bluuebunny/crossref_metadata_2025 huggingface dataset:
Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution
Authors: López-García Diego Antonio , Pérez Torreglosa Juan , Vera David , Sánchez-Raya Manuel
Published: January 2024

Mixnets are an instrument to achieve anonymity. They are generally a sequence of servers that apply a cryptographic process and a permutation to a batch of user messages. Most use asymmetric cryptography, with the high computational cost that this entails. The main objective of this study is to reduce delay in mixnet nodes. In this sense, this paper presents a new scheme that is based only on symmetric cryptography. The novelty of this scheme is the use of binary graphs built by mixnet nodes. The root node collects user keys and labels without knowing their owners. After feeding each node by its graph, they can establish a random permutation and relate their keys to the incoming batch positions through labels. The differences with previous symmetric schemes are that users do not need long headers and nodes avoid the searching process. The outcomes are security and efficiency improvements. As far as we know, it is the fastest mixnet system. Therefore, it is appropriate for high-throughput applications like national polls (many users) or debates (many messages).
Selective Vulnerability to Neurodegenerative Disease: Insights from Cell Type-Specific Translatome Studies
Authors: Jackson Walker S. , Bauer Susanne , Kaczmarczyk Lech , Magadi Srivathsa S.
Published: January 2024

Neurodegenerative diseases (NDs) manifest a wide variety of clinical symptoms depending on the affected brain regions. Gaining insights into why certain regions are resistant while others are susceptible is vital for advancing therapeutic strategies. While gene expression changes offer clues about disease responses across brain regions, the mixture of cell types therein obscures experimental results. In recent years, methods that analyze the transcriptomes of individual cells (e.g., single-cell RNA sequencing or scRNAseq) have been widely used and have provided invaluable insights into specific cell types. Concurrently, transgene-based techniques that dissect cell type-specific translatomes (CSTs) in model systems, like RiboTag and bacTRAP, offer unique advantages but have received less attention. This review juxtaposes the merits and drawbacks of both methodologies, focusing on the use of CSTs in understanding conditions like amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), Alzheimer’s disease (AD), and specific prion diseases like fatal familial insomnia (FFI), genetic Creutzfeldt–Jakob disease (gCJD), and acquired prion disease. We conclude by discussing the emerging trends observed across multiple diseases and emerging methods.
Advances in Engineered Nano-Biosensors for Bacteria Diagnosis and Multidrug Resistance Inhibition
Authors: Xia Qingxiu , Jiang Hui , Liu Xiaohui , Yin Lihong , Wang Xuemei
Published: January 2024

Bacterial infections continue to pose a significant global health challenge, with the emergence of multidrug-resistant (MDR) bacteria and biofilms further complicating treatment options. The rise of pan-resistant bacteria, coupled with the slow development of new antibiotics, highlights the urgent need for new therapeutic strategies. Nanotechnology-based biosensors offer fast, specific, sensitive, and selective methods for detecting and treating bacteria; hence, it is a promising approach for the diagnosis and treatment of MDR bacteria. Through mechanisms, such as destructive bacterial cell membranes, suppression of efflux pumps, and generation of reactive oxygen species, nanotechnology effectively combats bacterial resistance and biofilms. Nano-biosensors and related technology have demonstrated their importance in bacteria diagnosis and treatment, providing innovative ideas for MDR inhibition. This review focuses on multiple nanotechnology approaches in targeting MDR bacteria and eliminating antimicrobial biofilms, highlighting nano-biosensors via photodynamics-based biosensors, eletrochemistry biosensors, acoustic-dynamics sensors, and so on. Furthermore, the major challenges, opportunities of multi-physical-field biometrics-based biosensors, and relevant nanotechnology in MDR bacterial theranostics are also discussed. Overall, this review provides insights and scientific references to harness the comprehensive and diverse capabilities of nano-biosensors for precise bacteria theranostics and MDR inhibition.
Molecular Insights into Transcranial Direct Current Stimulation Effects: Metabolomics and Transcriptomics Analyses
Authors: Agrawal Bhanumita , Boulos Soad , Khatib Soliman , Feuermann Yonatan , Panov Julia , Kaphzan Hanoch
Published: January 2024

Introduction: Transcranial direct current stimulation (tDCS) is an evolving non-invasive neurostimulation technique. Despite multiple studies, its underlying molecular mechanisms are still unclear. Several previous human studies of the effect of tDCS suggest that it generates metabolic effects. The induction of metabolic effects by tDCS could provide an explanation for how it generates its long-term beneficial clinical outcome. Aim: Given these hints of tDCS metabolic effects, we aimed to delineate the metabolic pathways involved in its mode of action. Methods: To accomplish this, we utilized a broad analytical approach of co-analyzing metabolomics and transcriptomic data generated from anodal tDCS in rat models. Since no metabolomic dataset was available, we performed a tDCS experiment of bilateral anodal stimulation of 200 µA for 20 min and for 5 consecutive days, followed by harvesting the brain tissue below the stimulating electrode and generating a metabolomics dataset using LC-MS/MS. The analysis of the transcriptomic dataset was based on a publicly available dataset. Results: Our analyses revealed that tDCS alters the metabolic profile of brain tissue, affecting bioenergetic-related pathways, such as glycolysis and mitochondrial functioning. In addition, we found changes in calcium-related signaling. Conclusions: We conclude that tDCS affects metabolism by modulating energy production-related processes. Given our findings concerning calcium-related signaling, we suggest that the immediate effects of tDCS on calcium dynamics drive modifications in distinct metabolic pathways. A thorough understanding of the underlying molecular mechanisms of tDCS has the potential to revolutionize its applicability, enabling the generation of personalized medicine in the field of neurostimulation and thus contributing to its optimization.
Two-Dimensional Tellurium Nanosheets for the Efficient Nonenzymatic Electrochemical Detection of H2O2
Authors: Shringi Amit Kumar , Kumar Rajeev , Dennis Netanya F. , Yan Fei
Published: January 2024

This study reports, for the first time, the utilization of two-dimensional (2D) tellurium (Te) nanosheets for the efficient nonenzymatic detection of hydrogen peroxide (H2O2). H2O2 acts as a pivotal biomarker with widespread applications across environmental, biological, industrial, and food processing domains. However, an excessive accumulation of H2O2 in the body poses a severe threat to human life. Consequently, the imperative need for a selective, sensitive, and cost-effective sensing platform for H2O2 detection has gained paramount significance. Employing a low-cost and straightforward hydrothermal method, Te nanosheets were synthesized to address the escalating demand for a reliable detection platform. The as-synthesized Te nanosheets are characterized through Raman spectroscopy and atomic force microscopy techniques. The electrochemical performance of the Te nanosheets integrated onto a glassy carbon (Te-GC) electrode was thoroughly investigated using cyclic voltammetry, differential pulse voltammetry, and chronoamperometry. The experiments were designed to evaluate the response of the Te-GC electrode in the presence and absence of H2O2, alongside its performance in the detection of other pertinent interfering analytes. The sensor shows a limit of detection of 0.47 µM and a sensitivity of 27.2 µA µM−1 cm−2 towards H2O2. The outcomes of this study demonstrate the efficacy of Te nanosheets as a promising material for nonenzymatic H2O2 detection in urine samples. The simplicity and cost-effectiveness of the hydrothermal synthesis process, coupled with the notable electrochemical performance of the Te/GC electrode, highlight the potential of Te nanosheets in the development of a robust sensing platform. This research contributes to the ongoing efforts to enhance our capabilities in monitoring and detecting H2O2, fostering advancements in environmental, biomedical, and industrial applications.
Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review
Authors: Almoammar Khalid A.
Published: January 2024

Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.
1064 nm Q-Switched Fractional Laser for Transcutaneous Delivery of a Biostimulator: Efficacy and Safety Outcomes of a Split-Face Study
Authors: Moro Francesco , Camela Elisa , Samela Tonia , Pirrotta Lia , Pupa Maria Beatrice , Zingoni Tiziano , Fusco Irene , Colonna Laura
Published: January 2024

Background: Laser-assisted drug delivery is a promising strategy that enhances topical treatment by increasing cutaneous permeation and reducing side effects. In cosmetic settings, the efficacy and safety profiles of a treatment must meet the need of a painless procedure with fast recovery. In this context, Q-switched laser appears promising as it can open cutaneous pores without creating a carbonisation barrier. Methods: A split-face study on patients presenting for cosmetic procedures at IDI-IRCCS, Rome (30 September–18 October 2023), was conducted. Pan-facial Q-Switched laser was followed by a topical biostimulator applied on half of the face. Post-procedure local reactions were recorded together with patients’ perceptions. Cutaneous elasticity and hydration were assessed at baseline and three-week follow-up. The Skindex17 questionnaire evaluated the procedure’s impact on patients’ life quality. Also, participants and physicians expressed satisfaction with the treatment. Results: The procedure was well tolerated by patients; local reactions include transitory erythema, superficial bleeding, and oedema, none of which had an impact on daily life. An improvement in skin quality was documented objectively and subjectively by patients and physicians. Conclusion: Q-switched lasers emerge as promising devices for drug delivery, especially for cosmetic reasons. Indeed, the low risk of local reactions together with a remarkable increase in cutaneous permeation make this a suitable strategy for cosmetic procedures.
Structural Aspects of the ROS1 Kinase Domain and Oncogenic Mutations
Authors: Vilachã Juliana F. , Wassenaar Tsjerk A. , Marrink Siewert J.
Published: January 2024

Protein kinases function as pivotal regulators in biological events, governing essential cellular processes through the transfer of phosphate groups from ATP molecules to substrates. Dysregulation of kinase activity is frequently associated with cancer, ocasionally arising from chromosomal translocation events that relocate genes encoding kinases. Fusion proteins resulting from such events, particularly those involving the proto-oncogene tyrosine-protein kinase ROS (ROS1), manifest as constitutively active kinases, emphasizing their role in oncogenesis. Notably, the chromosomal reallocation of the ros1 gene leads to fusion of proteins with the ROS1 kinase domain, implicated in various cancer types. Despite their prevalence, targeted inhibition of these fusion proteins relies on repurposed kinase inhibitors. This review comprehensively surveys experimentally determined ROS1 structures, emphasizing the pivotal role of X-ray crystallography in providing high-quality insights. We delve into the intricate interactions between ROS1 and kinase inhibitors, shedding light on the structural basis for inhibition. Additionally, we explore point mutations identified in patients, employing molecular modeling to elucidate their structural impact on the ROS1 kinase domain. By integrating structural insights with in vitro and in silico data, this review advances our understanding of ROS1 kinase in cancer, offering potential avenues for targeted therapeutic strategies.
Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals
Authors: Alge Olivia P. , Gryak Jonathan , VanEpps J. Scott , Najarian Kayvan
Published: January 2024

The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient’s quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.
Comparison of Fine-Needle Aspiration Cytopathology with Histopathological Examination of the Thyroid Gland in Patients Undergoing Elective Thyroid Surgery: Do We Still Need Fine-Needle Aspiration Cytopathology?
Authors: Gąsiorowski Oskar , Leszczyński Jerzy , Kaszczewska Joanna , Stępkowski Kamil , Kaszczewski Piotr , Baryła Maksymilian , Gałązka Zbigniew
Published: January 2024

Background: The thyroid gland is responsible for various functions, but it is susceptible to pathologies. The gold standard for preliminarily diagnosing thyroid abnormalities is fine-needle aspiration cytology (FNAC), although it has some limitations; thus, postoperative histopathological examination confirms the diagnosis. The aim of the present study was to compare preoperative FNAC results with postoperative histopathological examination. Methods: This study is a retrospective study based on FNAC and postoperative histopathology examination, which were compared and analyzed. Results: This study included 344 patients between 18 and 86 years old (mean age: 53.06 ± 13.89), comprising 274 females and 70 males (mean ages 52.72 ± 13.86 and 54.39 ± 14.05, respectively) with a 3.9:1 female-to-male ratio. Statistical significance between the FNAC and histopathology results was observed (p = 0.0000), and 86 (25.00%) patients were found to have been diagnosed incorrectly based on FNAC. The sensitivity of FNAC was 92.31%, and its specificity was 82.08%, with positive and negative predictive values of 68.57% and 96.08%, respectively. Conclusions: Due to many factors, FNAC may lead to over- or under-diagnosis, increasing the chances of complications associated with the selected treatment. However, we do not have any other more accurate tools; therefore, FNAC should still remain as the gold standard of preliminary examination.
A Retrospective Analysis of 2-Year Follow-Up of Patients with Incidental Findings of Sarcoidosis
Authors: Thomas-Orogan Oluwabukola , Barratt Shaney L. , Zafran Muhammad , Kwok Apollo , Simons Anneliese , Judge Eoin P. , Wells Matthew , Daly Richard , Sharp Charles , Jeyabalan Abiramy , Plummeridge Martin , Chandratreya Ladli , Spencer Lisa G. , Medford Andrew R. L. , Adamali Huzaifa I.
Published: January 2024

Introduction: Sarcoidosis is a multi-system granulomatous disease most commonly involving the lungs. It may be incidentally diagnosed during imaging studies for other conditions or non-specific symptoms. The appropriate follow-up of incidentally diagnosed asymptomatic stage 1 disease has not been well defined. Objective: To define the clinical course of incidentally diagnosed asymptomatic stage 1 sarcoidosis and propose an algorithm for the follow-up of these patients. Methodology: A retrospective case note analysis was performed of all EBUS-TBNA (endobronchial ultrasound-guided transbronchial needle aspiration)-confirmed cases of stage 1 sarcoidosis presenting incidentally to Bristol and Liverpool Interstitial Lung Disease services. Clinical history, serology results, imaging scans, and lung function parameters were examined at baseline, 12, and 24 months. A cost analysis was performed comparing the cost of the current 2-year follow-up guidance to a 1 year follow-up period. Results: Sixty-seven patients were identified as the final cohort. There was no significant change in the pulmonary function tests over the two-year follow-up period. Radiological disease stability was observed in the majority of patients (58%, n = 29), and disease regression was evidenced in 40% (n = 20) at 1 year. Where imaging was performed at 2 years, the majority (69.8%, n = 37) had radiological evidence of disease regression, and 30.2% (n = 16) showed radiological evidence of stability. All patients remained asymptomatic and did not require therapeutic intervention over the study period. Conclusions: Our results show that asymptomatic patients with incidental findings of thoracic lymph nodal non-caseating granulomas do not progress over a 2-year period. Our results suggest that the prolonged secondary-care follow-up of such patients may not be necessary. We propose that these patients are followed up for 1 year with a further year of patient-initiated follow-up (PIFU) prior to discharge.
An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction
Authors: Revathi T.K. , Balasubramaniam Sathiyabhama , Sureshkumar Vidhushavarshini , Dhanasekaran Seshathiri
Published: January 2024

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM’s network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model’s efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM’s superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.
Robust Multi-Dimensional Time Series Forecasting
Authors: Shen Chen , He Yong , Qin Jin
Published: January 2024

Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during measurement or transmission. Directly forecasting such types of data (i.e., anomalous data) can be extremely challenging. The traditional method to deal with anomalies is to cut out the time series with anomalous value entries or replace the data. Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better handle anomalous values: the robust temporal nonnegative matrix factorization forecasting model (RTNMFFM) for multi-dimensional time series. RTNMFFM integrates the autoregressive regularizer into nonnegative matrix factorization (NMF) with the application of the L2,1 norm in NMF. This approach improves robustness and alleviates overfitting compared to standard methods. In addition, to improve the accuracy of model forecasts on severely missing data, we propose a periodic smoothing penalty that keeps the sparse time slices as close as possible to the time slice with high confidence. Finally, we train the model using the alternating gradient descent algorithm. Numerous experiments demonstrate that RTNMFFM provides better robustness and better prediction accuracy.
Probabilistic Hesitant Fuzzy Evidence Theory and Its Application in Capability Evaluation of a Satellite Communication System
Authors: Liu Jiahuan , Jian Ping , Liu Desheng , Xiong Wei
Published: January 2024

Evaluating the capabilities of a satellite communication system (SCS) is challenging due to its complexity and ambiguity. It is difficult to accurately analyze uncertain situations, making it difficult for experts to determine appropriate evaluation values. To address this problem, this paper proposes an innovative approach by extending the Dempster-Shafer evidence theory (DST) to the probabilistic hesitant fuzzy evidence theory (PHFET). The proposed approach introduces the concept of probabilistic hesitant fuzzy basic probability assignment (PHFBPA) to measure the degree of support for propositions, along with a combination rule and decision approach. Two methods are developed to generate PHFBPA based on multi-classifier and distance techniques, respectively. In order to improve the consistency of evidence, discounting factors are proposed using an entropy measure and the Jousselme distance of PHFBPA. In addition, a model for evaluating the degree of satisfaction of SCS capability requirements based on PHFET is presented. Experimental classification and evaluation of SCS capability requirements are performed to demonstrate the effectiveness and stability of the PHFET method. By employing the DST framework and probabilistic hesitant fuzzy sets, PHFET provides a compelling solution for handling ambiguous data in multi-source information fusion, thereby improving the evaluation of SCS capabilities.
FedTKD: A Trustworthy Heterogeneous Federated Learning Based on Adaptive Knowledge Distillation
Authors: Chen Leiming , Zhang Weishan , Dong Cihao , Zhao Dehai , Zeng Xingjie , Qiao Sibo , Zhu Yichang , Tan Chee Wei
Published: January 2024

Federated learning allows multiple parties to train models while jointly protecting user privacy. However, traditional federated learning requires each client to have the same model structure to fuse the global model. In real-world scenarios, each client may need to develop personalized models based on its environment, making it difficult to perform federated learning in a heterogeneous model environment. Some knowledge distillation methods address the problem of heterogeneous model fusion to some extent. However, these methods assume that each client is trustworthy. Some clients may produce malicious or low-quality knowledge, making it difficult to aggregate trustworthy knowledge in a heterogeneous environment. To address these challenges, we propose a trustworthy heterogeneous federated learning framework (FedTKD) to achieve client identification and trustworthy knowledge fusion. Firstly, we propose a malicious client identification method based on client logit features, which can exclude malicious information in fusing global logit. Then, we propose a selectivity knowledge fusion method to achieve high-quality global logit computation. Additionally, we propose an adaptive knowledge distillation method to improve the accuracy of knowledge transfer from the server side to the client side. Finally, we design different attack and data distribution scenarios to validate our method. The experiment shows that our method outperforms the baseline methods, showing stable performance in all attack scenarios and achieving an accuracy improvement of 2% to 3% in different data distributions.
A Dynamic Network Model of Societal Complexity and Resilience Inspired by Tainter’s Theory of Collapse
Authors: Schunck Florian , Wiedermann Marc , Heitzig Jobst , Donges Jonathan F.
Published: January 2024

In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies. Examples include the COVID-19 pandemic, which brought unprecedented health challenges and economic disruptions, and the emergence of geopolitical tensions and conflicts that have further strained international relations and economic stability. While empirical evidence on the dynamics and drivers of past societal collapse is mounting, a process-based understanding of these dynamics is still in its infancy. Here, we aim to identify and illustrate the underlying drivers of such societal instability or even collapse. The inspiration for this work is Joseph Tainter’s theory of the “collapse of complex societies”, which postulates that the complexity of societies increases as they solve problems, leading to diminishing returns on complexity investments and ultimately to collapse. In this work, we abstract this theory into a low-dimensional and stylized model of two classes of networked agents, hereafter referred to as “laborers” and “administrators”. We numerically model the dynamics of societal complexity, measured as the fraction of “administrators”, which was assumed to affect the productivity of connected energy-producing “laborers”. We show that collapse becomes increasingly likely as the complexity of the model society continuously increases in response to external stresses that emulate Tainter’s abstract notion of problems that societies must solve. We also provide an analytical approximation of the system’s dominant dynamics, which matches well with the numerical experiments, and use it to study the influence on network link density, social mobility and productivity. Our work advances the understanding of social-ecological collapse and illustrates its potentially direct link to an ever-increasing societal complexity in response to external shocks or stresses via a self-reinforcing feedback.
A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays
Authors: Li Pei , Li Zongyao , Chen Chaoyang , Chen Juan , Chen Zuguo
Published: January 2024

In underwater navigation of autonomous underwater vehicles (AUVs), communication delays frequently occur, leading to a reduction in positioning accuracy. To mitigate this challenge, this work introduces a novel method for relative angle correction, aiming to reconstruct measurement information. Initially, Doppler measurement data are assimilated into the reconstruction of measurement equations to determine the relative angle between the AUV and the observatory. Subsequently, the obtained angle information is integrated into the Extended Kalman Filter (EKF) for the reconstruction of measurement equations. The proposed method effectively reduces positioning errors caused by hydroacoustic communication delays, consequently enhancing AUV positioning accuracy. The efficacy of the proposed method is demonstrated through a simulation study. Simulation results reveal that the incorporation of Doppler angle correction in the reconstructed measurement information method significantly decreases the localization error by approximately 50% compared to EKF and by around 20% compared to the method lacking angle correction.
A Proposal for A Human-in-the-Loop Daylight Control System—Preliminary Experimental Results
Authors: de Rubeis Tullio , Ragnoli Mattia , Leoni Alfiero , Ambrosini Dario , Stornelli Vincenzo
Published: January 2024

Appropriate daylight control could maximize occupants’ visual comfort, potentially saving energy. However, the deployment of daylight control systems (DLCSs) is not happening, mainly due to the complex system calibration and the frequent reluctance of occupants toward automatic control systems that exclude their participation. In this paper, a human-in-the-loop DLCS is presented. The system is designed to allow the users to have direct interaction via smartphone Bluetooth communication, enabling them to set the lighting values deemed most comfortable nimbly. Special attention has been paid to the power consumption of the DLCS, especially in standby mode. Accessibility of configuration has been taken into consideration, leading to the choice of a wireless configured device. The performance of the prototype DLCS was evaluated experimentally in a side-lit room and compared with that of a commercial controller. The illuminance on a reference work plane was measured during the operation of the systems to observe the controllers’ effect on the lamp’s luminous flux while simultaneously considering the variation of daylight conditions. Moreover, the energy performance of the systems was studied to obtain information about the energetic effectiveness and convenience of the studied DLCSs. The main results showed that the proposed system could maintain the required target illuminance values on the work plane as daylight conditions vary: the maximum deviation measured using the prototype never exceeded 11 lx. In comparison, the commercial controller reached peaks of 220 lx. Moreover, the energy consumption of the prototype (resulting equal to 370 mVA) was lower than the consumption of the commercial system (equal to 600 mVA), allowing for increased energy savings over the long period. The more straightforward configuration allows the user to better interact with the DLCS.
A Review on the Anaerobic Co-Digestion of Livestock Manures in the Context of Sustainable Waste Management
Authors: Kadam Rahul , Jo Sangyeol , Lee Jonghwa , Khanthong Kamonwan , Jang Heewon , Park Jungyu
Published: January 2024

As the worldwide demand for meat per person is continuously increasing, there is a corresponding rise in the number of livestock animals, leading to an increase in livestock manure. Selecting appropriate treatment technologies for livestock manures is still a complex task and considerable debates over this issue persist. To develop a more comprehensive understanding of the manure treatment framework, this review was undertaken to assess the most utilized manure management technologies and underscore their respective challenges. Anaerobic digestion has become a commercial reality for treating livestock manures. However, the mono-digestion of single substrates comes with certain drawbacks associated with manure characteristics. Anaerobic co-digestion, involving the utilization of multiple feedstocks, holds the potential to overcome these limitations. Extensive research and development have underscored numerous intrinsic benefits of co-digestion. These include improved digestibility resulting from the synergistic effects of co-substrates and enhanced process stability. This review underscores the limitations associated with the mono-digestion of livestock manures and critically evaluates the advantages of their co-digestion with carbon-rich substrates. Additionally, this review delves into key livestock manure management practices globally, emphasizing the significance of co-digesting livestock manures while addressing the progress and challenges in this field.
Research on Voltage Compensation Methods and Optimization Algorithm for Insulated Core Transformer High-Voltage Power Supply
Authors: Yang Lei , Liu Xialing , Yang Jun
Published: January 2024

The insulated core transformer (ICT) power supply is widely employed in electron beam accelerators (EBAs) due to its high power, heightened efficiency, and stable operation. However, the segmented-core structure of the ICT power supply increases magnetic leakage, which leads to it adversely affecting the consistency of the output voltages in the rectifier stages. Currently, numerous studies focus on stage voltage compensation, including turns compensation, capacitor compensation, dummy primary winding compensation, and full-parameter compensation. This paper presents a unified simulation model and an improved gradient-based genetic algorithm, which can also optimize the parameters of the four compensation methods. Based on this, the performance of the power supply using the four compensation methods under different ICT energy levels and power supply requirements is studied, and the selection suggestions are given. This work fills the gap in the performance comparison and application research of various compensation methods.
Energy Production in Microbial Fuel Cells (MFCs) during the Biological Treatment of Wastewater from Soilless Plant Cultivation
Authors: Mielcarek Artur , Bryszewski Kamil Łukasz , Kłobukowska Karolina , Rodziewicz Joanna , Janczukowicz Wojciech
Published: January 2024

The management of drainage water (DW), which is produced during the soilless cultivation of plants, requires a high energy input. At the same time, DW is characterized by a high electrolytic conductivity, a high redox potential, and is also stable and putrefaction-free. In the present study, the natural properties of drainage water and a biotreatment method employing an external organic substrate in the form of citric acid (C/N 1.0, 1.5, 2.0) were utilized for energy recovery by a microbial fuel cell (MFC). The cathode chamber served as a retention tank for DW with a carbon felt electrode fixed inside. In turn, a biological reactor with biomass attached to the filling in the form of carbon felt served as the anode chamber. The filling also played the role of an electrode. The chambers were combined by an ion exchange membrane, forming an H letter-shaped system. They were then connected in an external electrical circuit with a resistance of 1k Ω. The use of a flow-through system eliminated steps involving aeration and mixing of the chambers’ contents. Citric acid was found to be an efficient organic substrate. The voltage of the electric current increased from 44.34 ± 60.92 mV to 566.06 ± 2.47 mV for the organic substrate dose expressed by the C/N ratio ranging from 1.0 to 2.0. At the same time, the denitrification efficiency ranged from 51.47 ± 9.84 to 95.60 ± 1.99% and that of dephosphatation from 88.97 ± 2.41 to 90.48 ± 1.99% at C/N from 1.0 to 2.0. The conducted studies confirmed the possibility of recovering energy during the biological purification of drainage water in a biofilm reactor. The adopted solution only required the connection of electrodes and tanks with an ion-selective membrane. Further research should aim to biologically treat DW followed by identification of the feasibility of energy recovery by means of MFC.
Exploring the Environmental and Performance Implications of Utilizing Waste Swine Oil Biodiesel in CI Engines
Authors: Khujamberdiev Ramozon , Cho Haeng Muk
Published: January 2024

This study investigates the effects of varying waste swine oil biodiesel blends on the emission characteristics and efficiency of a compression ignition (CI) engine. Through a series of controlled experiments, the engine was operated under a constant load of 25% across different speeds ranging from 1200 to 1800 rpm. This study meticulously recorded the emissions of carbon monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM), along with performance metrics, including Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE). The results revealed a clear inverse relationship between biodiesel blend percentage and emissions of CO, HC, and PM. CO emissions decreased from 0.76 at 1200 rpm to 0.22 at 1800 rpm for the B80 blend, compared to pure diesel. Similarly, HC emissions showed a decline from 36 to 20 for the B80 blend. Conversely, CO2 and NOx emissions increased in higher biodiesel blends, with CO2 peaking at 2.9 for the B80 blend and NOx emissions rising from 103 for pure diesel to 165 for the B80 blend. PM emissions also decreased with higher blends, from 15 in pure diesel to 10 in the B80 blend. This comprehensive analysis reveals that while biodiesel significantly reduces specific emissions, it also poses challenges in terms of increased fuel consumption and reduced thermal efficiency. The findings emphasize the need for advanced engine technologies and optimization strategies to harness the full potential of biodiesel as a sustainable and environmentally friendly alternative to diesel.
Optimization of Protease Treatment Conditions for Chlorella pyrenoidosa Protein Extraction and Investigation of Its Potential as an Alternative Protein Source
Authors: Cho Kyung-Jin , Kim Min-Ung , Jeong Geum-Jae , Khan Fazlurrahman , Jo Du-Min , Kim Young-Mog
Published: January 2024

This study aimed to determine enzymes that effectively extract Chlorella pyrenoidosa proteins and optimize the processing conditions using response surface methods. Furthermore, the potential of enzymatically hydrolyzed C. pyrenoidosa protein extract (CPE) as a substitute protein source was investigated. The enzymatic hydrolysis conditions for protein extraction were optimized using single-factor analysis and a response surface methodology–Box–Behnken design. The R2 value of the optimized model was 0.9270, indicating the reliability of the model, and the optimal conditions were as follows: a hydrolysis temperature of 45.56 °C, pH 9.1, and a hydrolysis time of 49.85 min. The amino acid composition of CPE was compared to that of C. pyrenoidosa powder (CP), which was found to have a higher content of essential amino acids (EAA). The electrophoretic profiles of CP and CPE confirmed that CPE has a low molecular weight. Furthermore, CPE showed higher antioxidant activity and phenol content than CP, with ABTS and DPPH radical scavenging abilities of 69.40 ± 1.61% and 19.27 ± 3.16%, respectively. CPE had high EAA content, antioxidant activity, and phenol content, indicating its potential as an alternative protein source. Overall, in this study, we developed an innovative, ecofriendly, and gentle enzymatic hydrolysis strategy for the extraction and refinement of Chlorella proteins.
Changes in Challenging Behaviors Accompanying Transition to a New Facility in Individuals with Intellectual Disabilities
Authors: Kawanaka Mizuho , Iwanaga Yuto , Tokunaga Akiko , Higashi Toshio , Tanaka Goro , Imamura Akira , Iwanaga Ryoichiro
Published: January 2024

Challenging behavior (CB), the most common example being extreme self-injurious or aggressive/destructive behavior, is often observed as a major behavior issue in individuals with severe intellectual disabilities. This study investigated how CB changed among residents of a facility for people with disabilities before and after it was restructured from a traditional format single room shared by two to three individuals with approximately 20 residents lived together to a format featuring private areas with two rooms per resident and a unitcare system. Twenty-one residents of Care Home A, which was rebuilt in the new care format, were selected. Care staff completed a questionnaire one month before, one month after, and six months after residents moved to the new facility. Scores were compared among each time point. The results revealed significant reductions in residents’ aggressive, stereotyped, and targeted behaviors, such as hitting their own head and fecal smearing. The major features of the restructured facility were a living space consisting of two private rooms per resident and a shift to unit care for the entire ward. These new features enabled residents to reduce destructive stimuli and made it easier to understand what to do in each private room.
A Prospective Multicenter Longitudinal Analysis of Suicidal Ideation among Long-COVID-19 Patients
Authors: Costanza Alessandra , Amerio Andrea , Aguglia Andrea , Magnani Luca , Alexander Jacques , Maiorano Alessandra , Richard-Lepouriel Hélène , Portacolone Elena , Berardelli Isabella , Pompili Maurizio , Serafini Gianluca , Amore Mario , Nguyen Khoa
Published: January 2024

Long coronavirus disease 19 (COVID-19) is an emerging multifaceted illness with the pathological hallmarks of chronic inflammation and neuropsychiatric symptoms. These pathologies have also been implicated in developing suicidal behaviors and suicidal ideation (SI). However, research addressing suicide risk in long COVID-19 is limited. In this prospective study, we aim to characterize SI development among long-COVID-19 patients and to determine the predictive power of inflammatory markers and long-COVID-19 symptoms—including those of psychiatric origin—for SI. During this prospective, longitudinal, multicenter study, healthy subjects and long-COVID-19 patients will be recruited from the University Hospital of Geneva, Switzerland, the University of Genova, the University of Rome “La Sapienza”, and the University of San Francisco. Study participants will undergo a series of clinic visits over a follow-up period of 1 year for SI assessment. Baseline and SI-onset levels of inflammatory mediators in plasma samples, along with 12 long-COVID-19 features (post-exertional malaise, fatigue, brain fog, dizziness, gastrointestinal disturbance, palpitations, changes in sexual desire/capacity, loss/change of smell/taste, thirst, chronic cough, chest pain, and abnormal movements) will be collected for SI risk analysis. The proposed enrollment period is from 15 January 2024 to 15 January 2026 with targeted recruitment of 100 participants for each study group. The anticipated findings of this study are expected to provide important insights into suicide risk among long-COVID-19 patients and determine whether inflammation and psychiatric comorbidities are involved in the development of SI in these subjects. This could pave the way to more effective evidence-based suicide prevention approaches to address this emerging public health concern.
Association between Organizational Support and Turnover Intention in Nurses: A Systematic Review and Meta-Analysis
Authors: Galanis Petros , Moisoglou Ioannis , Papathanasiou Ioanna V. , Malliarou Maria , Katsiroumpa Aglaia , Vraka Irene , Siskou Olga , Konstantakopoulou Olympia , Kaitelidou Daphne
Published: January 2024

Although recent studies suggest a negative relationship between organizational support and turnover intention among nurses, there has been no systematic review on this issue. The aim of this systematic review and meta-analysis was to synthesize and evaluate the association between organizational support and turnover intention in nurses. The review protocol was registered with PROSPERO (CRD42023447109). A total of eight studies with 5754 nurses were included. All studies were cross-sectional and were conducted after 2010. Quality was moderate in five studies and good in three studies. We found a moderate negative correlation between organizational support and turnover intention since the pooled correlation coefficient was −0.32 (95% confidence interval: −0.42 to −0.21). All studies found a negative correlation between organizational support and turnover intention ranging from −0.10 to −0.51. A leave-one-out sensitivity analysis showed that our results were stable when each study was excluded. Egger’s test and funnel plot suggested the absence of publication bias in the eight studies. Subgroup analysis showed that the negative correlation between organizational support and turnover intention was stronger in studies in China and Australia than those in Europe. Organizational support has a moderate negative correlation with turnover intention in nurses. However, data regarding the impact of organizational support on turnover intention are limited. Moreover, our study had several limitations, and thus, we cannot generalize our results. Therefore, further studies should be conducted to assess the independent effect of organizational support on turnover intention in a more valid way. In any case, nursing managers should draw attention to organizational support by developing effective clinical practice guidelines for nurses so as to reduce turnover intention.
Techniques and Strategies in Drug Design and Discovery
Authors: Nitulescu George Mihai
Published: January 2024

The process of drug discovery constitutes a highly intricate and formidable undertaking, encompassing the identification and advancement of novel therapeutic entities [...]
The Therapeutic Mechanisms of Mesenchymal Stem Cells in MS—A Review Focusing on Neuroprotective Properties
Authors: Gavasso Sonia , Kråkenes Torbjørn , Olsen Håkon , Evjenth Elisabeth Claire , Ytterdal Marie , Haugsøen Jonas Bull , Kvistad Christopher Elnan
Published: January 2024

In multiple sclerosis (MS), there is a great need for treatment with the ability to suppress compartmentalized inflammation within the central nervous system (CNS) and to promote remyelination and regeneration. Mesenchymal stem cells (MSCs) represent a promising therapeutic option, as they have been shown to migrate to the site of CNS injury and exert neuroprotective properties, including immunomodulation, neurotrophic factor secretion, and endogenous neural stem cell stimulation. This review summarizes the current understanding of the underlying neuroprotective mechanisms and discusses the translation of MSC transplantation and their derivatives from pre-clinical demyelinating models to clinical trials with MS patients.
Affinity Resins for the Isolation of Immunoglobulins G Obtained Using Biocatalytic Technology
Authors: Tereshin Mikhail N. , Melikhova Tatiana D. , Eletskaya Barbara Z. , Ksenofontova Olga B. , Pantyushenko Pavel V. , Berzina Maria Ya. , Ivanov Igor , Myagkikh Igor V. , Stepanenko Vasiliy N.
Published: January 2024

Affinity chromatography resins that are obtained by conjugation of matrices with proteins of bacterial origin, like protein A, are frequently used for the purification of numerous therapeutic monoclonal antibodies. This article presents the development of a biocatalytic method for the production of novel affinity resins with an immobilized mutant form of protein A via sortase A mediated reaction. The conditions for activation of the agarose Seplife 6FF matrix, selection of different types of linkers with free amino groups and conditions for immobilization of recombinant protein A on the surface of the activated matrix were studied. Finally, the basic operational properties, like dynamic binding capacity (DBC), temperature dependance of DBC and stability during the cleaning-in-place process of the affinity resin with the Gly-Gly-EDA-Gly-Gly linker, were assessed using recombinant hyperchimeric monoclonal antibodies. The main characteristics show comparable results with the widely used commercial samples.