of injury characteristics in ice hockey is necessary for meaningful injury management and injury prevention. The results of the present study provide information on the injury rate, location, types, severity, and mechanism in elite Swiss U20 ice hockey players. Most injuries result from contact with another player. More strict sanctioning for irregular behavior and fair play can serve as preventive measures. Further studies should examine different preventive measures such as wearing full-face coverage.
Exercise Combined with a Low-Calorie Diet Improves Body Composition, Attenuates Muscle Mass Loss, and Regulates Appetite in Adult Women with High Body Fat Percentage but Normal BMI
Authors: Wu Xinyue , Zhang Chengnan , Liang Zhuoying , Liang Yiheng , Li Yuxuan , Qiu Junqiang
Published: March 2024

Background: The present study aimed to examine the effects of a 500 kcal reduction in daily energy intake alone and in combination with 90 min of moderate-to-vigorous aerobic exercise per week on body weight, body composition, and appetite sensations in young women with normal BMI and abnormal body fat percentage. Methods: sixty-six young women with normal BMI and abnormal body fat percentage (21.33 ± 1.20 kg/m2 and 34.32 ± 2.94%) were randomly assigned into three groups: (1) caloric restriction (CR; n = 22), (2) caloric restriction with exercise (CR–EX; n = 22), and (3) control (C; n = 22). Data on anthropometry, blood samples, and subjective appetite sensations pre- and post-intervention were collected. Results: After 4 weeks of intervention, CR and CR–EX groups both reduced body weight, fat percentage, and waist and hip circumferences compared to the C group (p < 0.05). Muscle mass of the CR group was significantly lower than that of the C group (−1.21 ± 0.86 kg vs. −0.27 ± 0.82 kg, p < 0.05), and no significant difference between CR–EX and C groups was observed. For appetite sensations, the subjects of the CR group showed significant increases in change of scores in desire to eat and prospective consumption than that of the C group (p < 0.05), while no significant difference between CR–EX and C groups was observed. Conclusion: A 500 kcal reduction in daily energy intake alone and in combination with 90 min of moderate-to-vigorous aerobic exercise per week could both reduce weight and improve body composition in young adult women with normal BMI and abnormal body fat percentage. More importantly, calorie restriction combined with exercise intervention was superior to calorie restriction alone in improving muscle mass loss and regulating appetite sensations.
Design Optimization of a Grid-Tied Hybrid System for a Department at a University with a Dispatch Strategy-Based Assessment
Authors: Ishraque Md. Fatin , Rahman Akhlaqur , Shezan Sk. A. , Shafiullah G. M. , Alenezi Ali H , Hossen Md Delwar , Bintu Noor E Nahid
Published: March 2024

In this research project, the optimal design and design evaluation of a hybrid microgrid based on solar photovoltaics, wind turbines, batteries, and diesel generators were performed. The conventional grid-tied mode was used in addition to dispatch strategy-based control. The study’s test location was the loads in the Electrical, Electronic and Communication Engineering (EECE) department at Pabna University of Science and Technology (PUST), Pabna, Bangladesh. DIgSILENT PowerFactory was employed to determine the power system-based behaviors (electrical power, current, voltage, and frequency) of the proposed hybrid system, while a derivative-free algorithm was used for the expense, optimal size, and emission assessments. While developing the microgrid, load following (LoF) and cycle charging (CyC) control were employed. The microgrid is supposed to have a 23.31 kW peak load requirement. The estimated microgrid’s levelized cost of energy (LE), its net present cost (NC), its operating cost, and its annual harmful gas emissions were estimated in this work. Additionally, since the microgrid is grid-connected, the amount of energy output that might be exported to the grid was also estimated, which will potentially increase during blackouts. The power system responses found in this study ensure that the various microgrid components’ voltage, frequency, current, and power outcomes are steady within the designated range, making the microgrid practical and robust.
The Effect of Corporate Social Responsibility and Sustainable Development Practices on Employer Branding—A Case Study of an International Corporation Operating in Poland
Authors: Andruszkiewicz Katarzyna , Wierzejski Tomasz , Siemiński Marek
Published: March 2024

Corporate social responsibility (CSR) and sustainable development practices and employer branding (EB) significantly influence the effectiveness of recruitment processes, human resource management (HRM), and an employer’s image. The main aim of this study was to assess the effectiveness of internal and external CSR practices in an international corporation operating in Poland. The empirical study consisted of two stages. In the first stage, an in-depth interview was conducted with a group of managers in the examined company. In the second stage, the company’s employees and local residents (non-employees) were surveyed with the use of questionnaires. The study demonstrated that CSR activities addressing environmental issues were most effective. External CSR practices received greater recognition among white-collar (office) employees, whereas blue-collar (manual) employees were more appreciative of internal CSR measures—in particular, financial support schemes. The effectiveness of internal and external CSR practices has been rarely compared in the literature, and the present study was undertaken to fill in this knowledge gap.
Developing a Project-Expectancy Inventory for the Construction Industry from the Owner’s Perspective
Authors: Wei Xin , Zhu Liuying
Published: March 2024

Although current research recognizes the importance of the Expectancy Theory in the construction industry, a standardized project expectancy (PE, hereafter) inventory is still an area for further exploration, especially from the owner’s perspective. This inventory is essential to identify the owner’s expectancy priorities and help select partners aligned with their long-term and sustainable project goals. Based on the Expectancy Theory, a PE inventory is proposed after conducting a comprehensive literature review. It incorporates dimensions like goal difficulty, perceived control, and self-efficacy. The reliability of the inventory is confirmed by analyzing data from 197 construction-project openers through partial least squares structural equation modeling (PLS-SEM, hereafter). The findings indicate that perceived control is the most crucial dimension in PE, followed by self-efficacy and goal difficulty. A tunneling construction megaproject in Southeast China is presented as a case study. It suggests that when selecting partners for construction projects, the project owner should prioritize those amenable to control, thereby enhancing teamwork and collaboration quality. This strategy emphasizes the importance of the owner’s command over project operation, rather than merely focusing on the partners’ capabilities.
The Influence of Strategic Human Resource Management and Artificial Intelligence in Determining Supply Chain Agility and Supply Chain Resilience
Authors: Yamin Mohammad Ali , Almuteri Sultan Dakhilallah , Bogari Khaled Jamil , Ashi Abdulrahim Khaled
Published: March 2024

The aim of this research was to investigate factors that influence logistics firms’ supply chain agility and supply chain resilience. Therefore, an integrated research model based on strategic human resource management and artificial intelligence was developed to determine the agility and resilience of logistics firms. Empirical data were collected from 221 employees working in manufacturing firms in Saudi Arabia. For the data analysis, a structural equation modeling approach was used. The results indicated that joint leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence had substantial explained variance R2 of 80% for supply chain agility. Similarly, an importance performance analysis revealed that, within the integrated research model of supply chain agility, the factors of leadership, human capital development, and organizational flexibility had greater importance in determining supply chain resilience. Practically, this research shows that factors like leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence are positively associated with supply chain agility and, hence, require policymakers’ attention. The value of this research lies in its integration of artificial intelligence, organizational flexibility, and strategic human resource management to explore supply chain agility and its examination of the impact of these factors on supply chain resilience.
The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China
Authors: Zhang Limin , Gu Fei , He Mingke
Published: March 2024

In this era of intense global competition, supply chains are facing challenges in coping with emerging market issues. Within diverse industries worldwide, supply chains are experiencing accelerated reconfiguration, with one of the most notable transformations being the digitalization of supply chain operations. But the literature lacks empirical evidence about how digital transformation effectively contributes to it. Thus, this paper delves into the implications of the supply chain digital transformation (SCDT) and supply chain reconfigurability (SCR) on its overall performance. Cross-sectional data from 379 respondents in the machinery, electronics, and home appliance manufacturing industries were collected through a closed questionnaire. Utilizing a hybrid approach involving the Partial Least Squares Structural Equation Model (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study employs a cross-validation of the conceptual model. Initially, the PLS-SEM is employed to unveil the direct impact of SCDT on supply chain performance (SCP), as well as the intermediary effect of SCR. Building on this foundation, the fsQCA method is further utilized to investigate the configuration paths that enable enterprises to achieve high SCP under the combined influence of SCR and SCDT. The research results affirm the significant influence of SCDT on SCP. Likewise, the findings highlight the crucial intermediary role of SCR between SCDT and SCP. Ultimately, three distinct configurations driving high supply chain performance are identified: technical, management, and flexible configurations, each playing a unique role.
Understanding Customer Opinion Change on Fresh Food E-Commerce Products and Services—Comparative Analysis before and during COVID-19 Pandemic
Authors: Li Yanlai , Shen Zifan , Zhao Cuiming , Chin Kwai-Sang , Lang Xuwei
Published: March 2024

During the coronavirus disease 2019 (COVID-19) pandemic, non-face-to-face e-commerce has become a significant consumer channel for customers to buy fresh food. However, little is known about customer opinion changes in fresh food e-commerce (FFEC) products and services during COVID-19. This study investigated the changes in expectations and preferences of FFEC customers on products and services before and during the pandemic from online reviews through a text mining approach. We divided the pandemic into two phases, acute and recovery, and found that eight attributes affect customers’ opinions. Some logistic service-related attributes gained customer attention during the acute phase, but product-related attributes gained more attention in the recovery phase. Customers showed a great level of forgiveness on many attributes during the acute phase, but customers’ dissatisfaction was expressed during the recovery phase. Finally, the results of the comparative importance–performance analysis provide improvement strategies for FFEC and help optimize their resource allocation of FFEC and enhance sustainable operation capacity in the case of a crisis.
Environmental Education on Sustainable Principles in Kindergartens—A Foundation or an Option?
Authors: Poje Miroslav , Marinić Iva , Stanisavljević Aleksandar , Rechner Dika Iva
Published: March 2024

Preschool education plays an important role because it lays the foundation for a child’s cognitive, social, and emotional development and shapes responsible and informed individuals. Early childhood education teaches values such as sustainability, environmental protection, and social responsibility. By integrating sustainability into preschool education, we are cultivating an environmentally conscious generation that will contribute to a more sustainable and resilient future. The aim of this study was to determine if there are differences in attitudes and knowledge about various aspects of sustainability (such as gardening, plants, food and proper nutrition, and waste recycling) among children attending two different kindergarten educational programs (ecological and language programs). The study was conducted using the F2F-PAPI survey method and included 58 children aged 4 to 6 years. The results showed that children from the ecological program had greater knowledge of sustainability topics than children whose program focused on early foreign language learning (t(44) = 12.542, p = 0.000). Taking into account the desirable diversity of different educational programs in kindergartens, the results suggest that it is necessary to include environmental education to a greater extent as a foundation in the curricula of mandatory regular programs if we are to achieve some of the key sustainable development goals.
Dual-Mobility Cups in Patients Undergoing Primary Total Hip Arthroplasty with Prior Lumbar Spine Fusion: A Systematic Review
Authors: Issa Tariq Ziad , Pearl Adam , Moore Emelia , Maqsood Hannan Ahmad , Saleh Khaled J.
Published: March 2024

Spine and hip abnormalities frequently occur together in most of the orthopedic population; therefore, both of these abnormalities impact the outcomes of the modalities that are being used. Few studies have reported reduced dislocation and revision rates with the use of dual-mobility cups (DMCs) in high-risk lumbar spine fusion (LSF) patients undergoing primary total hip arthroplasty (THA). This study aims to clarify the relationship between pre-existing lumbar spinal fusion and the outcomes of THA with dual-mobility constructs. We systematically reviewed the current literature through several online databases following PRISMA protocol and the Cochrane Handbook for Systematic Reviews of Interventions. We used the methodological index for non-randomized studies (MINORS) to evaluate the methodological quality of the included trials. Four studies examined the feasibility and effectiveness of dual-mobility cups in patients undergoing primary THA with prior LSF. Two studies were conducted in the United States, while the other two originated in Finland and France, respectively. The included studies enrolled 284 patients. Most of these patients had instrumented fusions. Seventy-eight percent of patients received one- or two-level fusions. The average age across the studies was 68.22 and the mean body mass index was 28. No cases of postoperative DMC implant dislocations were identified. The incidence of postoperative complications was 6% (10/173), including deep venous thrombosis, periprosthetic loosening, infection, and fracture, greater trochanteric fracture, and superficial wound infections. Most included studies had some methodological limitations, with an average MINORS score of 10.5 ± 5.8. The use of dual-mobility cups in these high-risk patients undergoing total hip arthroplasty may lead to reduced dislocation rates and postoperative complications. Further long-term follow-up studies are warranted to support these findings.
On q-Hermite Polynomials with Three Variables: Recurrence Relations, q-Differential Equations, Summation and Operational Formulas
Authors: Fadel Mohammed , Raza Nusrat , Du Wei-Shih
Published: March 2024

In the present study, we use several identities from the q-calculus to define the concept of q-Hermite polynomials with three variables and present their associated formalism. Many properties and new results of q-Hermite polynomials of three variables are established, including their generation function, series description, summation equations, recurrence relationships, q-differential formula and operational rules.
Validation of Left Atrial Volume Correction for Single Plane Method on Four-Chamber Cine Cardiac MRI
Authors: Assadi Hosamadin , Sawh Nicholas , Bailey Ciara , Matthews Gareth , Li Rui , Grafton-Clarke Ciaran , Mehmood Zia , Kasmai Bahman , Swoboda Peter P. , Swift Andrew J. , Geest Rob J. van der , Garg Pankaj
Published: March 2024

Background: Left atrial (LA) assessment is an important marker of adverse cardiovascular outcomes. Cardiovascular magnetic resonance (CMR) accurately quantifies LA volume and function based on biplane long-axis imaging. We aimed to validate single-plane-derived LA indices against the biplane method to simplify the post-processing of cine CMR. Methods: In this study, 100 patients from Leeds Teaching Hospitals were used as the derivation cohort. Bias correction for the single plane method was applied and subsequently validated in 79 subjects. Results: There were significant differences between the biplane and single plane mean LA maximum and minimum volumes and LA ejection fraction (EF) (all p < 0.01). After correcting for biases in the validation cohort, significant correlations in all LA indices were observed (0.89 to 0.98). The area under the curve (AUC) for the single plane to predict biplane cutoffs of LA maximum volume ≥ 112 mL was 0.97, LA minimum volume ≥ 44 mL was 0.99, LA stroke volume (SV) ≤ 21 mL was 1, and LA EF ≤ 46% was 1, (all p < 0.001). Conclusions: LA volumetric and functional assessment by the single plane method has a systematic bias compared to the biplane method. After bias correction, single plane LA volume and function are comparable to the biplane method.