bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultra-Wideband and Narrowband Switchable, Bi-Functional Metamaterial Absorber Based on Vanadium Dioxide
Authors: Wang Xiaoyan , Liu Yanfei , Jia Yilin , Su Ningning , Wu Qiannan
Published: July 2023

A switchable ultra-wideband THz absorber based on vanadium dioxide was proposed, which consists of a lowermost gold layer, a PMI dielectric layer, and an insulating and surface vanadium dioxide layer. Based on the phase transition properties of vanadium dioxide, switching performance between ultra-broadband and narrowband can achieve a near-perfect absorption. The constructed model was simulated and analyzed using finite element analysis. Simulations show that the absorption frequency of vanadium dioxide above 90% is between 3.8 THz and 15.6 THz when the vanadium dioxide is in the metallic state. The broadband absorber has an absorption bandwidth of 11.8 THz, is insensitive to TE and TM polarization, and has universal incidence angle insensitivity. When vanadium dioxide is in the insulating state, the narrowband absorber has a Q value as high as 1111 at a frequency of 13.89 THz when the absorption is more excellent than 99%. The absorber proposed in this paper has favorable symmetry properties, excellent TE and TM wave insensitivity, overall incidence angle stability, and the advantages of its small size, ultra-widebands and narrowbands, and elevated Q values. The designed absorber has promising applications in multifunctional devices, electromagnetic cloaking, and optoelectronic switches.
An Amperometric Biosensor Based on a Bilayer of Electrodeposited Graphene Oxide and Co-Crosslinked Tyrosinase for L-Dopa Detection in Untreated Human Plasma
Authors: Cembalo Giuseppa , Ciriello Rosanna , Tesoro Carmen , Guerrieri Antonio , Bianco Giuliana , Lelario Filomena , Acquavia Maria Assunta , Di Capua Angela
Published: July 2023

L-Dopa, a bioactive compound naturally occurring in some Leguminosae plants, is the most effective symptomatic drug treatment for Parkinson’s disease. During disease progression, fluctuations in L-DOPA plasma levels occur, causing motor complications. Sensing devices capable of rapidly monitoring drug levels would allow adjusting L-Dopa dosing, improving therapeutic outcomes. A novel amperometric biosensor for L-Dopa detection is described, based on tyrosinase co-crosslinked onto a graphene oxide layer produced through electrodeposition. Careful optimization of the enzyme immobilization procedure permitted to improve the long-term stability while substantially shortening and simplifying the biosensor fabrication. The effectiveness of the immobilization protocol combined with the enhanced performances of electrodeposited graphene oxide allowed to achieve high sensitivity, wide linear range, and a detection limit of 0.84 μM, suitable for L-Dopa detection within its therapeutic window. Interference from endogenous compounds, tested at concentrations levels typically found in drug-treated patients, was not significant. Ascorbic acid exhibited a tyrosinase inhibitory behavior and was therefore rejected from the enzymatic layer by casting an outer Nafion membrane. The proposed device was applied for L-Dopa detection in human plasma, showing good recoveries.
Epigallocatechin-3-Gallate Therapeutic Potential in Cancer: Mechanism of Action and Clinical Implications
Authors: Kciuk Mateusz , Alam Manzar , Ali Nemat , Rashid Summya , Głowacka Pola , Sundaraj Rajamanikandan , Celik Ismail , Yahya Esam Bashir , Dubey Amit , Zerroug Enfale , Kontek Renata
Published: July 2023

Cellular signaling pathways involved in the maintenance of the equilibrium between cell proliferation and apoptosis have emerged as rational targets that can be exploited in the prevention and treatment of cancer. Epigallocatechin-3-gallate (EGCG) is the most abundant phenolic compound found in green tea. It has been shown to regulate multiple crucial cellular signaling pathways, including those mediated by EGFR, JAK-STAT, MAPKs, NF-κB, PI3K-AKT-mTOR, and others. Deregulation of the abovementioned pathways is involved in the pathophysiology of cancer. It has been demonstrated that EGCG may exert anti-proliferative, anti-inflammatory, and apoptosis-inducing effects or induce epigenetic changes. Furthermore, preclinical and clinical studies suggest that EGCG may be used in the treatment of numerous disorders, including cancer. This review aims to summarize the existing knowledge regarding the biological properties of EGCG, especially in the context of cancer treatment and prophylaxis.
(1-(4-(5-Phenyl-1,3,4-oxadiazol-2-yl)phenyl)-1H-1,2,3-triazol-4-yl)-methylenyls α,ω-Bisfunctionalized 3- and 4-PEG: Synthesis and Photophysical Studies
Authors: Mohammed Mohammed S. , Kovalev Igor S. , Slovesnova Natalya V. , Sadieva Leila K. , Platonov Vadim A. , Kim Grigory A. , Aluru Rammohan , Novikov Alexander S. , Taniya Olga S. , Charushin Valery N.
Published: July 2023

Two new azaheterocycle-based bolas, such as (1-(4-(5-phenyl-1,3,4-oxadiazol-2-yl)phenyl)-1H-1,2,3-triazol-4-yl)-methylenyls α,ω-bisfunctionalized PEGs, were prepared via Cu-catalyzed click reaction between 2-(4-azidophenyl)-5-(aryl)-oxadiazole-1,3,4 and terminal ethynyls derived from PEG-3 and PEG-4. Due to the presence of two heteroaromatic cores and a PEG linker, these bola molecules are considered as promising fluorescent chemosensors for electron-deficient species. As a result of a well-pronounced “turn-off” fluorescence response towards common nitro-explosive components, such as 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT), hard-to-detect pentaerythritol tetranitrate (PETN), as well as Hg2+ cation was observed.
Long COVID, the Brain, Nerves, and Cognitive Function
Authors: Reiss Allison B. , Greene Caitriona , Dayaramani Christopher , Rauchman Steven H. , Stecker Mark M. , De Leon Joshua , Pinkhasov Aaron
Published: July 2023

SARS-CoV-2, a single-stranded RNA coronavirus, causes an illness known as coronavirus disease 2019 (COVID-19). Long-term complications are an increasing issue in patients who have been infected with COVID-19 and may be a result of viral-associated systemic and central nervous system inflammation or may arise from a virus-induced hypercoagulable state. COVID-19 may incite changes in brain function with a wide range of lingering symptoms. Patients often experience fatigue and may note brain fog, sensorimotor symptoms, and sleep disturbances. Prolonged neurological and neuropsychiatric symptoms are prevalent and can interfere substantially in everyday life, leading to a massive public health concern. The mechanistic pathways by which SARS-CoV-2 infection causes neurological sequelae are an important subject of ongoing research. Inflammation- induced blood-brain barrier permeability or viral neuro-invasion and direct nerve damage may be involved. Though the mechanisms are uncertain, the resulting symptoms have been documented from numerous patient reports and studies. This review examines the constellation and spectrum of nervous system symptoms seen in long COVID and incorporates information on the prevalence of these symptoms, contributing factors, and typical course. Although treatment options are generally lacking, potential therapeutic approaches for alleviating symptoms and improving quality of life are explored.
Smart Strategies to Overcome Drug Delivery Challenges in the Musculoskeletal System
Authors: Vorrius Brandon , Qiao Zhen , Ge Jonathan , Chen Qian
Published: July 2023

The musculoskeletal system (MSKS) is composed of specialized connective tissues including bone, muscle, cartilage, tendon, ligament, and their subtypes. The primary function of the MSKS is to provide protection, structure, mobility, and mechanical properties to the body. In the process of fulfilling these functions, the MSKS is subject to wear and tear during aging and after injury and requires subsequent repair. MSKS diseases are a growing burden due to the increasing population age. The World Health Organization estimates that 1.71 billon people suffer from MSKS diseases worldwide. MSKS diseases usually involve various dysfunctions in bones, muscles, and joints, which often result in pain, disability, and a decrease in quality of life. The most common MSKS diseases are osteoporosis (loss of bone), osteoarthritis (loss of cartilage), and sarcopenia (loss of skeletal muscle). Because of the disease burden and the need for treatment, regenerative drug therapies for MSKS disorders are increasingly in demand. However, the difficulty of effective drug delivery in the MSKS has become a bottleneck for developing MSKS therapeutics. The abundance of extracellular matrix and its small pore size in the MSKS present a formidable barrier to drug delivery. Differences of vascularity among various MSKS tissues pose complications for drug delivery. Novel strategies are necessary to achieve successful drug delivery in different tissues composing the MSKS. Those considerations include the route of administration, mechanics of surrounding fluids, and biomolecular interactions, such as the size and charge of the particles and targeting motifs. This review focuses on recent advances in challenges to deliver drugs to each tissue of the MSKS, current strategies of drug delivery, and future ideas of how to overcome drug delivery challenges in the MSKS.
Discovery of Novel Coumarin-Schiff Base Hybrids as Potential Acetylcholinesterase Inhibitors: Design, Synthesis, Enzyme Inhibition, and Computational Studies
Authors: Hasan Aso Hameed , Abdulrahman Faruq Azeez , Obaidullah Ahmad J. , Alotaibi Hadil Faris , Alanazi Mohammed M. , Noamaan Mahmoud A. , Murugesan Sankaranarayanan , Amran Syazwani Itri , Bhat Ajmal R. , Jamalis Joazaizulfazli
Published: July 2023

To discover anti-acetylcholinesterase agents for the treatment of Alzheimer’s disease (AD), a series of novel Schiff base-coumarin hybrids was rationally designed, synthesized successfully, and structurally characterized using Fourier transform infrared (FTIR), Nuclear magnetic resonance (NMR), and High-Resolution Mass Spectrometry (HRMS) analyses. These hybrids were evaluated for their potential inhibitory effect on acetylcholinesterase (AChE). All of them exhibited excellent inhibitory activity against AChE. The IC50 values ranged from 87.84 to 515.59 μg/mL; hybrids 13c and 13d with IC50 values of 0.232 ± 0.011 and 0.190 ± 0.004 µM, respectively, showed the most potent activity as acetylcholinesterase inhibitors (AChEIs). The reference drug, Galantamine, yielded an IC50 of 1.142 ± 0.027 µM. Reactivity descriptors, including chemical potential (μ), chemical hardness (η), electrophilicity (ω), condensed Fukui function, and dual descriptors are calculated at wB97XD/6-311++ G (d,p) to identify reactivity changes of the designed compounds. An in-depth investigation of the natural charge pattern of the studied compounds led to a deep understanding of the important interaction centers between these compounds and the biological receptors of AChE. The molecular electrostatic surface potential (MESP) of the most active site in these derivatives was determined using high-quality information and visualization. Molecular docking analysis was performed to predict binding sites and binding energies. The structure-activity-property relationship studies indicated that the proposed compounds exhibit good oral bioavailability properties. To explore the stability and dynamic behavior of the ligand-receptor complexes, molecular dynamics simulations (MDS) were performed for 100 ns on the two best docked derivatives, 13c and 13d, with the AChE (4EY7) receptor. A popular method for determining the free binding energies (MM/GBSA) is performed using snapshots taken from the systems’ trajectories at 100 ns. These results revealed that the complex system of compound 13d acquired a relatively more stable conformation and exhibited better descriptors than the complex system of compound 13c and the Galantamine drug, suggesting its potential as an effective inhibiting drug. The binding free energy analysis revealed that the 13d-4EY7 complex exhibited greater stability with AChE receptors compared to other complexes.
Budesonide Analogues Preserve Stem Cell Pluripotency and Delay 3D Gastruloid Development
Authors: Amoroso Filomena , Ibello Eduardo , Saracino Federica , Cermola Federica , Ponticelli Giovanna , Scalera Enrica , Ricci Francesca , Villetti Gino , Cobellis Gilda , Minchiotti Gabriella , Patriarca Eduardo Jorge , De Cesare Dario , D’Aniello Cristina
Published: July 2023

Small molecules that can modulate or stabilize cell–cell interactions are valuable tools for investigating the impact of collective cell behavior on various biological processes such as development/morphogenesis, tissue regeneration and cancer progression. Recently, we showed that budesonide, a glucocorticoid widely used as an anti-asthmatic drug, is a potent regulator of stem cell pluripotency. Here we tested the effect of different budesonide derivatives and identified CHD-030498 as a more effective analogue of budesonide. CHD-030498 was able to prevent stem cell pluripotency exit in different cell-based models, including embryonic stem-to-mesenchymal transition, spontaneous differentiation and 3D gastruloid development, and at lower doses compared to budesonide.
Assessing the Measurement Model for Source-Separating Waste for Recycling under a Proposed Smart Waste Management Scheme in Shah Alam, Malaysia
Authors: Bazrbachi Abdullatif , Sidique Shaufique Fahmi , Adam Shehu Usman , Ismail Normaz Wana bt , Sheng Tey Yeong
Published: July 2023

Due to rapid urbanization, solid waste management (SWM) is a major challenge in Malaysia, hence the need to sustainably manage it. Compared with other states, Selangor produces the highest volume of domestic waste. Most of the state’s waste is generated in Shah Alam City. This condition is expected to worsen because the population of Shah Alam is projected to rise by 2.5% from 2018 to 2035. This situation will increase the demand for resources, production, and consumption, increasing the volume of waste generated in Shah Alam. Hence, the pressing necessity to advance from the current traditional waste management practices to a more sustainable SWM system has been identified as a key target in Shah Alam’s 2025–2030 plans. The Smart Waste Management System (SWMS) has been identified as a novel approach to dealing with the absence of route optimization, real-time information exchange, and the consequent increase in waste management costs. All of these elements have characterized the current traditional households’ SWM. However, because this method is novel, there is a dearth of knowledge on the appropriate measurement model for evaluating the dimension of households’ intention to recycle waste through source separation as well as measuring the determinants of such a pro-environmental intention under the new SWMS. Thus, confirmatory factor analysis (CFA) was carried out to verify the factorial structure of the variables, relying on the Theory of Planned Behavior (TPB) based on the structural dimensions identified in prior exploratory factor analysis (EFA). The study found support for the use of TPB as a relevant framework for modeling the intention for source separation and its determinants under SWMS.
The Unknowability and Imagination in Mystical Doctrines of the Late Medieval English Mysticism
Authors: Rathouzská Lucie
Published: July 2023

There are three English authors of the fourteenth century we may call “imaginative mystics” because of their use of imagination in spiritual praxis, i.e., Richard Rolle, Walter Hilton, and the unknown author of the Cloud of Unknowing. However, recently, there has been some criticism expressed regarding these doctrines; in particular, there is a question of whether a spiritual praxis, which includes imaginative images, can keep the principle of the unknowability of God. There is also a question of sensual perception. Imaginative images keep some attributes of sensual perception, such as shape, and they always have some spatiality and temporality. There is a question: how can these images depict the spiritual nature of God and spiritual objects themselves? There is even a possibility that imagination darkens contemplative vision and turns the soul’s attention back to the world. In this paper, I will try to show how these three English authors kept the principle of God’s unknowability and what the role of the imagination in their spiritual praxis is.
Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations
Authors: Tan Zhonghui , Zhao Xianbin , Hu Shensen , Ma Shuo , Wang Li , Wang Xin , Ai Weihua
Published: July 2023

Cloud base height (CBH) is crucial for parameterizing the cloud vertical structure (CVS), but knowledge concerning the temporal and spatial distribution of CBH is still poor owing to the lack of large-scale and continuous CBH observations. Taking advantage of high temporal and spatial resolution observations from the Advanced Himawari Imager (AHI) on board the geostationary Himawari-8 satellite, this study investigated the climatology of CBH by applying a novel CBH retrieval algorithm to AHI observations. We first evaluated the accuracy of the AHI-derived CBH retrievals using the active measurements of CVS from the CloudSat and CALIPSO satellites, and the results indicated that our CBH retrievals for single-layer clouds perform well, with a mean bias of 0.3 ± 1.9 km. Therefore, the CBH climatology was compiled based on AHI-derived CBH retrievals for single-layer clouds for the time period between September 2015 and August 2018. Overall, the distribution of CBH is tightly associated with cloud phase, cloud type, and cloud top height and also exhibits significant geographical distribution and temporal variation. Clouds at low latitudes are generally higher than those at middle and high latitudes, with CBHs peaking in summer and lowest in winter. In addition, the surface type affects the distribution of CBH. The proportion of low clouds over the ocean is larger than that over the land, while high cloud occurs most frequently over the coastal area. Due to periodic changes in environmental conditions, cloud types also undergo significant diurnal changes, resulting in periodic changes in the vertical structure of clouds.
RiSSNet: Contrastive Learning Network with a Relaxed Identity Sampling Strategy for Remote Sensing Image Semantic Segmentation
Authors: Li Haifeng , Jing Wenxuan , Wei Guo , Wu Kai , Su Mingming , Liu Lu , Wu Hao , Li Penglong , Qi Ji
Published: July 2023

Contrastive learning techniques make it possible to pretrain a general model in a self-supervised paradigm using a large number of unlabeled remote sensing images. The core idea is to pull positive samples defined by data augmentation techniques closer together while pushing apart randomly sampled negative samples to serve as supervised learning signals. This strategy is based on the strict identity hypothesis, i.e., positive samples are strictly defined by each (anchor) sample’s own augmentation transformation. However, this leads to the over-instancing of the features learned by the model and the loss of the ability to fully identify ground objects. Therefore, we proposed a relaxed identity hypothesis governing the feature distribution of different instances within the same class of features. The implementation of the relaxed identity hypothesis requires the sampling and discrimination of the relaxed identical samples. In this study, to realize the sampling of relaxed identical samples under the unsupervised learning paradigm, the remote sensing image was used to show that nearby objects often present a large correlation; neighborhood sampling was carried out around the anchor sample; and the similarity between the sampled samples and the anchor samples was defined as the semantic similarity. To achieve sample discrimination under the relaxed identity hypothesis, the feature loss was calculated and reordered for the samples in the relaxed identical sample queue and the anchor samples, and the feature loss between the anchor samples and the sample queue was defined as the feature similarity. Through the sampling and discrimination of the relaxed identical samples, the leap from instance-level features to class-level features was achieved to a certain extent while enhancing the network’s invariant learning of features. We validated the effectiveness of the proposed method on three datasets, and our method achieved the best experimental results on all three datasets compared to six self-supervised methods.
Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis
Authors: Hussain Sarfraz , Mustafa Ghulam , Haider Khan Imran , Liu Jiayuan , Chen Cheng , Hu Bingtao , Chen Min , Ali Iftikhar , Liu Yuhong
Published: July 2023

The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques.
The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data
Authors: Yuan Yuan , Wang Congxiao , Liu Shaoyang , Chen Zuoqi , Ma Xiaolong , Li Wei , Zhang Lingxian , Yu Bailang
Published: July 2023

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.
Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
Authors: Hong Fangzhou , He Guojin , Wang Guizhou , Zhang Zhaoming , Peng Yan
Published: July 2023

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.
Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application
Authors: Liu Lihan , Xu Mengjiao , Wang Zhuwei , Fang Chao , Li Zhensong , Li Meng , Sun Yang , Chen Huamin
Published: July 2023

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader–follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader’s trajectory. This paper studies the leader–follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.
Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings
Authors: Parlato Salvatore , Centracchio Jessica , Esposito Daniele , Bifulco Paolo , Andreozzi Emilio
Published: July 2023

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject’s chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors’ location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R2 > 0.99), a Pearson’s correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.
Communal Organizational Culture as a Source of Business-Success Sustainability in Kibbutz Industry—Two Case Studies
Authors: Moskovich Yaffa
Published: July 2023

This article explored the source of the organizational success of two kibbutz factories. This stood in stark contrast with most kibbutz industries, which abandoned their cooperative and communal attributes and became hierarchical and bureaucratic. This movement away from founding principles was the result of the massive privatization process that the kibbutz movement had been undergoing since the 1990s. This research followed the case study approach, with a comparative analysis of the two kibbutz firms. The author interviewed fifty respondents and supplemented the data with document analysis. The findings in the two factories reflected their ability to assimilate capitalist features into their organizational culture while preserving much of their kibbutz nature, expressed in various cultural features. These factories preferred kibbutz members over outsiders and maintained much of the original organizational democracy and equality among managers and workers. In addition, the firms demonstrated concern for the well-being of all the workers. This mixture of expertise and professionalism, along with internal democracy, equity, and communal concern, could be an example for other factories seeking business success. One important theoretical implication of this research is that an organization whose members identify with their organizational roots can better achieve long-term business success. Finally, this article offers practical implications for managers seeking to design a strong organizational culture.
Visualization and Interpretation of Life Cycle Sustainability Assessment—Existing Tools and Future Development
Authors: Backes Jana Gerta , Steinberg Leonie Sophie , Weniger Alexandra , Traverso Marzia
Published: July 2023

The aim of this study is the evaluation of Life Cycle Sustainability Assessment (LCSA) visualizations, which have been gaining increasing relevance in recent years. Despite this, the final interpretation and visualization of LCSA are not yet sufficiently established. Three of the existing LCSA visualization tools, Life Cycle Sustainability Triangle (LCST), Life Cycle Sustainability Dashboard (LCSD), and Sustainability Crowns, are compared and discussed along previously established target criteria. Subsequently, a “new” visualization tool (LCSA-Wheel) is developed based on analysis results and tested within a case study. It became clear that the LCST and Sustainability Crowns are mainly used to help weigh the sustainability dimensions. Nevertheless, the Sustainability Crowns meet most of the defined target criteria and thus serve as a model for the development of a visualization approach. The LCSD maps a wealth of information but is more difficult to understand without a deeper dive into the topic. The proposed LCSA-Wheel adopts a clear structure and provides information needed to understand the visualization. Although further developments are still necessary for general applicability, there is a justified assumption, shown with the help of a case study, that the LCSA-Wheel will gain acceptance in science and practice and thus drive the use of the LCSA.
Enhancing Single-Phase Grid Integration Capability of PMSG-Based Wind Turbines to Support Grid Operation under Adverse Conditions
Authors: Ali Syed Wajahat , Su Chun-Lien , Verma Anant Kumar , Mellado Claudio Burgos , Gonzalez-Castano Catalina
Published: July 2023

The proposed work delivers a robust control solution for a single-phase permanent magnet synchronous generator-based wind power conversion system (PMSG-WPCS) to enhance grid integration capability. The proposed control approach also offers an extended facility to fulfill low-voltage fault ride-through (LVRT) requirements under adverse grid conditions. Unlike the conventional observer-based PLL (O-PLL) approach, the proposed improved Lyapunov theory-based prefilter (ILP) is helpful in yielding a quadrature signal to solve the single-phase grid synchronization problem. Moreover, the proposed prefilter can leverage delayed signal operation, which improves the harmonic and the DC-offset component rejection abilities while eliminating the need for internal feedback-based submodule blocks for the case of an O-PLL. Consequently, the proposed ILP-PLL exhibits better dynamic behavior to rapidly synchronize a grid-tied power converter and can accurately track the fundamental amplitude information that is required for inverter control to meet the fault ride-through requirements. In addition, the suggested LVRT controller ensures smooth transition between the unity and non-unity power factor modes for superior converter control over reactive current injection into the grid to recover the grid from faults while maintaining a lower amount of total harmonic current distortions. The dynamic performance of the proposed control scheme is experimentally validated in view of the existing O-PLL approach for lower-rating wind-turbine-based PMSG-WPCS.
A Grounded Theory Examination of Supervisory Boards’ Governance Capability Indicators in Publicly Traded Firms: A Sustainability Perspective
Authors: Guo Baorong , Zhao Xinan , Hu Lizhi
Published: July 2023

Embedded within the sustainable development framework, our research leverages proceduralized grounded theory to refine and universalize supervisory governance capabilities, thereby aiming to assess the theoretical saturation of the resultant model and to devise a comprehensive, sustainability-inclusive index of supervisory board governance competence. Focusing on five publicly traded Chinese companies, the research employs a tripartite coding process integral to grounded theory methodologies. By methodically refining case studies, it identifies sustainability-oriented governance capacity indicators. Data are conceptualized and compartmentalized via open coding, then divided into five primary clusters via axial coding, resulting in sustainability-focused governance capacity indicators for supervisory boards. Using selective coding strategies, the study uncovers forty-two competency indicators pertinent to sustainable corporate governance, organized into three domains across eight sustainability-related dimensions. These include individual characteristics, sustainability value judgment, experiential wisdom, collaborative communication for sustainable practices, resource integration, general employment prerequisites, professional application in sustainability, and sustainable business acumen. The findings enhance supervisory board member selection and performance assessment processes, promoting sustainable corporate governance. They also clarify supervisory roles in sustainability, offering a holistic view of supervisory board internal governance mechanisms. By maintaining the objectivity of these indicators, the study advances the field.
Research on Safety Design Strategy of Evacuation Stairs in Deep Underground Station Based on Human Heart Rate and Ascending Evacuation Speed
Authors: Pan Gao , Peng Mingxi , Zhou Tiejun , Wan Zhanzhi , Liang Zheng
Published: July 2023

An effective evacuation staircase safety design strategy is an important measure to ensure the safe evacuation of personnel in deep underground stations, and its design is influenced by human heart rate (HR) and ascending evacuation speed. This study clarifies the relationship between the ascending evacuation speed and human HR in deep underground stations by simulating an emergency situation in a deep underground station and observing individuals evacuating via stairs. A mathematical model of the ascending evacuation speed and HR at different heights is then established. Through the identification and prediction of intelligent safety systems, a safety design strategy for the rest area of evacuation stairs in deep underground stations was proposed. Rest areas of the stairs allow people in a state of fatigue to pause their ascent, preventing tired people from causing congestion and affecting the evacuation of less-tired people. This improves the overall evacuation speed and ensures the safety of life and property.
Experimental Study of Permeable Asphalt Mixture Containing Reclaimed Asphalt Pavement
Authors: Xiao Jingjing , Wang Teng , Hong Jinlong , Ruan Chong , Zhang Yufei , Yuan Dongdong , Wu Wangjie
Published: July 2023

The current focus of research attention on reclaimed asphalt pavement (RAP) utilization is expanding the applications of RAP. This study aims to analyze the road performance of recycled permeable asphalt mixtures (RPAMs), which represents a novel direction for utilizing RAP. Firstly, the Marshall design method was used to carry out the material composition design of the RPAM with varying RAP contents (10%, 20%, and 30%). Subsequently, the performance of the RPAM with different RAP contents (10%, 20%, and 30%) and preheating temperatures (120 °C, 130 °C, 140 °C, 150 °C, and 160 °C) was tested with a permeable asphalt mixture containing 12% high-viscosity asphalt as the control group. The mixture’s performance included high-temperature stability, low-temperature crack resistance, water stability, anti-raveling performance, and dynamic mechanical properties. The results indicate that the higher the RAP content, the better the high-temperature performance of the RPAM, while the low-temperature performance, water stability, and anti-raveling performance deteriorate. At 30% RAP content, its pavement performance is comparable to that of the control group mixture. However, increasing RAP preheating temperature can improve low-temperature and water stability but may reduce high-temperature performance. The optimal RAP preheating temperature for pavement performance is between 140 and 150 °C. The dynamic modulus test showed that the higher the RAP content, the greater the dynamic modulus of the RPAM, leading to better high-temperature stability but reduced low-temperature crack resistance. The influence of RAP preheating temperature is the opposite. These test results demonstrate the feasibility of utilizing RAP for paving permeable asphalt pavement under controlled RAP content and preheating temperature conditions.
Is E-Trust a Driver of Sustainability? An Assessment of Turkish E-Commerce Sector with an Extended Intuitionistic Fuzzy ORESTE Approach
Authors: Sıcakyüz Çiğdem , Erdebilli Babek
Published: July 2023

Due mainly to COVID-19 and the demanding work schedules of many individuals, online purchasing sites have become indispensable. However, the dynamic online environment and everchanging customer demands make sustainable competitiveness challenging for e-commerce platforms. Humans primarily influence the preference for online purchase platforms. This study aimed to discover Türkiye’s top popular online shopping sites by adopting an extended intuitionistic fuzzy ORESTE (Organisation, Rangement Et Synthèse De Données Relationnelles) approach. Our study targeted this by surveying female users of four online shopping platforms using IF-ORESTE. The criteria were determined according to customer preferences. These were as follows: easy accessibility to the platform, providing regular discounts and campaigns, advanced filtering settings, the contractual merchants’ reliability, quick delivery, being more affordable than competing platforms, positive feedback in user comments, having a large brand volume, having an installment option, and having partnered cargo companies. The least important factor was the large volume of brands on the online websites. Quick delivery of orders and positive feedback in reviews were equally important. Similarly, the decision-makers considered regular discounts and promotions and the comprehensive filtering settings as equally critical. However, these criteria were less significant than quick delivery and positive customer feedback. This work’s novelty lies in implementing the IF to the ORESTE in the Turkish e-commerce industry. The implications and future directions are discussed.
The Corporate Economic Influence and Corporate Social Responsibility: Evidence from China
Authors: Guo Lan , Yang Ling
Published: July 2023

This paper uses a panel threshold model to examine the relationship between corporate social responsibility (CSR, hereafter) and enterprises’ economic influence on regional gross domestic product (GDP, hereafter) and employment. We find that there is a threshold effect between CSR and enterprises’ economic influence on regional GDP and employment in different regimes of local GDP and unemployment rates. When local GDP is low, the relationship between CSR and enterprises’ economic influence on regional GDP is significantly negative; however, when local GDP is high, the relationship between the two factors is significantly positive. Meanwhile, firms employing more staff do less CSR when the local unemployment rate is higher. Furthermore, in terms of different government types, the relationship between CSR and corporate influence on regional GDP is negative in predatory and collusive governments, but not in market-leading governments, and the relationship between CSR and corporate influence on regional employment seems insignificant. The findings imply that local leading enterprises exert influence on the social responsibility rules stipulated by local governments by decreasing or increasing regional GDP and regional employment.
Newton-like Polynomial-Coded Distributed Computing for Numerical Stability
Authors: Dai Mingjun , Lai Xiong , Tong Yanli , Li Bingchun
Published: July 2023

For coded distributed computing (CDC), polynomial code is one prevalent encoding method for CDC (called Poly-CDC). It suffers from poor numerical stability due to the Vandermonde matrix serving as the coefficient matrix which needs to be inverted, and whose condition number increases exponentially with the size of the matrix or equivalently with the number of parallel worker nodes. To improve the numerical stability, especially for large networks, we propose a Newton-like polynomial code (NLPC)-based CDC (NLPC-CDC), with a design dedicated for both matrix–vector and matrix–matrix multiplications. The associated proof of the constructed code possesses a (n,k)-symmetrical combination property (CP), where symmetrical means the worker nodes have identical computation volume, CP means the k-symmetrical original computing tasks are encoded into n(n≥k)-symmetrically coded computing tasks, and the arbitrary k resulting from the n-coded computing tasks can recover the intended computing results. Extensive numerical studies verify the significant numerical stability improvement of our proposed NLPC-CDC over Poly-CDC.
Relative Error Linear Combination Forecasting Model Based on Uncertainty Theory
Authors: Shi Hongmei , Wei Lin , Wang Cui , Wang Shuai , Ning Yufu
Published: July 2023

The traditional combination forecasting model has good forecasting effect, but it needs precise historical data. In fact, many random events are uncertain, and much of the data are imprecise; sometimes, historical data are lacking. We need to study combination forecasting problems by means of uncertainty theory. Uncertain least squares estimation is an important technique of uncertain statistics, an important way to deal with imprecise data, and one of the best methods to solve the unknown parameters of uncertain linear regression equations. On the basis of the traditional combination forecasting method and uncertain least squares estimation, this paper proposes two kinds of uncertain combination forecasting models, which are the unary uncertain linear combination forecasting model and the uncertain relative error combination forecasting model, respectively. We set up several piecewise linear regression models according to the data of different periods and, according to certain weights, These piecewise linear regression models are combined into a unary uncertain linear combination forecasting model with a better forecasting effect. The uncertain relative error combination forecasting model is a new forecasting model that combines the traditional relative error linear forecasting model and the uncertain least squares estimation. Compared with the traditional forecasting model, the model can better deal with the forecasting problem of imprecise data. We verify the feasibility of the uncertain combination forecasting model through a numerical example. According to the data analysis, compared with the existing model, the forecasting effect of the proposed model is better.
Plasma Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) as a Possible Biomarker for Severe COVID-19
Authors: Mester Patricia , Amend Pablo , Schmid Stephan , Müller Martina , Buechler Christa , Pavel Vlad
Published: July 2023

Proprotein convertase subtilisin/kexin type 9 (PCSK9) reduces low density lipoprotein (LDL) uptake, leading to increased plasma levels of LDL. In addition, PCSK9 has been implicated in inflammation independently of the effects on cholesterol metabolism. The current analysis showed that our 156 patients with systemic inflammatory response syndrome (SIRS) or sepsis had higher plasma PCSK9 levels in contrast with the 68 healthy controls. COVID-19 sepsis patients had increased plasma PCSK9 levels in comparison to sepsis patients not infected by SARS-CoV-2. For further analysis, patients were divided in two groups based on COVID-19. In both sub-cohorts, plasma PCSK9 levels did not correlate with C-reactive protein, leukocyte count, and procalcitonin. Plasma PCSK9 levels of both patient groups did not significantly differ among SIRS/sepsis patients with and without dialysis and patients with and without ventilation. Furthermore, vasopressor therapy was not significantly associated with altered plasma PCSK9 levels. In the non-COVID-19 SIRS/sepsis group, patients with Gram-negative and Gram-positive infections had similar plasma PCSK9 levels as patients without a detectable pathogen in their blood. In conclusion, the current study suggests PCSK9 as a possible biomarker for COVID-19, but this needs to be validated in larger cohorts.