bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Low-Cost Differential Isolated Probe for Voltage Measurements
Authors: Antolín-Cañada Diego , Perez-Cebolla Francisco Jose , Eneriz Daniel , Calvo Belén , Medrano Nicolás
Published: January 2024

The growing development of communication technologies has given rise to the Internet of Things, which has led to the emergence of new cities, smart grids, and smart buildings, and the development of energy generation using renewable sources, as well as the emergence of new electrical loads such as the electric car. These advances give rise to the need for new media devices with remote communication, and require a greater control and monitoring of the state of the electrical grid in order to verify its correct state, as well as the detection of faults or alterations that are occurring in it due to these new generation systems or new loads. These remote, unsupervised measurement devices require galvanic isolation to protect the measurement and communication system, so that even if there is a break in the isolation, the integrity of the measurement and communication system is maintained. In addition, as it is a device prepared for multipoint measurement, the cost of the probe must be contained. This article details the design, implementation, and validation of a low-cost remote isolated differential voltage probe. This probe is intended for monitoring at network supply points, as well as for the verification of the European standard EN 50160 as a means of detecting disturbances in network behaviour. Its characteristics as a differential and isolated probe provide it with the possibility of floating voltage averaging, guaranteeing the integrity of the electronics of the low-voltage probe, i.e., the digitalisation and communication system. The measurements collected are sent via an MQTT protocol, which makes the remote probe a device compatible with the Internet of Energy. For the validation of the probe, a full functional test is performed, including FFT spectral analysis to verify the compliance of the mains voltage with the aforementioned European standard EN 50160.
MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
Authors: Koo Minseok , Park Jaesung
Published: January 2024

Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay.
Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation
Authors: Tran Vu , Septier François , Murakami Daisuke , Matsui Tomoko
Published: January 2024

Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain adaptation for daily maximum temperature forecasting in experimental low-resource settings. We used an attention-based transformer deep learning architecture as the core forecasting framework and used kernel mean matching (KMM) for domain adaptation. Domain adaptation significantly improved forecasting accuracy in most experimental settings, thereby mitigating domain differences between source and target regions. Specifically, we observed that domain adaptation is more effective than exclusively training on a small amount of target-domain training data. This study reinforces the potential of using DNNs for temperature forecasting and underscores the benefits of domain adaptation using KMM. It also highlights the need for caution when using small amounts of target-domain data to avoid overfitting. Future research includes investigating strategies to minimize overfitting and to further probe the effect of various factors on model performance.
Neurofilament Light Protein Rod Domain Exhibits Structural Heterogeneity
Authors: Nefedova Victoria V. , Kleymenov Sergey Y. , Safenkova Irina V. , Levitsky Dmitrii I. , Matyushenko Alexander M.
Published: January 2024

Neurofilaments are neuron-specific proteins that belong to the intermediate filament (IFs) protein family, with the neurofilament light chain protein (NFL) being the most abundant. The IFs structure typically includes a central coiled-coil rod domain comprised of coils 1A, 1B, and 2, separated by linker regions. The thermal stability of the IF molecule plays a crucial role in its ability for self-association. In the current study, we investigated the thermal stability of NFL coiled-coil domains by analyzing a set of recombinant domains and their fusions (NFL1B, NFL1A+1B, NFL2, NFL1B+2, and NFLROD) via circular dichroism spectroscopy and differential scanning calorimetry. The thermal stability of coiled-coil domains is evident in a wide range of temperatures, and thermal transition values (Tm) correspond well between isolated coiled-coil domains and full-length NFL. NFL1B has a Tm of 39.4 °C, and its’ fusions, NFL1A+1B and NFL1B+2, have a Tm of 41.9 °C and 41.5 °C, respectively. However, in the case of NFL2, thermal denaturation includes at least two thermal transitions at 37.2 °C and 62.7 °C. These data indicate that the continuous α-helical structure of the coil 2 domain has parts with varied thermal stability. Among all the NFL fragments, only NFL2 underwent irreversible heat-induced denaturation. Together, these results unveil the origin of full-length NFL’s thermal transitions, and reveal its domains structure and properties.
How Information Framing Nudges Acceptance of China’s Delayed Retirement Policy: A Moderated Mediation Model of Anchoring Effects and Perceived Fairness
Authors: Zeng Weixi , Zhao Lixia , Zhao Wenlong , Zhang Yijing
Published: January 2024

China’s delayed retirement policy will be prudently rolled out at the appropriate time, yet the public’s acceptance of this policy is concerning. To address this issue, our endeavor explores the impact of framing and anchoring effects on policy acceptance, aiming to mitigate the populace’s resistance to the new policy. We conducted two survey studies on the Chinese population aged 16–65. Achieved through an online survey, Study 1 (N = 225) demonstrated that information framing significantly influences the public’s acceptance of the delayed retirement policy. It was found that perceived fairness plays a mediating role between information framing and policy acceptance. Notably, the positive frame had a more pronounced effect on acceptance than its negative counterpart, with the positive presentation being perceived as more fair. Study 2 (N = 383), utilizing a combination of online and offline approaches, revealed that the anchoring effect moderates the relationship between information framing and perceived fairness. The interaction of anchoring and framing effects significantly influences perceived fairness, subsequently promoting public policy acceptance. The interplay between anchoring and framing effects significantly shapes perceived fairness, in turn bolstering the public’s receptiveness to policy. These insights offer reasonable communication strategies for the smooth advancement of new policies, further enriching the field of behavioral science.
Feasibility Study of Steel Derailment Containment Provisions through Quasi-Static Experiments
Authors: Nguyen Huy Q. , Kim Hoe-Jin , Lim Nam-Hyoung , Kang Yun-Suk , Kim Jung J.
Published: January 2024

Railway derailments present a safety hazard, carrying the potential for severe consequences for both human lives and the economy. Implementing derailment containment provisions (DCPs) near the track centerline is essential for mitigating risks in operating high-speed rail (HSR) while providing significant advantages for the large-scale upgrade of existing railway infrastructure. Therefore, this paper investigated the feasibility of a DCP system made of steel through quasi-static experiments, aiming to enhance safety in HSR operations. Initially, single anchor tests were conducted to assess its capacity to withstand applied loads, prevent the pullout of steel anchors, and avoid the local rotation of the steel frame. Then, full-scale steel DCP systems were manufactured and tested for quasi-static load at different locations, including the mid-anchor, the mid-span, and the end-anchor. The relationship between applied load and displacement, along with the initial stiffness of the DCP specimens, was discussed. The findings revealed that the single anchor can withstand an applied load of up to 197.9 kN. The DCP specimen maintained structural integrity at the 207 kN target load under all load scenarios, showing a maximum displacement of 8.93 mm in the case of applied load at mid-span. Furthermore, the initial stiffness of the DCP systems was 1.77 to 2.55 times greater than that of a single anchor, validating a force-bearing coordination mechanism among neighboring anchors and the substantial impact of the applied load positions on their stiffness.
Thermally Future-Proofing Existing Libyan Housing Stock with Biobased Insulation Materials and Passive Measures: An Empirical and Numerical Study Using a Digital Twin
Authors: Albarssi Salwa , Hou Shan Shan , Latif Eshrar
Published: January 2024

Thermal refurbishment and retrofitting building envelopes with passive measures such as the optimisation of opaque and transparent fabric performance may play a key role in reducing cooling and heating load and promoting building energy efficiency. Furthermore, to reduce the embodied carbon impact of the building, the refurbishment measures need to consider the use of low-carbon building materials. This paper investigates ways to thermally future-proof typical Libyan houses using biobased materials. Several typical Libyan houses were monitored for one year to investigate the heating and cooling energy use and to thermally retrofit the building envelope. A digital twin was created in the DesignBuilder software using the real building data of one building for digital model calibration. Finally, multi-objective optimisation was carried out with low-impact biobased materials for insulation, including camel hair, sheep wool, and date palm fibre as well as using other optimisation variables such as shading and glazing types. The study reveals that thermally upgrading the building roof and wall with insulation materials and upgrading the windows with energy-efficient glazing and local shadings can achieve a reduction in cooling load from 53.51 kWh/m2/y to 40.8 kWh/m2/y. Furthermore, the heating load reduces from 19.4 kW/m2/y to 15 kW/m2/y without compromising the standard annual discomfort hours.
The Multidisciplinary Approach and Surgical Management of GE Junction Adenocarcinoma
Authors: Oberoi Meher , Noor Md. Sibat , Abdelfatah Eihab
Published: January 2024

Gastroesophageal (GE) junction adenocarcinoma is an aggressive malignancy of growing incidence and is associated with public health issues such as obesity and GERD. Management has evolved over the last two decades to incorporate a multidisciplinary approach, including endoscopic intervention, neoadjuvant chemotherapy/chemoradiation, and minimally invasive or more limited surgical approaches. Surgical approaches include esophagectomy, total gastrectomy, and, more recently, proximal gastrectomy. This review analyzes the evidence for and applicability of these varied approaches in management, as well as areas of continued controversy and investigation.
The Potential of Siglecs and Sialic Acids as Biomarkers and Therapeutic Targets in Tumor Immunotherapy
Authors: Feng Haokang , Feng Jiale , Han Xu , Ying Ying , Lou Wenhui , Liu Liang , Zhang Lei
Published: January 2024

The dysregulation of sialic acid is closely associated with oncogenesis and tumor progression. Most tumor cells exhibit sialic acid upregulation. Sialic acid-binding immunoglobulin-like lectins (Siglecs) are receptors that recognize sialic acid and are expressed in various immune cells. The activity of Siglecs in the tumor microenvironment promotes immune escape, mirroring the mechanisms of the well-characterized PD-1/PD-L1 pathway in cancer. Cancer cells utilize sialic acid-linked glycans to evade immune surveillance. As Siglecs exhibit similar mechanisms as the established immune checkpoint inhibitors (ICIs), they are potential therapeutic targets for different forms of cancer, especially ICI-resistant malignancies. Additionally, the upregulation of sialic acid serves as a potential tumor biomarker. This review examines the feasibility of using sialic acid and Siglecs for early malignant tumor detection and discusses the potential of targeting Siglec–sialic acid interaction as a novel cancer therapeutic strategy.
Long Non-Coding RNAs as Regulators for Targeting Breast Cancer Stem Cells and Tumor Immune Microenvironment: Biological Properties and Therapeutic Potential
Authors: Yang Fang , Yang Yiqi , Qiu Yuling , Tang Lin , Xie Li , Guan Xiaoxiang
Published: January 2024

Breast cancer stem cells (BCSCs) is a subpopulation of cancer cells with self-renewal and differentiation capacity, have been suggested to give rise to tumor heterogeneity and biologically aggressive behavior. Accumulating evidence has shown that BCSCs play a fundamental role in tumorigenesis, progression, and recurrence. The development of immunotherapy, primarily represented by programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors, has greatly changed the treatment landscape of multiple malignancies. Recent studies have identified pervasive negative associations between cancer stemness and anticancer immunity. Stemness seems to play a causative role in the formation of cold tumor immune microenvironment (TIME). The multiple functions of long non-coding RNAs (lncRNAs) in regulating stemness and immune responses has been recently highlighted in breast cancer. The review focus on lncRNAs and keys pathways involved in the regulation of BCSCs and TIME. Potential clinical applications using lncRNAs as biomarkers or therapies will be discussed.
Correction: Smith et al. MCAK Inhibitors Induce Aneuploidy in Triple-Negative Breast Cancer Models. Cancers 2023, 15, 3309
Authors: Smith John C. , Husted Stefan , Pilrose Jay , Ems-McClung Stephanie C. , Stout Jane R. , Carpenter Richard L. , Walczak Claire E.
Published: January 2024

The authors alerted the Editorial Office of the mistake on 5 August 2023 and the final documents were sent for evaluation on 12 December 2023 [...]
It Takes Two to Tango: The Interplay between Prostate Cancer and Its Microenvironment from an Epigenetic Perspective
Authors: Zaalberg Anniek , Pottendorfer Elisabeth , Zwart Wilbert , Bergman Andries M.
Published: January 2024

Prostate cancer is the second most common cancer in men worldwide and is associated with high morbidity and mortality. Consequently, there is an urgent unmet need for novel treatment avenues. In addition to somatic genetic alterations, deviations in the epigenetic landscape of cancer cells and their tumor microenvironment (TME) are critical drivers of prostate cancer initiation and progression. Unlike genomic mutations, epigenetic modifications are potentially reversible. Therefore, the inhibition of aberrant epigenetic modifications represents an attractive and exciting novel treatment strategy for castration-resistant prostate cancer patients. Moreover, drugs targeting the epigenome also exhibit synergistic interactions with conventional therapeutics by directly enhancing their anti-tumorigenic properties by “priming” the tumor and tumor microenvironment to increase drug sensitivity. This review summarizes the major epigenetic alterations in prostate cancer and its TME, and their involvement in prostate tumorigenesis, and discusses the impact of epigenome-targeted therapies.
Antidiabetic Drugs in Breast Cancer Patients
Authors: Garczorz Wojciech , Kosowska Agnieszka , Francuz Tomasz
Published: January 2024

Diabetes is one of the leading chronic conditions worldwide, and breast cancer is the most prevalent cancer in women worldwide. The linkage between diabetes and its ability to increase the risk of breast cancer should always be analyzed in patients. This review focuses on the impact of antihyperglycemic therapy in breast cancer patients. Patients with diabetes have a higher risk of developing cancer than the general population. Moreover, diabetes patients have a higher incidence and mortality of breast cancer. In this review, we describe the influence of antidiabetic drugs from insulin and metformin to the current and emerging therapies, incretins and SGLT-2 inhibitors, on breast cancer prognosis. We also emphasize the role of obesity and the metastasis process in breast cancer patients who are treated with antidiabetic drugs.
Heat Shock Protein SSA1 Enriched in Hypoxic Secretome of Candida albicans Exerts an Immunomodulatory Effect via Regulating Macrophage Function
Authors: Teng Wei , Subsomwong Phawinee , Narita Kouji , Nakane Akio , Asano Krisana
Published: January 2024

Candida albicans is an opportunistic pathogenic yeast that can survive in both normoxic and hypoxic environments. The involvement of C. albicans secretome on host biological processes has been demonstrated. However, the immunoregulatory function of C. albicans secretome released under hypoxic condition remains unclear. This study demonstrated the differences in cytokine responses and protein profiles between secretomes prepared under normoxic and hypoxic conditions. Furthermore, the immunoregulatory effects of heat shock protein SSA1(Ssa1), a protein candidate enriched in the hypoxic secretome, were investigated. Stimulation of mouse bone marrow-derived macrophages (BMMs) with Ssa1 resulted in the significant production of interleukin (IL)-10, IL-6, and tumor necrosis factor (TNF)-α as well as the significant expression of M2b macrophage markers (CD86, CD274 and tumor necrosis factor superfamily member 14), suggesting that C. albicans Ssa1 may promote macrophage polarization towards an M2b-like phenotype. Proteomic analysis of Ssa1-treated BMMs also revealed that Ssa1 reduced inflammation-related factors (IL-18-binding protein, IL-1 receptor antagonist protein, OX-2 membrane glycoprotein and cis-aconitate decarboxylase) and enhanced the proteins involved in anti-inflammatory response (CMRF35-like molecule 3 and macrophage colony-stimulating factor 1 receptor). Based on these results, we investigated the effect of Ssa1 on C. albicans infection and showed that Ssa1 inhibited the uptake of C. albicans by BMMs. Taken together, our results suggest that C. albicans alters its secretome, particularly by promoting the release of Ssa1, to modulate host immune response and survive under hypoxic conditions.
A One-Step Novel Method to Fabricate Multigrade Ti6Al4V/TiN Composites Using Laser Powder Bed Fusion
Authors: Sánchez de Rojas Candela Carmen , Riquelme Ainhoa , Rodrigo Pilar , Bonache Victoria , Bedmar Javier , Torres Belén , Rams Joaquín
Published: January 2024

Ti6Al4V is the most used alloy for implants because of its excellent biocompatibility; however, its low wear resistance limits its use in the biomedical industry. The additive manufacturing (AM) of Ti6Al4V is a well-established technique that is being used in many fields. However, the AM of Ti6Al4V composites is currently under investigation, and its manufacture using laser powder bed fusion (L-PBF) would result in a great benefit for many industries. The one-step novel concept proposed uses a gas-controlled L-PBF system that enables the AM of layers with different compositions. Six millimeter-edged cubes of Ti6Al4V were manufactured in an Ar atmosphere and coated with in situ Ti6Al4V/TiN layers by using an Ar–N2 mixture given the direct reaction between titanium and nitrogen. Unreinforced Ti6Al4V presented a martensitic microstructure, and TiN reinforcement dendrites and a minor Ti2N phase were gradually introduced into an α + β basketweave titanium matrix. The composites’ microhardness, nanohardness, and elastic modulus were 2, 3, and 1.5 times higher, respectively, than those of the Ti6Al4V. Porosity levels (caused by a lack of fusion, trapping gases, and interdendritic porosity), ranged from 7 to 12% (most measured 20–40 µm) and increased with the reinforcement content (15 to 25%). A scaled-up, proof-of-concept design of a hip implant stem was 3D printed using this nitriding method. Since the neck of the stem (top part) is more susceptible to the fracture and fretting corrosion process, the resulting graded material part consisted of unreinforced Ti6Al4V at the bottom and Ti6Al4V/TiN at the top. This change was controlled by gradually adding nitrogen to the atmosphere; moreover, it was found that the more nitrogen in the chamber, the more TiN reinforcement formed in the part. A microhardness of ~450 HV0.1 was measured at the bottom and gradually increased to ~900 HV0.1, with the increment corresponding to the in situ TiN reinforcement amount.
Antimicrobial Activity of Photocatalytic Coatings on Surfaces: A Systematic Review and Meta-Analysis
Authors: Ubaldi Francesca , Valeriani Federica , Volpini Veronica , Lofrano Giusy , Romano Spica Vincenzo
Published: January 2024

Photocatalytic technologies represent an innovative method to reduce microbial load on surfaces, even considering recent public health emergencies involving coronaviruses and other microorganisms, whose presence has been detected on surfaces. In this review paper, the antimicrobial efficacy of various photocatalysts applied by different coating methods on different surfaces has been compared and critically discussed. Publications reviewing the use of photocatalytic coatings on surfaces for antimicrobial effectiveness have been examined. Clear search parameters were employed to analyze the PubMed, Scopus, and WOS databases, resulting in 45 papers published between 2006 to 2023 that met the inclusion criteria. The paper assessed various types of photocatalytic coatings that targeted different microbial objectives. Based on the pooled data analysis, the TiO2 coating exhibited a substantial effect in decreasing bacteria strains, both Gram-positive and -negative (99.4%). Although the diversity of these technologies poses significant obstacles to obtaining a comprehensive final assessment of their effectiveness and feasibility for surface application, subgroup analysis indicated significant variations in the removal efficiency of Gram-positive strains based on different surface types (p = 0.005) and time of exposure (p = 0.05). Photocatalytic coatings provide a promising approach to combating the spread of microorganisms on surfaces. Further “in-field” investigations are necessary in the foreseeable future to explore and optimize this novel and exciting health technology.
Complex Substrate Leading to PVC-Mediated Systolic Dysfunction in addition to Sustained Monomorphic VT in Repaired Tetralogy of Fallot
Authors: Cojocaru Cosmin , Deaconu Silvia , Gondos Viviana , Onciul Sebastian , Petre Ioana , Gheorghe-Fronea Oana , Vătășescu Radu
Published: January 2024

Frequent premature ventricular complexes (PVCs) are associated with deleterious effects on left ventricular (LV) function in various clinical scenarios. Repaired tetralogy of Fallot (rTOF) is frequently affected by sustained ventricular arrhythmias dependent on complex post-surgical substrates. However, there is limited data regarding the potential of arrhythmogenic isthmuses to generate frequent PVCs and PVC-mediated LV systolic dysfunction development in rTOF. We present a case of rTOF experiencing relatively infrequent episodes of internal shocks for episodes of sustained monomorphic ventricular tachycardia and a high burden of PVCs associated with left ventricular systolic function deterioration, in which the successful substrate ablation of the anatomical VT isthmuses also led to PVC abolition and consequently to LV systolic function normalization. In such cases, understanding the pathogenic mechanisms that lead to LV dysfunction is only possible by rigorous clinical reasoning, which leads to a tailored specific treatment.
Built-In Self-Test Architecture Enabling Diagnosis for Massive Embedded Memory Banks in Large SoCs
Authors: Bernardi Paolo , Guerriero Augusto Maria , Insinga Giorgio , Paganini Giovanni , Carnevale Giambattista , Coppetta Matteo , Mischo Walter , Ullmann Rudolf
Published: January 2024

This paper describes a hardware/software strategy for the effective and efficient management of several distributed Memory Built-In Self-Test (MBIST) units orchestrated by a single CPU to enable the parallel testing of several memory banks. Experimental testing of the implementation on an Infineon chip shows up to a 25% test time reduction compared to traditional strategies, especially in cases for which there are a large number of failures affecting several banks. Additionally, it permits balanced failure collection from different banks in cases for which there are limitations to the storage of failure-related information.
Target Speaker Extraction Using Attention-Enhanced Temporal Convolutional Network
Authors: Wang Jian-Hong , Lai Yen-Ting , Tai Tzu-Chiang , Le Phuong Thi , Pham Tuan , Wang Ze-Yu , Li Yung-Hui , Wang Jia-Ching , Chang Pao-Chi
Published: January 2024

When recording conversations, there may be multiple people talking at once. While our human ears can filter out unwanted sounds, this can be challenging for automatic speech recognition (ASR) systems, leading to reduced accuracy. To address this issue, preprocessing mechanisms such as speech separation and targeted speaker extraction are necessary to separate each person’s speech. With the development of deep learning, the quality of separated speech has improved significantly. Our objective is to focus on speaker extraction, which entails implementing a primary system for speech extraction and a secondary subsystem for delivering target information. To accomplish this, we have chosen a temporal convolutional network (TCN) architecture as the foundation of our speech extraction model. A TCN enables convolutional neural networks (CNNs) to manage time series modeling, and it can be constructed in various model lengths. Furthermore, we have integrated attention enhancement into the secondary subsystem to provide the speech extraction model with comprehensive and effective target information, which helps to improve the model’s ability to estimate masks. As a result, the quality of the target speaker extraction will be greatly enhanced with a more precise mask.
Channel Switching Algorithms for a Robust Networked Control System with a Delay and Packet Errors
Authors: Yang Janghoon
Published: January 2024

Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQG control performance with respect to delay. Based on this insight, we develop two threshold-based channel-switching algorithms, proven to ensure finite total decoder estimation error variance under certain conditions. Specific conditions are also identified where the proposed algorithms offer improved probabilistic stability. Numerical simulations confirm the superior performance of the proposed algorithms compared to conventional methods across diverse channel environments. Notably, the proposed algorithms demonstrate near-optimal performance in a practical operational scenario involving multiple channels, specifically 5G cellular link and Starlink.
A Fast and Efficient Task Offloading Approach in Edge-Cloud Collaboration Environment
Authors: Liu Linyuan , Zhu Haibin , Wang Tianxing , Tang Mingwei
Published: January 2024

Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method.
Spillover Effects between Crude Oil Returns and Uncertainty: New Evidence from Time-Frequency Domain Approaches
Authors: Tissaoui Kais , Abidi Ilyes , Azibi Nadia , Nsaibi Mariem
Published: January 2024

This paper examines the extent to which uncertainty in the energy market, the financial market, the commodity market, the economic policy, and the geopolitical events affect crude oil returns. To consider the complex properties of time series, such as nonlinearity, temporal variability, and unit roots, we adopt a two-instrument technique in the time–frequency domain that employs the DCC-GARCH (1.1) model and the Granger causality test in the frequency domain. This allows us to estimate the dynamic transmission of uncertainty from various sources to the oil market in the time and frequency domains. Significant dynamic conditional correlations over time are found between oil returns—commodity uncertainty, oil returns—equity market uncertainty, and oil returns—energy uncertainty. Furthermore, at each frequency, the empirical results demonstrate a significant spillover effect from the commodity, energy, and financial markets to the oil market. Additionally, we discover that sources with high persistence volatility (such as commodities, energy, and financial markets) have more interactions with the oil market than sources with low persistence volatility (economic policy and geopolitical risk events). Our findings have significant ramifications for boosting investor trust in risky energy assets.
Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data
Authors: Haghi Rad , Stagg Cassidy , Crawford Curran
Published: January 2024

Assessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.
Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model
Authors: Pasandideh Mostafa , Taylor Matthew , Tito Shafiqur Rahman , Atkins Martin , Apperley Mark
Published: January 2024

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.
Epigenetics as a Regulator of Tree Specialized Metabolites In Vitro Production
Authors: Maury Stéphane
Published: January 2024

Specialized metabolites correspond to millions of natural molecules from different chemical families depending on plant taxa that play a key role in ecological interactions during their life cycle. Due to their chemical properties, plants’ specialized metabolites have been exploited for a long time for various industrial applications. However, the limitations in natural population resources as well as the difficulties of their cultivation in terms of production quality or product safety have not always been satisfactory, notably for perennials such as forest trees. Reliable and eco-adapted practices for the production of specialized metabolites such as in vitro cultures provide a useful and powerful alternative to agronomic cultures. Modern omics have allowed the identification of metabolite pathways but have also raised the question of their complex regulation to improve their production. Among the major regulatory players, epigenetics have been shown in recent years to be involved in plant development and the response to environmental variations. Here, the state of the art concerning the epigenetic control of plant specialized metabolite in vitro production as well as the challenges in forest trees are presented.
Elevation-Dependent Natural Regeneration of Abies georgei var. smithii Forest in Southeastern Tibet
Authors: Wang Ruihong , Hu Ronggui , Wu Yupeng , Shaaban Muhammad , Zhang Tao , Pan Gang , Lu Jie , Jiang Yanbin
Published: January 2024

The comprehensive impacts of biotic and abiotic factors on the natural regeneration of Abies georgei var. smithii (Abies) forests in Tibet are not thoroughly understood. To address this gap, our study focused on the regeneration densities of Abies seedlings, saplings, and small trees across 21 plots (each 20 m × 20 m) along an elevation gradient from 3730 m to 4330 m in the Sygera Mountains of Southeastern Tibet. We meticulously measured a suite of 11 variables that describe stand structures and ecological conditions. Through analyses using Spearman’s correlation analysis, hierarchical partitioning, and multiple linear regression, we identified key ecological drivers for successful Abies regeneration. Our results highlighted a peak in the abundance of seedlings, saplings, and small trees at an elevation of 3930 m. As the elevation rose from 3730 m to 4330 m, we observed an initial increase followed by a decrease in canopy cover (canopy), mother tree density (MotherT), 1000-seed weight (SeedW), litter thickness (LitterT), moss cover (MossC), moss thickness (MossT), soil moisture (SM), and soil bulk density, while mean annual temperature and soil depth to permafrost consistently decreased. The critical ecological drivers for Abies natural regeneration were identified as follows: MossT was pivotal for seedling density; canopy and MossC were influential for sapling density, and MotherT was the main factor affecting the density of small trees. This study suggests that a high density of mother trees and a thick and highly covered layer of moss are conducive to the natural regeneration of Abies in the Sygera Mountains. Understanding the current status of regeneration is vital for informing conservation and management strategies for Abies forests in Tibet.
Enzymatic Processing of DNA–Protein Crosslinks
Authors: Essawy Maram M. , Campbell Colin
Published: January 2024

DNA–protein crosslinks (DPCs) represent a unique and complex form of DNA damage formed by covalent attachment of proteins to DNA. DPCs are formed through a variety of mechanisms and can significantly impede essential cellular processes such as transcription and replication. For this reason, anti-cancer drugs that form DPCs have proven effective in cancer therapy. While cells rely on numerous different processes to remove DPCs, the molecular mechanisms responsible for orchestrating these processes remain obscure. Having this insight could potentially be harnessed therapeutically to improve clinical outcomes in the battle against cancer. In this review, we describe the ways cells enzymatically process DPCs. These processing events include direct reversal of the DPC via hydrolysis, nuclease digestion of the DNA backbone to delete the DPC and surrounding DNA, proteolytic processing of the crosslinked protein, as well as covalent modification of the DNA-crosslinked proteins with ubiquitin, SUMO, and Poly(ADP) Ribose (PAR).
Molecular Markers Help with Breeding for Agronomic Traits of Spring Wheat in Kazakhstan and Siberia
Authors: Morgounov Alexey , Babkenov Adylkhan , Ben Cécile , Chudinov Vladimir , Dolinny Yuriy , Dreisigacker Susanne , Fedorenko Elena , Gentzbittel Laurent , Rasheed Awais , Savin Timur , Shepelev Sergey , Zhapayev Rauan , Shamanin Vladimir
Published: January 2024

The Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000, forming a collaboration between breeding and research programs through biannual yield trials. A core set of 142 genotypes from 15 breeding programs was selected, genotyped for 81 DNA functional markers and phenotyped for 10 agronomic traits at three sites in Kazakhstan (Karabalyk, Shortandy and Shagalaly) and one site in Russia (Omsk) in 2020–2022. The study aim was to identify markers demonstrating significant effects on agronomic traits. The average grain yield of individual trials varied from 118 to 569 g/m2. Grain yield was positively associated with the number of days to heading, plant height, number of grains per spike and 1000-kernel weight. Eight DNA markers demonstrated significant effects. The spring-type allele of the Vrn-A1 gene accelerated heading by two days (5.6%) and was present in 80% of the germplasm. The winter allele of the Vrn-A1 gene significantly increased grain yield by 2.7%. The late allele of the earliness marker per se, TaMOT1-D1, delayed development by 1.9% and increased yield by 4.5%. Translocation of 1B.1R was present in 21.8% of the material, which resulted in a 6.2% yield advantage compared to 1B.1B germplasm and a reduction in stem rust severity from 27.6 to 6.6%. The favorable allele of TaGS-D1 increased both kernel weight and yield by 2–3%. Four markers identified in ICARDA germplasm, ISBW2-GY (Kukri_c3243_1065, 3B), ISBW3-BM (TA004946-0577, 1B), ISBW10-SM2 (BS00076246_51, 5A), ISBW11-GY (wsnp_Ex_c12812_20324622, 4A), showed an improved yield in this study of 3–4%. The study recommends simultaneous validation and use of selected markers in KASIB’s network.
Socioeconomic, Demographic, and Environmental Factors May Inform Malaria Intervention Prioritization in Urban Nigeria
Authors: Chiziba Chilochibi , Mercer Laina D. , Diallo Ousmane , Bertozzi-Villa Amelia , Weiss Daniel J. , Gerardin Jaline , Ozodiegwu Ifeoma D.
Published: January 2024

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.
Functional Analysis of Membrane-Associated Scaffolding Tight Junction (TJ) Proteins in Tumorigenic Characteristics of B16-F10 Mouse Melanoma Cells
Authors: Ko Eun-Ji , Kim Do-Ye , Kim Min-Hye , An Hyojin , Kim Jeongtae , Jeong Jee-Yeong , Song Kyoung Seob , Cha Hee-Jae
Published: January 2024

Tight junction (TJ) proteins (Tjps), Tjp1 and Tjp2, are tight junction-associated scaffold proteins that bind to the transmembrane proteins of tight junctions and the underlying cytoskeleton. In this study, we first analyzed the tumorigenic characteristics of B16-F10 melanoma cells, including cell proliferation, migration, invasion, metastatic potential, and the expression patterns of related proteins, after the CRISPR–Cas9-mediated knockout (KO) of Tjp genes. The proliferation of Tjp1 and Tjp2 KO cells significantly increased in vitro. Other tumorigenic characteristics, including migration and invasion, were significantly enhanced in Tjp1 and Tjp2 KO cells. Zonula occludens (ZO)-associated protein Claudin-1 (CLDN-1), which is a major component of tight junctions and functions in controlling cell-to-cell adhesion, was decreased in Tjp KO cells. Additionally, Tjp KO significantly stimulated tumor growth and metastasis in an in vivo mouse model. We performed a transcriptome analysis using next-generation sequencing (NGS) to elucidate the key genes involved in the mechanisms of action of Tjp1 and Tjp2. Among the various genes affected by Tjp KO-, cell cycle-, cell migration-, angiogenesis-, and cell–cell adhesion-related genes were significantly altered. In particular, we found that the Ninjurin-1 (Ninj1) and Catenin alpha-1 (Ctnna1) genes, which are known to play fundamental roles in Tjps, were significantly downregulated in Tjp KO cells. In summary, tumorigenic characteristics, including cell proliferation, migration, invasion, tumor growth, and metastatic potential, were significantly increased in Tjp1 and Tjp2 KO cells, and the knockout of Tjp genes significantly affected the expression of related proteins.
From Beach to the Bedside: Harnessing Mitochondrial Function in Human Diseases Using New Marine-Derived Strategies
Authors: Mirra Serena , Marfany Gemma
Published: January 2024

Mitochondria are double-membrane organelles within eukaryotic cells that act as cellular power houses owing to their ability to efficiently generate the ATP required to sustain normal cell function. Also, they represent a “hub” for the regulation of a plethora of processes, including cellular homeostasis, metabolism, the defense against oxidative stress, and cell death. Mitochondrial dysfunctions are associated with a wide range of human diseases with complex pathologies, including metabolic diseases, neurodegenerative disorders, and cancer. Therefore, regulating dysfunctional mitochondria represents a pivotal therapeutic opportunity in biomedicine. Marine ecosystems are biologically very diversified and harbor a broad range of organisms, providing both novel bioactive substances and molecules with meaningful biomedical and pharmacological applications. Recently, many mitochondria-targeting marine-derived molecules have been described to regulate mitochondrial biology, thus exerting therapeutic effects by inhibiting mitochondrial abnormalities, both in vitro and in vivo, through different mechanisms of action. Here, we review different strategies that are derived from marine organisms which modulate specific mitochondrial processes or mitochondrial molecular pathways and ultimately aim to find key molecules to treat a wide range of human diseases characterized by impaired mitochondrial function.