bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Chronic Psychostimulant Administration on Bone Health: A Review
Authors: Nowak Jessica , Aronin Jacob , Beg Faraaz , O’Malley Natasha , Ferrick Michael , Quattrin Teresa , Pavlesen Sonja , Hadjiargyrou Michael , Komatsu David E. , Thanos Panayotis K.
Published: August 2024

(1) Background: Methylphenidate (MP) and amphetamine (AMP) are psychostimulants that are widely prescribed to treat Attention Deficit Hyperactivity Disorder (ADHD) and narcolepsy. In recent years, 6.1 million children received an ADHD diagnosis, and nearly 2/3 of these children were prescribed psychostimulants for treatment. The purpose of this review is to summarize the current literature on psychostimulant use and the resulting effects on bone homeostasis, biomechanical properties, and functional integrity. (2) Methods: Literature searches were conducted from Medline/PubMed electronic databases utilizing the search terms “methylphenidate” OR “amphetamine” OR “methylphenidate” AND “bone health” AND “bone remodeling” AND “osteoclast” AND “osteoblast” AND “dopamine” from 01/1985 to 04/2023. (3) Results: Of the 550 publications found, 44 met the inclusion criteria. Data from identified studies demonstrate that the use of MP and AMP results in decreases in specific bone properties and biomechanical integrity via downstream effects on osteoblasts and osteoclast-related genes. (4) Conclusions: The chronic use of psychostimulants negatively affects bone integrity and strength as a result of increased osteoclast activity. These data support the need to take this into consideration when planning the treatment type and duration for bone fractures.
Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction
Authors: Hasan Md Rakibul , Li Qingrui , Saha Utsha , Li Juan
Published: August 2024

Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction.
miRNA Profiles in Patients with Hematological Malignancy at Different Stages of the Disease: A Preliminary Study
Authors: Hashem Jood , Alkhalaileh Lujain , Abushukair Hassan , Ayesh Mahmoud
Published: August 2024

The dysregulation of miRNA expression has been shown to impact cellular physiology and tumorigenesis. Studies have reported several miRNA regulatory elements and pathways that play a significant role in the diagnosis, prognosis, and treatment of hematological malignancies. This is the first study to test the differential expression of miRNAs at crucial stages of the disease, specifically newly diagnosed, resistant to treatment, and remission. Circulating miRNAs extracted from the blood samples of 18 patients diagnosed with leukemia or lymphoma at different stages and 2 healthy controls were quantified by qPCR using a panel of 96 tumorigenic miRNAs. An enrichment analysis was performed to understand the mechanisms through which differential miRNA expression affects cellular and molecular functions. Significant upregulation of hsa-miR-1, hsa-miR-20a-5p, hsa-miR-23a-3p, hsa-miR-92b3p, and hsa-miR-196a-5p was detected among the different stages of leukemia and lymphoma. mir-1 and mir-196a-5p were upregulated in the remission stage of leukemia, while mir-20a-5p, mir-23a-3p, and mir-92b-3p were upregulated during the resistant stage of lymphoma. The enrichment analysis revealed these miRNAs’ involvement in the RAS signaling pathway, TGF-β signaling, and apoptotic pathways, among others. This study highlights new biomarkers that could be used as potential targets for disease diagnosis, prognosis, and treatment, therefore enhancing personalized treatments and survival outcomes for patients.
Complex Interactions between the Human Major Histocompatibility Complex (MHC) and Microbiota: Their Roles in Disease Pathogenesis and Immune System Regulation
Authors: Arnaiz-Villena Antonio , Juarez Ignacio , Vaquero-Yuste Christian , Lledo Tomás , Martin-Villa José Manuel , Suarez-Trujillo Fabio
Published: August 2024

The relationship between microbiota and the immune system is complex and characterized by the ways in which microbiota directs immune function interactions, both innate and acquired and also keeps activating the immune system throughout an individual’s life. In this respect, the human Major Histocompatibility Complex (MHC, referred to as HLA in humans) plays a crucial role and is also established in self-defense against microbes by presenting microbial-derived peptides to the immune cells. However, this assumption has some unclear aspects that should be investigated. For example, how is the microbiota shaped by microbe species diversity, quantity and functions of the immune system, as well as the role and molecular mechanisms of the HLA complex during this process. There are autoimmune diseases related to both HLA and specific microbiota changes or alterations, many of which are mentioned in the present review. In addition, the HLA peptide presenting function should be put in a framework together with its linkage to diseases and also with HLA compatibility necessary for transplants to be successful. These are still quite an enigmatically statistical and phenomenological approach, but no firm pathogenic mechanisms have been described; thus, HLA’s real functioning is still to be fully unveiled. After many years of HLA single-genes studies, firm pathogenesis mechanisms underlying disease linkage have been discovered. Finally, microbiota has been defined as conformed by bacteria, protozoa, archaea, fungi, and viruses; notwithstanding, endogenous viral sequences integrated into the human genome and other viral particles (obelisks) recently found in the digestive mucosa should be taken into account because they may influence both the microbiome and the immune system and their interactions. In this context, we propose to integrate these microbial-genetic particle components into the microbiome concept and designate it as “microgenobiota”.
Effect of Pravastatin on Placental Expression of Epidermal Growth Factor-like Domain 7 in Early-Onset Pre-Eclampsia: A New Potential Mechanism of Action
Authors: Salvi Silvia , Fruci Stefano , Lacconi Valentina , Totaro Aprile Federica , Rullo Roberta , Stuhlmann Heidi , Lanzone Antonio , Campagnolo Luisa , Massimiani Micol
Published: August 2024

The primary intervention for pre-eclampsia (PE) remains iatrogenic delivery, which can be very preterm and not optimal for the fetus. Although many efforts have been made to prevent and manage PE, there is still a dearth of drugs to treat its pathophysiological progression. Pravastatin (PRA), a hydrophilic statin, has gained interest for the prevention and treatment of PE. The aim of the present study was to evaluate the ability of PRA to modulate factors involved in placentation, such as Epidermal Growth Factor-Like Domain 7 (EGFL7), in human chorionic villous culture from healthy controls and women with PE. A total of 18 women were enrolled: 10 controls and 8 cases. Chorionic villous explants were maintained in culture for 24 h with or without 10 μM Pravastatin, and the expression of EGFL7 and NOTCH1 pathway members was evaluated by qRT-PCR and Western blot analysis. The rationale of the present study was to establish an ex vivo model to identify potential different responses to PRA treatment of chorionic villous explants in order to clarify the molecular mechanism of PRA in the prevention and treatment of PE and to predict whether there are specific clinical conditions that modulate the response to the drug treatment. Within PE patients, two different groups were identified: the high responders, whose villous cultures exhibit significantly increased expressions of the EGFL7 and Notch pathways after PRA incubation; and the low responders, who are high-risk PE patients in which prophylaxis failed to prevent PE and PRA was not able to modulate EGFL7 expression. In conclusion, we identified EGFL7 as a new factor regulated by PRA, placing interest in early discrimination between low- and high- risk women, in which the well-known pharmacological prophylaxis seems to be ineffective, and to explore new potential prevention strategies.
Miniaturized Iridium Oxide Microwire pH Sensor for Biofluid Sensing
Authors: Chawang Khengdauliu , Bing Sen , Kwon Ki Yong , Chiao J.-C.
Published: August 2024

pH regulation in human biofluids is a crucial step for disease diagnosis and health monitoring. Traditional pH sensors are limited by their bulky size in wearable systems, and fragile glass tips require frequent calibration, thus limiting their use in continuous monitoring. Flexible sensors, particularly those utilizing microwires and thread-based substrates, present advantages for small sample analysis, including natural breathability and suitability for bandage or textile integration. This study examines iridium oxide and silver–silver chloride coated on thin gold wires, fabricated using sol–gel and dip-coating processes known for their simplicity. The flexible microwires demonstrated promising pH performance from a study of their pH characteristics, sensitivity, hysteresis, and potential drift. Electrodes tested in microwells allowed for small sample volumes and localized pH measurement in a controlled environment. Additional integration into fabrics for sweat sensing in wearables highlighted their potential for continuous, real-time health monitoring applications.
Long-Term Socioeconomic and Neurologic Outcome for Individuals with Childhood-Onset Multiple Sclerosis
Authors: Tacke Moritz , Hannibal Iris , Vill Katharina , Bonfert Michaela V. , Müller-Felber Wolfgang , Blaschek Astrid
Published: August 2024

Intorduction: Most studies on the progression of childhood-onset multiple sclerosis (MS) involve relatively short follow-up periods, focusing primarily on neurological outcomes and disability progression. The influence of these and other factors on the health-related quality of life is not known. To gain a comprehensive understanding of early-onset MS, it is crucial to evaluate the effects of treatment and the disease on quality of life. Method: This pilot project aimed to evaluate the feasibility of using an online survey tool for long-term follow-up data collection from patients with childhood-onset MS. An anonymized, monocentric, prospective survey was conducted on a convenience cohort of patients treated at a certified centre for neuromuscular diseases in childhood between 2007 and 2019. Results: A total of 27 patients completed the survey. There were no mandatory items, therefore some patients chose not to answer all the questions in the questionnaire. Patients exhibited promising educational achievements, low neurological disease burden, and high resilience. However, anxiety, depression, and pain significantly impacted their perceived health status. Conclusion:This single-centre study has yielded new insights into childhood-onset MS. To enable more accurate comparisons across different centres and countries, it is essential to establish a minimum data set and questionnaire subset for patients with paediatric-onset MS transitioning into adulthood.
Exploring Pediatric Vertebral, Sacral, and Pelvic Osteosarcomas through the NCDB: Demographics, Treatment Utilization, and Survival Outcomes
Authors: Jagtiani Pemla , Karabacak Mert , Carr Matthew T. , Bahadir Zeynep , Morgenstern Peter F. , Margetis Konstantinos
Published: August 2024

Background and Objectives: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. Methods: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. Results: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. Conclusions: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes.
Quality of Life in Children with Neurofibromatosis Type 1: Agreement between Parents and Patients, and the Role of Disease Severity and Visibility
Authors: Cavallo Nicola Davide , Maietta Paola , Perrotta Silverio , Moretta Pasquale , Carotenuto Marco , Esposito Maria , Santangelo Gabriella , Santoro Claudia
Published: August 2024

Background: Neurofibromatosis type 1 (NF1) is a genetic disorder that affects multiple systems in the body, often leading to physical disfigurements and a wide range of clinical symptoms. This study aims to investigate the relationship between NF1 severity and visibility and the quality of life (QoL) in children. Methods: The Pediatric Quality of Life Inventory (PedsQL) and a modified version of the Ablon scale were used to assess QoL and NF1 severity and visibility, respectively. Self-reported and parent-reported QoL scores were compared, and the associations between NF1 severity/visibility and QoL were explored. Results: Thirty-eight pediatric NF1 patients and their parents were enrolled. QoL scores did not differ significantly between patient self-reports and parent reports. However, correlational analyses revealed that higher NF1 severity was associated with lower physical QoL in patients, and greater NF1 visibility was linked to lower physical and social QoL. For parents, higher NF1 severity correlated with lower school functioning, whereas NF1 visibility did not show a significant correlation with QoL. Conclusion: The severity and visibility of NF1 have distinct impacts on various aspects of QoL in children, highlighting the need for tailored interventions that address both physical and psychological challenges. These findings underscore the importance of comprehensive care approaches in managing NF1 in pediatric populations.
Investigating the Challenges and Opportunities in Persian Language Information Retrieval through Standardized Data Collections and Deep Learning
Authors: Moniri Sara , Schlosser Tobias , Kowerko Danny
Published: August 2024

The Persian language, also known as Farsi, is distinguished by its intricate morphological richness, yet it contends with a paucity of linguistic resources. With an estimated 110 million speakers, it finds prevalence across Iran, Tajikistan, Uzbekistan, Iraq, Russia, Azerbaijan, and Afghanistan. However, despite its widespread usage, scholarly investigations into Persian document retrieval remain notably scarce. This circumstance is primarily attributed to the absence of standardized test collections, which impedes the advancement of comprehensive research endeavors within this realm. As data corpora are the foundation of natural language processing applications, this work aims at Persian language datasets to address their availability and structure. Subsequently, we motivate a learning-based framework for the processing of Persian texts and their recognition, for which current state-of-the-art approaches from deep learning, such as deep neural networks, are further discussed. Our investigations highlight the challenges of realizing such a system while emphasizing its possible benefits for an otherwise rarely covered language.
Resource Allocation with Karma Mechanisms—A Review
Authors: Riehl Kevin , Kouvelas Anastasios , Makridis Michail A.
Published: August 2024

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource-allocating contexts, money faces social, ethical, and legal constraints. Consequently, artificial currencies and non-monetary markets are increasingly explored, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road map for future research directions.
Multi-Factor Cost Adjustment for Enhanced Export-Oriented Production Capacity in Manufacturing Firms
Authors: Mishrif Ashraf , Hammad Mohamed A.
Published: August 2024

Many manufacturing firms face considerable difficulties in building export capacity and selling their products in international markets. These firms often struggle with unpredictable cost changes, logistical problems along the supply chain, and rising labor expenses that could threaten the competitive edge of manufacturing operations. As there is also a clear absence of practical export models tailored to the unique needs of industrial firms, our study aims to offer a more holistic approach to assessing the impact of cost components on enhancing export-oriented production capacity (EOPC), a perspective not comprehensively provided by the comparative advantage theory, the Heckscher–Ohlin model, or the resource-based theory. While offering a comprehensive analysis of cost components in production, we argue that adjusting the resources, managing the costs, and enhancing production efficiency can significantly improve the EOPC of the manufacturing firms. Using primary data collected from 200 manufacturing firms in Oman during the period 2012–2016, multiple regression analysis followed by descriptive statistical analysis together with a correlation matrix indicates strong positive relationships between the EOPC and factors such as the raw material cost (RMC), labor wages (LW), labor force (LF), and R&D costs (RND). Multicollinearity assessment shows VIF values below the threshold, suggesting reliable estimates. Interaction terms and market conditions were integrated into the model, enhancing its predictive accuracy. Preliminary multiple regression analysis confirms the significant impact of the RMC, LW, LF, and R&D on the EOPC, while highlighting the importance of market conditions in moderating these effects. The model’s adjusted R2 value indicates a strong fit, showing that the independent variables account for a substantial proportion of the variance in the EOPC. Each variable’s importance is reflected in its coefficient, while p-values assess the statistical significance, highlighting which factors are crucial for enhancing export capabilities. Specifically, low p-values for cost components, labor force size, and wages confirm their significant influence, and varying market conditions further modulate these effects, demonstrating the accurate interplay between internal and external factors. Adjustments in cost components under varying market scenarios were analyzed, indicating optimal strategies for increasing the EOPC. Of the five scenarios proposed to distribute the cost either among some variables while keeping others constant or among all the factors, the best-case scenario adjusted all variables together, resulting in a 20% increment in exports. We conclude with some practical and policy implications for governments to support industries in accessing cheap resources through tax reductions on imported raw materials and efficient supply chains, while promoting innovation, technology adoption, and R&D investment at the firm level.
RegMamba: An Improved Mamba for Medical Image Registration
Authors: Hu Xin , Chen Jiaqi , Chen Yilin
Published: August 2024

Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets.
Enhancing Indoor Localization Accuracy through Multiple Access Point Deployment
Authors: Aziz Toufiq , Insoo Koo
Published: August 2024

This study addresses the limitations of wireless local area networks in indoor localization by utilizing Extra-Trees Regression (ETR) to estimate locations based on received signal strength indicator (RSSI) values from a radio environment map (REM). We investigate how integrating numerous access points can enhance indoor localization accuracy. By constructing an extensive REM using RSSI data from various access points collected by a mobile robot in the intended interior setting, we evaluate several machine learning regression techniques. Our research pays special attention to an optimized ETR model, validated through 10-fold cross-validation and hyperparameter tuning. We quantitatively evaluate the efficiency of our suggested multi-access-point approach using root mean square error (RMSE) for REM evaluation and location error metrics for accurate localization. The results show that incorporating multiple access points significantly improves indoor localization accuracy, providing a substantial improvement over single-access-point systems when assessing interior radio frequency environments.
Ancient Painting Inpainting Based on Multi-Layer Feature Enhancement and Frequency Perception
Authors: Liu Xiaotong , Wan Jin , Wang Nan , Wang Yuting
Published: August 2024

Image inpainting aims to restore the damaged information in images, enhancing their readability and usability. Ancient paintings, as a vital component of traditional art, convey profound cultural and artistic value, yet often suffer from various forms of damage over time. Existing ancient painting inpainting methods are insufficient in extracting deep semantic information, resulting in the loss of high-frequency detail features of the reconstructed image and inconsistency between global and local semantic information. To address these issues, this paper proposes a Generative Adversarial Network (GAN)-based ancient painting inpainting method using multi-layer feature enhancement and frequency perception, named MFGAN. Firstly, we design a Residual Pyramid Encoder (RPE), which fully extracts the deep semantic features of ancient painting images and strengthens the processing of image details by effectively combining the deep feature extraction module and channel attention. Secondly, we propose a Frequency-Aware Mechanism (FAM) to obtain the high-frequency perceptual features by using the frequency attention module, which captures the high-frequency details and texture features of the ancient paintings by increasing the skip connections between the low-frequency and the high-frequency features, and provides more frequency perception information. Thirdly, a Dual Discriminator (DD) is designed to ensure the consistency of semantic information between global and local region images, while reducing the discontinuity and blurring differences at the boundary during image inpainting. Finally, extensive experiments on the proposed ancient painting and Huaniao datasets show that our proposed method outperforms competitive image inpainting methods and exhibits robust generalization capabilities.
Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
Authors: Baird Iain , Wadhaj Isam , Ghaleb Baraq , Thomson Craig
Published: August 2024

Mobile ad hoc networks (MANETs) offer a decentralized communication solution ideal for infrastructure-less environments like disaster relief zones. However, their inherent lack of central control and dynamic topology make them vulnerable to attacks. This paper examines the impact of various attacks on mobile nodes within two network types: randomly and uniformly distributed stationary networks. Four types of attacks are investigated: delay, dropping, sinkhole (alone), and a combined black hole attack (dropping + sinkhole). The effects of these attacks are compared using the packet delivery ratio, throughput, and end-to-end delay. The evaluation results show that all single attacks negatively impacted network performance, with the random network experiencing the most significant degradation. Interestingly, the combined black hole attack, while more disruptive than any single attack, affected the uniformly distributed network more severely than the random network.
A Study of Electric Bicycle Lithium Battery Charging Monitoring Using CNN and BiLSTM Networks Model with NILM Method
Authors: Liu Jiameng , Wang Chao , Xu Liangfeng , Wang Mengjiao , Hu Dongfang , Jin Weiya , Li Yuebing
Published: August 2024

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users’ electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA’s lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments.
Distributed Robust Optimization Method for Active Distribution Network with Variable-Speed Pumped Storage
Authors: Pan Pengyu , Chen Gang , Shi Huabo , Zha Xiaoming , Huang Zhiqiang
Published: August 2024

Variable-speed pumped storage has the advantages of flexible adjustment and low economic cost, which can reduce the adverse effects caused by a high proportion of new energy access. In order to reduce the system operation risk caused by the randomness of new energy output, a distribution robust optimization method for an active distribution network with variable-speed pumped storage is proposed. Firstly, considering the probability distribution uncertainty of wind–solar prediction error, a two-stage distributed robust optimization model of an active distribution network is constructed with the sum of day-ahead operating cost, intra-day adjustment cost expectation and conditional value-at-risk as the objective function. Then, the probability distribution fuzzy set is constructed based on the norm distance, and the fuzzy set boundary is determined by a data-driven method. Finally, the model is transformed into a mixed integer second-order cone optimization model by a linearization method and duality theory, and verified by an example. The results show that the proposed model can effectively reduce the operation risk caused by the uncertainty of operating cost and wind–solar output and reduce the operational costs and the risks associated with uncertainty in wind and solar power output.
Zero-Shot Proxy with Incorporated-Score for Lightweight Deep Neural Architecture Search
Authors: Nguyen Thi-Trang , Han Ji-Hyeong
Published: August 2024

Designing a high-performance neural network is a difficult task. Neural architecture search (NAS) methods aim to solve this process. However, the construction of a high-quality accuracy predictor, which is a key component of NAS, usually requires significant computation. Therefore, zero-shot proxy-based NAS methods have been actively and extensively investigated. In this work, we propose a new efficient zero-shot proxy, Incorporated-Score, to rank deep neural network architectures instead of using an accuracy predictor. The proposed Incorporated-Score proxy is generated by incorporating the zen-score and entropy information of the network, and it does not need to train any network. We then introduce an optimal NAS algorithm called Incorporated-NAS that targets the maximization of the Incorporated-Score of the neural network within the specified inference budgets. The experiments show that the network designed by Incorporated-NAS with Incorporated-Score outperforms the previously proposed Zen-NAS and achieves a new SOTAaccuracy on the CIFAR-10, CIFAR-100, and ImageNet datasets with a lightweight scale.
Depth Image Rectification Based on an Effective RGB–Depth Boundary Inconsistency Model
Authors: Cao Hao , Zhao Xin , Li Ang , Yang Meng
Published: August 2024

Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this paper, a simple method is proposed to rectify the erroneous object boundaries of depth images with the guidance of reference RGB images. First, an RGB–Depth boundary inconsistency model is developed to measure whether collocated pixels in depth and RGB images belong to the same object. The model extracts the structures of RGB and depth images, respectively, by Gaussian functions. The inconsistency of two collocated pixels is then statistically determined inside large-sized local windows. In this way, pixels near object boundaries of depth images are identified to be erroneous when they are inconsistent with collocated ones in RGB images. Second, a depth image rectification method is proposed by embedding the model into a simple weighted mean filter (WMF). Experiment results on two datasets verify that the proposed method well improves the RMSE and SSIM of depth images by 2.556 and 0.028, respectively, compared with recent optimization-based and learning-based methods.
Review of Smart-Home Security Using the Internet of Things
Authors: Vardakis George , Hatzivasilis George , Koutsaki Eleftheria , Papadakis Nikos
Published: August 2024

As the Internet of Things (IoT) continues to revolutionize the way we interact with our living spaces, the concept of smart homes has become increasingly prevalent. However, along with the convenience and connectivity offered by IoT-enabled devices in smart homes comes a range of security challenges. This paper explores the landscape of smart-home security. In contrast to similar surveys, this study also examines the particularities of popular categories of smart devices, like home assistants, TVs, AR/VR, locks, sensors, etc. It examines various security threats and vulnerabilities inherent in smart-home ecosystems, including unauthorized access, data breaches, and device tampering. Additionally, the paper discusses existing security mechanisms and protocols designed to mitigate these risks, such as encryption, authentication, and intrusion-detection systems. Furthermore, it highlights the importance of user awareness and education in maintaining the security of smart-home environments. Finally, the paper proposes future research directions and recommendations for enhancing smart-home security with IoT, including the development of robust security best practices and standards, improved device authentication methods, and more effective intrusion-detection techniques. By addressing these challenges, the potential of IoT-enabled smart homes to enhance convenience and efficiency while ensuring privacy, security, and cyber-resilience can be realized.
Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations
Authors: Peng Zhiqiang , Lei Jichong , Ni Zining , Yu Tao , Xie Jinsen , Hong Jun , Hu Hong
Published: August 2024

With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model’s predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations.
Point and Interval Forecasting of Coal Price Adopting a Novel Decomposition Integration Model
Authors: Liu Junjie , Liu Lang
Published: August 2024

Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel decomposition integration model, called VCNQM, to perform point and interval forecasting of coal price by a combination of variational modal decomposition (VMD), chameleon swarm algorithm (CSA), N-BEATS, and quantile regression. Initially, the variational modal decomposition is enhanced by the chameleon swarm algorithm for decomposing the coal price sequence. Then, N-BEATS is used to forecast each subsequence of coal prices, integrating all results to obtain a point forecast of coal prices. Next, interval forecasting of coal prices is achieved through quantile regression. Finally, to demonstrate the superiority of the VCNQM model’s prediction, we make a cross-comparison about predictive performance between the VCNQM model and other benchmark models. According to the experimental findings, we demonstrate the following: after the decomposition by CSA-VMD, the coal price subseries’ fluctuation is significantly weakened; using quantile regression provides a reliable interval prediction, which is superior to point prediction; the predicted interval coverage probability (PICP) is higher than the confidence level of 90%; the share power industry index and coal industry index have the greatest impact on coal prices in China; compared to these benchmark models, the VCNQM model’s prediction errors are all reduced. Therefore, we conclude that when forecasting coal prices, the VCNQM model has an accurate and reliable prediction.
Experimental Investigation of Physicochemical Properties of the Produced Biodiesel from Waste Frying Oil and Its Blend with Diesel Fuel
Authors: Wcisło Grzegorz , Leśniak Agnieszka , Kurczyński Dariusz , Pracuch Bolesław
Published: August 2024

The imperative of utilising alternative fuels for the operation of internal combustion engines stems from the requirements to reduce the emissions of greenhouse gases and other contaminants, the substantial demand for fuels, and the diminishing reserves of natural resources. The global inclination towards sustainable development necessitates the employment of biofuels as a substitute for fossil fuels. Nonetheless, the expenditures on raw materials for the manufacture of biodiesel remain substantial, thus underlining the importance of exploring solutions for reducing them. An instance of this could be the utilisation of plant and animal by-products, such as used frying oils and slaughterhouse waste, as feedstock for biodiesel production. Not only will this facilitate the creation of less costly biofuel, but it will also provide an effective solution for the management of post-production waste. The objective of the research delineated in this paper was to ascertain select physicochemical attributes of second-generation biodiesel, derived from spent frying oil, as well as mixtures of this biodiesel with diesel and biodiesel concentrations of 10, 20, and 30% (v/v). The biodiesel produced is the waste frying oil methyl esters WFOME. The proprietary GW-201 reactor was employed in the production of biodiesel. For WFOME biodiesel, DF diesel, and their blends—B10, B20, and B30—properties that influence the formation process of the combustible mixture, autoignition, and combustion of fuel–air mixtures in self-ignition engines were determined. The conducted research has established that “B” type fuels prepared from WFOME and DF present a viable alternative to fossil fuels. Pure biodiesel exhibited a marginally reduced lower heating value, however, in the case of fuel mixtures comprising up to 30% (v/v) biodiesel and diesel, the lower heating values approximated that of diesel. An elevated cetane number alongside an increased flash point of pure B100 biodiesel have been noted. The values of cetane number for WFOME and DF mixtures were found to be either comparable or marginally higher than those of pure DF diesel fuel.
Size-Dispersed Calcium Phosphate-Based Paints for Sustainable, Durable Cool Roof Applications
Authors: Caratenuto Andrew , Leung Sunny , LeCompte Nathaniel , Zheng Yi
Published: August 2024

Passive radiative cooling materials are widely recognized as attractive innovations for reducing emissions and expanding life-saving cooling access. Despite immense research attention, the adoption of such technologies is limited largely due to a lack of scalability and cost compatibility with market needs. While paint and coating-based approaches offer a more sensible solution, many demonstrations suffer from issues such as a low solar reflectance performance or a lack of material sustainability due to the use of harmful solvents. In this work, we demonstrate a passive radiative cooling paint which achieves an extremely high solar reflectance value of 98% using a completely water-based formulation. Material sustainability is promoted by incorporating size-dispersed calcium phosphate biomaterials, which offer broadband solar reflectance, as well as a self-crosslinking water-based binder, providing water resistance and durability without introducing harmful materials. Common industry pigments are integrated within the binder for comparison, illustrating the benefit of finely-tuned particle size distributions for broadband solar reflectance, even in low-refractive-index materials such as calcium phosphates. With scalability, outdoor durability, and eco-friendly materials, this demonstrated paint offers a practical passive radiative cooling approach without exacerbating other environmental issues.
Study on the Gas Phase Liquid Carrying Velocity of Deep Coalbed Gas Well with Atomization Assisted Production
Authors: Wu Ruidong , Wang Haidong , Song Gangxiang , Duan Dongping , Zhang Chunguang , Zhu Wenjuan , Liu Yikun
Published: August 2024

In order to clarify the gas-phase carrying capacity after the atomization of water from the bottom of deep coalbed wells, considering characteristics of atomization-assisted production and the dynamic equilibrium principle of gas–liquid two-phase flow in the wellbore, the gas-phase liquid-carrying drop model was established, and the solution method of the upstream and downstream driving force of liquid drop flow was studied. We also verified the theoretical model through physical simulation. Then, the law for the influence of droplet size, wellbore inclination, wellbore diameter, and wellhead back pressure of the critical liquid-carrying velocity in the gas phase is analyzed using the model. The results show the following: ① the larger the diameter of atomized droplets, the greater the gravity force applied to it, the worse the ability to be carried by the gas phase, a onefold increase in droplet diameter corresponds to the increase in the minimum critical velocity of the gas phase by 1.45 times; ② with the increase in wellbore inclination, the liquid-carrying capacity of the gas phase decreases, and the minimum critical liquid-carrying velocity of equal diameter droplets increases by 0.01438 m/s or 1.27 times for the increase in wellbore inclination by 10°; ③ with the increase in wellbore diameter, both the driving force of a droplet of equal diameter and the flow resistance through the gas phase in the wellbore decrease within the range of a driving pressure difference of 0.2 Mpa; the decrease in liquid-carrying velocity caused by the decrease in received flow resistance can reach the maximum value of 0.0473 m/s; ④ with the increase in wellhead back pressure, the driving force of equal-diameter droplets decreases, the resistance against passing through the high-concentration gas phase increases, and the gas-phase-carrying droplets start the game; ⑤ the atomization-assisted production has the function of drainage gas recovery, and the adoption of atomization-assisted production technology can increase the production time of a coalbed gas flowing well, enabling the wells to have a good transition time interval for the conversion of flowing wells to pumping ones, which provides a precise production dynamic basis for the efficient design and implements the overall strategy of drainage gas recovery by deep-well pumping. In short, this technology has the high-efficiency liquid-carrying function of “water atomization to help liquid-phase flow and increase gas production”, as well as obvious technical advantages, which can provide a new idea for the development of deep coalbed methane wells and other types of gas wells with water.
A Cost-Effective Energy Management Approach for On-Grid Charging of Plug-in Electric Vehicles Integrated with Hybrid Renewable Energy Sources
Authors: Bilal Mohd , Bokoro Pitshou N. , Sharma Gulshan , Pau Giovanni
Published: August 2024

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations.