bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of In Situ Refrigeration Cycle Measurement Method Using Air-Side Data of Air Source Heat Pump
Authors: Lee Han-Gyeol , Kim Hyo-Jun , Cho Young-Hum
Published: August 2023

The refrigeration cycle of an air source heat pump system is an important information that reveals critical operating data, such as the cooling capacity, power consumption, and performance of a system during operation. Operating data, such as refrigerant pressure and enthalpy in situ, can be difficult to measure. Therefore, this study developed an in situ refrigeration cycle measurement method using the airside data of an air source heat pump. A method for measuring the refrigeration cycle is proposed using the characteristics of evaporation, compression, condensation, and expansion processes. The distance function was analyzed by normalizing the difference between the refrigerant pressure and enthalpy of the existing and proposed measurement methods. In addition, the distance function for the maximum error of the pressure and enthalpy measurement devices was analyzed and compared with the distance function of the entire data used in the evaluation. All the evaluation data had low distance function values with a maximum difference of 5%, confirming the reliability of the proposed refrigeration cycle measurement method. The power consumption and calculated COP were also evaluated using the proposed method. The Mean Bias Error (MBE) of power consumption and COP were 0.15% and 0.04%, and the coefficient of variation of root-mean-square error (CvRMSE) was 8.967% and 7.14%, respectively.
Short-Term Net Load Forecasting for Regions with Distributed Photovoltaic Systems Based on Feature Reconstruction
Authors: Zheng Xudong , Yang Ming , Yu Yixiao , Wang Chuanqi
Published: August 2023

Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model’s ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks.
A Semantic Framework for Decision Making in Forest Fire Emergencies
Authors: Masa Panagiota , Kintzios Spyridon , Vasileiou Zoe , Meditskos Georgios , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: August 2023

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the available information in order to extract useful insights and knowledge to facilitate advanced whereas and support human operators and authorities in a real operational scenario is a challenge. In this work, we present ONTO-SAFE, an ontology-based framework for wildfire events, adopting Semantic Web technologies for data integration and infusion of domain and background knowledge. More specifically, the framework creates a unified representation of the available assets, taking into account data generated from different sources, such as sensors, weather forecasts, earth observations, etc. To this end, previously existing ontologies and standards are used, such as Empathi and EmergencyFire ontology, to provide the conceptual model and the necessary level of abstraction in the form of interconnected knowledge graphs to satisfy the modeling requirements. On top of the generated knowledge graphs, a declarative framework extracts facts and higher-level inferred knowledge from asserted data to support users in decision making. In addition, the framework supports the generation of recommendations, such as sharing important wildfire information with citizens and professionals, that can be adjusted based on user-defined factors and the current disaster risk management phase.
An Intelligent Approach to the Unit Nesting Problem of Coil Material
Authors: Qi Dezhong , Yang Wenguang , Ding Lu , Wu Yunzhi , Tian Chen , Yuan Lifeng , Wang Yuanfang , Huang Zhigao
Published: August 2023

With the popularization of small batch production, the main cutting method for sheet metal parts has changed. Laser cutting has become the main production method for coil material cutting. Developing an irregular part nesting method for the continuous cutting of coil material is urgent. Based on the coil material cutting process, this paper proposes an intelligent approach for the unit nesting problem of coil material. Firstly, a unit nesting model of coil material was constructed. Secondly, an intelligent approach using an improved marine predator algorithm was used to solve this model. In solving the model, the minimum nesting unit was continuously updated by changing the position, angle, and quantity of the nesting parts. Thirdly, the geometric characteristics of this minimum nesting unit were extracted. Finally, the nesting units for production were obtained using a single row or opposite row of the minimum nesting unit. The computational results and comparison prove that the presented approach is feasible and effective in improving material utilization, reducing production costs, and meeting the requirements of the production site.
Antibiotic Resistance Patterns of Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa Isolated from Hospital Wastewater
Authors: Karungamye Petro , Rugaika Anita , Mtei Kelvin , Machunda Revocatus
Published: August 2023

Antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in treated hospital wastewater effluents constitute a major environmental and public health concern. The aim of this study was to investigate the antibiotic resistance patterns of Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa isolated from wastewater effluent at the Benjamin Mkapa Hospital (BMH) in Dodoma, Tanzania. These bacteria were selected to represent the most prevalent gram-negative bacteria found in hospital wastewater, and they have the potential to generate resistance and spread resistance genes to antibiotics. The wastewater BMH is treated in a Constructed Wetland (CW) planted with Typha latifolia before being released into the environment. The bacteria were isolated from wastewater effluent collected at the outlet of the CW. Isolated bacteria were analyzed for antibiotic resistance by disc diffusion method. Molecular identification of bacterial species was performed by using 16S rRNA. The results show that Klebsiella ssp. was the most common isolate detected, with a prevalence of 39.3%, followed by E. coli (27.9%) and Pseudomonas ssp. (18.0%). Klebsiella ssp. were more resistant than Pseudomonas ssp. for Tetracycline, Gentamycin, Ciprofloxacin, and Sulfamethoxazole. Pseudomonas ssp. were more resistant than Klebsiella ssp. for Ceftriaxone and Azithromycin. Klebsiella ssp. harbored more resistance genes (40%), followed by Pseudomonas ssp. (35%) and E. coli (20%). The findings of this investigation indicate that the effluent from the CW requires additional treatment to reduce discharged ARB and ARGs in the receiving water bodies. As a result, the effluent quality of the CW should be continuously monitored and assessed, and further developments for treating the final effluent are necessary.
Research on Solid Oxide Fuel Cell System Model Building and 3D Testing Based on the Nodal Idea
Authors: Li Mingfei , Zhu Kanglin , Rao Mumin , Chen Zhengpeng , Xiong Kai , Hou Longtong , Wang Xiabin , Chen Chuangting , Li Shujun , Li Xi
Published: August 2023

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATLAB/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor’s inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system’s response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system’s efficiency and performance.
Vestibular and Oculomotor Findings in Vestibular Migraine Patients
Authors: Waissbluth Sofia , Sepúlveda Valeria , Leung Jai-Sen , Oyarzún Javier
Published: August 2023

Background: Vestibular migraine (VM) is the most frequent etiology of recurrent spontaneous episodic vertigo. Vestibular and oculomotor abnormalities have been described in VM; however, the diagnosis is currently based on symptoms. The objective of this study was to determine the most frequent abnormalities in videonystagmography (VNG), caloric testing (Cal) and video head impulse test (vHIT) in patients with VM. Methods: A retrospective cohort study was conducted, including all VM and probable VM patients seen from January 2021 to July 2022. Demographics, auditory symptoms and results via VNG, Cal and vHIT were evaluated. VNG results were compared with a control group. Results: Sixty patients, 81.7% with VM and 18.3% with probable vestibular migraine, were included. VNG revealed the following abnormalities: 21.7% spontaneous nystagmus; 33.3% positional nystagmus, mostly central; 26.7% optokinetic nystagmus; 56.7% smooth pursuit abnormalities and 70% saccade test abnormalities, mostly velocity and latency. An abnormal unilateral caloric response was seen in 22.9%, while vHIT revealed a low gain in at least one canal in 21.7%, and saccades were seen in at least one canal with normal gains in 18.3%. Concordant results between Cal and lateral vHIT were seen in 77.1% of cases. Conclusions: Although VM is a clinical diagnosis, vestibular and oculomotor abnormalities are commonly seen. The most frequent oculomotor findings were an abnormal saccade test, abnormal smooth pursuit and central positional nystagmus.
Nonlinear Disturbance Observer-Based Bearing-Only Unmanned Aerial Vehicle Formation Control
Authors: Ding Can , Zhang Jing , Zhang Zhe
Published: August 2023

This article primarily investigates nonlinear disturbance observer-based bearing-only formation tracking control for unmanned aerial vehicle (UAV) systems that encounter uncertainties and disturbances. The employed distributed control strategy relies solely on the relative bearing information of neighboring UAVs. To tackle the challenges posed by unknown disturbances and system uncertainties, a novel nonlinear disturbance observer is proposed to effectively mitigate their impact. Moreover, the issue of unknown controller orientation arising from controller singularities is addressed by introducing a Butterworth low-pass filter. This filter ensures a consistent controller gain and enhances disturbance suppression, ultimately transforming the controller gain function into a constant value of 1. Subsequently, a bearing-only formation tracking controller is developed using the backstepping control approach. The stability of the closed-loop control systems is rigorously proven using Lyapunov theory. Finally, numerical simulations are conducted to validate the effectiveness of the proposed scheme in achieving formation control objectives.
A Soft-Reference Breast Ultrasound Image Quality Assessment Method That Considers the Local Lesion Area
Authors: Wang Ziwen , Song Yuxin , Zhao Baoliang , Zhong Zhaoming , Yao Liang , Lv Faqin , Li Bing , Hu Ying
Published: August 2023

The quality of breast ultrasound images has a significant impact on the accuracy of disease diagnosis. Existing image quality assessment (IQA) methods usually use pixel-level feature statistical methods or end-to-end deep learning methods, which focus on the global image quality but ignore the image quality of the lesion region. However, in clinical practice, doctors’ evaluation of ultrasound image quality relies more on the local area of the lesion, which determines the diagnostic value of ultrasound images. In this study, a global–local integrated IQA framework for breast ultrasound images was proposed to learn doctors’ clinical evaluation standards. In this study, 1285 breast ultrasound images were collected and scored by experienced doctors. After being classified as either images with lesions or images without lesions, they were evaluated using soft-reference IQA or bilinear CNN IQA, respectively. Experiments showed that for ultrasound images with lesions, our proposed soft-reference IQA achieved PLCC 0.8418 with doctors’ annotation, while the existing end-to-end deep learning method that did not consider the local lesion features only achieved PLCC 0.6606. Due to the accuracy improvement for the images with lesions, our proposed global–local integrated IQA framework had better performance in the IQA task than the existing end-to-end deep learning method, with PLCC improving from 0.8306 to 0.8851.
The Fungal Root Endophyte Serendipita indica (Piriformospora indica) Enhances Bread and Durum Wheat Performance under Boron Toxicity at Both Vegetative and Generative Stages of Development through Mechanisms Unrelated to Mineral Homeostasis
Authors: Kaval Ali , Yılmaz Halil , Tunca Gedik Sedef , Yıldız Kutman Bahar , Kutman Ümit Barış
Published: August 2023

While the importance of beneficial soil microorganisms for soil health and crop performance has been receiving ever-increasing attention, Serendipita indica has been widely studied as a fungal root endophyte with significant potential for increasing the stress tolerance of host plants. Boron (B) toxicity as an adverse soil condition is particularly prevalent in arid and semi-arid regions and threatens crop production. Studies on S. indica-wheat symbiosis are limited, and effects of S. indica on crops have never been reported in the context of B toxicity. Here, two pot experiments were conducted under greenhouse conditions to investigate the effects of S. indica on the growth and yield parameters of bread (Triticum aestivum) and durum wheat (Triticum durum) grown at different levels of B toxicity in native vs. sterilized soil, and parameters related to root colonization, membrane damage, oxidative stress, chlorophyll, and mineral nutrition were measured to elucidate the physiological mechanisms of damage and benefit. Boron toxicity decreased early vegetative growth and grain yield, but it did not affect the straw dry weight of mature plants, whereas S. indica significantly enhanced the vegetative growth, straw dry weight, and the grain number of both wheat species. Membrane damage as demonstrated by increased lipid peroxidation and relative electrolyte leakage was caused by B toxicity and alleviated by S. indica. The benefits provided by S. indica could not be attributed to any significant changes in tissue concentrations of B or other minerals such as phosphorus. Soil sterilization generally improved plant performance but it did not consistently strengthen or weaken the effects of S. indica. The presented results suggest that S. indica may be used as an effective microbial inoculant to enhance wheat growth under adverse soil conditions such as B toxicity through mechanisms that are possibly unrelated to mineral homeostasis.
Non-Invasive Retinal Blood Vessel Wall Measurements with Polarization-Sensitive Optical Coherence Tomography for Diabetes Assessment: A Quantitative Study
Authors: Afsharan Hadi , Silva Dilusha , Joo Chulmin , Cense Barry
Published: August 2023

Diabetes affects the structure of the blood vessel walls. Since the blood vessel walls are made of birefringent organized tissue, any change or damage to this organization can be evaluated using polarization-sensitive optical coherence tomography (PS-OCT). In this paper, we used PS-OCT along with the blood vessel wall birefringence index (BBI = thickness/birefringence2) to non-invasively assess the structural integrity of the human retinal blood vessel walls in patients with diabetes and compared the results to those of healthy subjects. PS-OCT measurements revealed that blood vessel walls of diabetic patients exhibit a much higher birefringence while having the same wall thickness and therefore lower BBI values. Applying BBI to diagnose diabetes demonstrated high accuracy (93%), sensitivity (93%) and specificity (93%). PS-OCT measurements can quantify small changes in the polarization properties of retinal vessel walls associated with diabetes, which provides researchers with a new imaging tool to determine the effects of exercise, medication, and alternative diets on the development of diabetes.
Liquid Metal-Based Flexible Bioelectrodes for Management of In-Stent-Restenosis: Potential Application
Authors: Zhang Xilong , Li Lei , Deng Zhongshan
Published: August 2023

Although vascular stents have been widely used in clinical practice, there is still a risk of in-stent restenosis after their implantation. Combining conventional vascular stents with liquid metal-based electrodes with impedance detection, irreversible electroporation, and blood pressure detection provides a new direction to completely solve the restenosis problem. Compared with conventional rigid electrodes, liquid metal-based electrodes combine high conductivity and stretchability, and are more compliant with the implantation process of vascular stents and remain in the vasculature for a long period of time. This perspective reviews the types and development of conventional vascular stents and proposes a novel stent that integrates liquid metal-based electrodes on conventional vascular stents. This vascular stent has three major functions of prediction, detection and treatment, and is expected to be a new generation of cardiovascular implant with intelligent sensing and real-time monitoring.
Effects of Early Adverse Life Events on Depression and Cognitive Performance from the Perspective of the Heart-Brain Axis
Authors: Xia Yujie , Wang Gaohua , Xiao Ling , Du Yiwei , Lin Shanshan , Nan Cai , Weng Shenhong
Published: August 2023

Early adverse life events (EALs) increase susceptibility to depression and impair cognitive performance, but the physiological mechanisms are still unclear. The target of this article is to clarify the impact of adverse childhood experiences on emotional and cognitive performance from the perspective of the heart–brain axis. We used the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to test cognitive function and the Childhood Trauma Questionnaire (CTQ) to assess adverse childhood experiences. Heart rate variability (HRV) and electroencephalograms (EEG) were acquired at rest. We observed that subjects with depression had experienced more traumatic events during their childhood. Furthermore, they exhibited lower heart rate variability and higher power in the delta, theta, and alpha frequency bands. Moreover, heart rate variability partially mediated the association between childhood trauma exposure and depressive symptoms. Our findings suggested that adverse life events in childhood could influence the development of depression in adulthood, which might be linked to cardiac autonomic dysfunction and altered brain function.
Effects of the Fear of COVID-19 and Efficacy of Coping Behavior for Infectious Diseases after the End of COVID-19: Moderating Effects of Cyberchondria and eHealth Literacy
Authors: Jeong Goo-Churl , Lee Kunho , Jin Yinghua
Published: August 2023

As the coronavirus disease (COVID-19) pandemic ends, it is worth considering whether the ability to cope with such a pandemic has improved. The initial response to COVID-19 was hampered by the fear of new infectious diseases and spread of misinformation on the Internet. This highlights the need to enhance our ability to critically evaluate information rather than indiscriminately search for and trust information on the Internet. Therefore, this study examined how cyberchondria and eHealth literacy moderate the effect of fear of COVID-19 on the efficacy of coping behaviors for future epidemics and pandemics. The participants were 1000 adults in South Korea who were selected based on population proportionality. The results showed that fear of COVID-19 was significantly positively related to cyberchondria, and eHealth literacy was significantly positively related to the efficacy of coping behaviors. Further, the fear of COVID-19 had a significantly negative effect on the efficacy of coping behaviors, and the moderating effect of cyberchondria varied according to the level of eHealth literacy. These results emphasize the importance of eHealth literacy in enabling critical decision-making regarding misinformation.
Effects of Graphene Oxide on the Properties of Engineered Cementitious Composites: Multi-Objective Optimization Technique Using RSM
Authors: Bheel Naraindas , Mohammed Bashar S. , Abdulkadir Isyaka , Liew M. S. , Zawawi Noor Amila Wan Abdullah
Published: August 2023

Despite the excellent ductility and energy absorption properties of engineered cementitious composites (ECCs), a low modulus of elasticity and excessive drying shrinkage remain some of its major disadvantages. With the current trend in the application of nanotechnology in cementitious composites research, the effect of graphene oxide (GO) on the properties of ECCs is yet to be fully investigated, despite its promising results in ordinary cement paste, mortar, and concrete. ECCs need extensive material tailoring to provide the required mechanical characteristics and controlled fracture size with strain-hardening behavior. Striking a balance between these crucial hardened aspects of ECC without compromising any desired properties is a challenge. Hence, the main aim of the study reported in this paper is to use the response surface methodology (RSM) multi-objective optimization technique to identify an appropriate GO content via the weight of cement and also the volume fraction of polyvinyl alcohol (PVA) fiber as input variables that positively impact ECCs’ properties. Using RSM’s central composite design (CCD), 13 mixtures of various combinations of the variables (GO: 0.05%, 0.065%, 0.08%; PVA: 1%, 1.5%, 2%) were developed. Six responses were studied, including compressive strength, direct tensile strength, tensile capacity, flexural strength, modulus of elasticity, Poisson’s ratio, and drying shrinkage. Moreover, the microstructural properties of the composites were assessed using field-emission scanning electron microscopy (FESEM) and mercury intrusion porosimetry (MIP). The outcomes revealed that all the properties of ECCs were significantly enhanced by adding an optimum amount of 0.05% GO and 1–1.5% PVA fiber volume fractions. A maximum increase in 30%, 35%, 49%, and 33.9% in the compressive strength, direct tensile strength, flexural strength, and modulus of elasticity, respectively, of the mixes with 0.05% GO addition was recorded. It is demonstrated that the use of 0.05% GO as a nanoscale particle can provide good outputs for the construction industry.
Net-Zero Energy Consumption Building in China: An Overview of Building-Integrated Photovoltaic Case and Initiative toward Sustainable Future Development
Authors: Li Yunlong , Mao Yan , Wang Weizheng , Wu Na
Published: August 2023

Carbon-neutral strategies have become the focus of international attention, and many countries around the world have adopted building-integrated photovoltaic (BIPV) technologies to achieve low-carbon building operation by utilizing power-generating building materials to generate energy in buildings. The purpose of this study is to review the basic status of the development of building-integrated photovoltaic (BIPV) technologies in China, to identify and analyze the existing problems and challenges, and to propose optimization strategies and methods so as to better promote the overall development of green buildings and net-zero energy consumption buildings in China and the world. Primarily, the research area of BIPV is focused on the Chinese region through a case study approach. Subsequently, it elaborates on the theoretical basis of zero-net energy buildings and BIPV as well as the current status of the construction of the world’s low-carbon building standard system, and it summarizes the annual electricity generation of zero carbon buildings adopting BIPV in some European countries. Then, the article further quantitatively and comprehensively analyzes six successful BIPV application cases in China, and it graphically and visually evaluates and demonstrates the average annual net-zero energy performance percentage of the application cases by using the EPI evaluation and measurement tool. At the same time, based on the results of the above assessment, the challenges facing the development of BIPV in China are summarized, and specific incentives for new BIPVs are proposed to address the challenges as well as strategic approaches to optimize BIPVs that are applicable to China as well as Europe and the US. Ultimately, it is concluded that several classic BIPV building cases have achieved essentially 100% net-zero energy operation and maintenance with significant reductions in CO2 emissions and savings of tens of thousands of tonnes of coal consumption. This shows that BIPV technology is gradually developing and maturing in China, and there are great advantages and incremental development space for promoting BIPV in China in the future. The application and promotion strategy of its results in China is also applicable to other countries in the world. It is hoped that based on this experience, countries around the world will implement the “carbon neutral” strategy and zero-net energy consumption development more efficiently and with higher quality so as to realize a greener and cleaner future.
From Circulating Tumor Cells to Mirna: New Challenges in the Diagnosis and Prognosis of Medullary Thyroid Cancer
Authors: Ricci Claudia , Salvemini Antonia , Dalmiglio Cristina , Castagna Maria Grazia , Cantara Silvia
Published: August 2023

Medullary thyroid carcinoma (MTC) is a malignant tumor that arises from parafollicular C cells, which are responsible for producing calcitonin. The majority (75%) of MTC cases are sporadic forms, while the remaining (25%) have a hereditary component. In these hereditary cases, MTC can occur in conjunction with other endocrine disorders (i.e., pheochromocytoma) or as an isolated condition known as familial medullary thyroid carcinoma. The primary genetic mutation associated with the development of MTC, regardless of its hereditary or sporadic nature, is a point mutation in the RET gene. Evaluation of serum calcitonin levels represents the most reliable and sensitive marker for both the initial diagnosis and the postsurgical monitoring of MTC. Unfortunately, most patients do not achieve normalization of postsurgical serum calcitonin (CT) levels after surgery. Therefore, there is a need to find new biomarkers to be used with serum CT in order to increase test sensitivity and specificity. In this review, we summarize the literature from 2010 to 2023 to review the role of circulating tumor cells, cell-free DNA, and miRNA and their application in diagnosis, outcome of MTC, and response to treatments.
Bitcoin’s Carbon Footprint Revisited: Proof of Work Mining for Renewable Energy Expansion
Authors: Ibañez Juan Ignacio , Freier Alexander
Published: August 2023

While blockchain and distributed ledger technology offer immense potential for applications in transparency, security, efficiency, censorship resistance, and more, they have been criticized due to the energy-intensive nature of the proof of work consensus algorithm, particularly in the context of Bitcoin mining. We systematically explore the state-of-the-art regarding the relationship between Bitcoin mining and grid decarbonization. We specifically focus on the role of flexible load response through proof of work mining as a potential contributor to renewable energy penetration and net decarbonization of the energy grid. The existing literature has not comprehensively examined this area, leading to conflicting views. We address the gap, analyzing the capabilities and limitations of Bitcoin mining in providing flexible load response services. Our findings show that renewable-based mining could potentially drive a net-decarbonizing effect on energy grids, although key adaptations in mining practices are needed to fully realize this potential. Overall, the paper suggests a re-evaluation of the environmental impact of Bitcoin mining, highlighting its potential role as a facilitator for renewable energy expansion, and decarbonization more broadly.
Corrosion of Silica-Based Optical Fibers in Various Environments
Authors: Leong Amanda , Rountree Steven Derek , Zhang Jinsuo
Published: August 2023

This research article explores the potential of optical fibers as sensors, highlighting their ability to measure various parameters such as temperature, pressure, stress, and radiation dose. The study focuses on investigating the material compatibility of optical fibers in challenging sensing environments like Gen II/II+ and advance nuclear reactors, as well as concentrated solar power (CSP) plants. Material compatibility tests were conducted to determine the feasibility of using fluorine and germanium optical fiber sensors in these environments. The study found that raw fibers were corrosion-resistant to lead bismuth eutectic at 600 °C, regardless of the coating. In molten salt environments, raw fibers were incompatible with FLiNaK but showed corrosion resistance to MgCl₂-NaCl-KCl. However, the survivability of raw fiber optics improved with a gold coating in FLiNaK. Raw fiber optics were found to be incompatible in high-temperature steam at 1200 °C and in a pressurized water reactor (PWR) at 300 °C.
A Survey of Changes in the Psychological State of Individuals with Social Withdrawal (hikikomori) in the Context of the COVID Pandemic
Authors: Ogawa Takafumi , Shiratori Yuki , Midorikawa Haruhiko , Aiba Miyuki , Sugawara Daichi , Kawakami Naoaki , Arai Tetsuaki , Tachikawa Hirokazu
Published: August 2023

Background: The coronavirus disease (COVID) 2019 pandemic has been reported to have resulted in psychological disturbances. The Japanese term “hikikomori” refers to a state of preferring to stay at home. The COVID pandemic provided an opportunity to extend our current understanding of hikikomori by examining the psychological states of individuals who were in this state under lockdown, during which, paradoxically, their condition may have been adaptive. Methods: We administered a questionnaire to examine psychological changes among 600 people with hikikomori traits in Japan. The Hospital Anxiety and Depression Scale (HADS), Subjective Stress Scale, and Stigma Questions for hikikomori were administered retrospectively at three time points. We also collected descriptive data regarding the participants’ coping strategies. Results: The participants’ sense of stigma regarding hikikomori was improved during the pandemic, whereas depression and anxiety worsened. The participants with ‘definite’ hikikomori (they met the diagnostic criteria) reported more severe depression and anxiety than those with ‘possible’ hikikomori. Their coping strategies were adaptive to the pandemic situation. Conclusion: Although the sense of stigma against hikikomori was improved and adaptive strategies were employed, the participants with hikikomori experienced a worsening of depression and anxiety during the COVID lockdowns. The improvement of stigma and the participants’ indoor adaptive coping strategies could not ameliorate the mental state of hikikomori. Therapeutic interventions should be considered in the future for definite hikikomori meeting the criteria.
Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals
Authors: Aldawsari Haya , Al-Ahmadi Saad , Muhammad Farah
Published: August 2023

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.
Exploring the Impact of the Video Game Monité on Exogenous Factors and Resilience against Bullying in Primary Education Students
Authors: Revuelta Domínguez Francisco-Ignacio , Guerra-Antequera Jorge , Antequera-Barroso Juan-Antonio , Pedrera-Rodríguez María-Inmaculada
Published: August 2023

This study focuses on the issue of school bullying and explores the potential of video games as a tool to prevent and address this problem. To accomplish this, the video game Monité, specifically designed for the prevention of school bullying, was utilized, using the paradigm of digital game-based learning as an intervention methodology that emphasizes its educational and recreational potential. This study was conducted using a quasi-experimental approach, employing a pre-test and post-test design with a non-equivalent control group. The sample consisted of 92 boys and girls from fifth and sixth grade in primary education in the province of Cáceres, Spain. Data were collected through questionnaires from the System for the Evaluation of Children and Adolescents (SENA), which allowed for the gathering of information on peer problems, school problems and family issues. The results show that there are significant differences between the intervention group and the control group regarding problems external to individuals. In conclusion, this study investigates the use of the video game Monité as a tool to prevent school bullying and analyzes its impact on students’ perception of external agents such as family, peer group, and school. Our findings support its implementation in educational environments as an effective strategy.
An Intelligent Robust Operator-Based Sliding Mode Control for Trajectory Tracking of Nonlinear Uncertain Systems
Authors: Zhang Qiang , Liu Ping , Deng Quan , Tong Angxin , Pannek Jürgen
Published: August 2023

This paper investigates the problem of trajectory tracking control in the presence of bounded model uncertainty and external disturbance. To cope with this problem, we propose a novel intelligent operator-based sliding mode control scheme for stability guarantee and control performance improvement in the closed-loop system. Firstly, robust stability is guaranteed by using the operator-based robust right coprime factorization method. Secondly, in order to further achieve the asymptotic tracking and enhance the responsiveness to disturbance, a finite-time integral sliding mode control law is designed for fast convergence and non-zero steady-state error in accordance with Lyapunov stability analysis. Lastly, the controller’s parameters are automatically adjusted by the proved stabilizing particle swarm optimization with the linear time-varying inertia weight, which significantly saves tuning time with a remarkable performance guarantee. The effectiveness and efficiency of the proposed method are verified on a highly nonlinear ionic polymer metal composite application. The extensive numerical simulations are conducted and the results show that the proposed method is superior to the state-of-the-art methods in terms of tracking accuracy and high robustness against disturbances.
A CEI-Based Method for Precise Tracking and Measurement of LEO Satellites in Future Mega-Constellation Missions
Authors: Zhang Entao , Wu Tao , Hu Minchao , Yang Wenge , Ma Hong , Jiao Yiwen , Shi Xueshu , Gao Zefu
Published: August 2023

With the development of low-orbit mega-constellations, low-orbit navigation augmentation systems, and other emerging LEO projects, the tracking accuracy requirement for low-orbit satellites is constantly increasing. However, existing methods have obvious shortcomings, and a new tracking and measurement method for LEO satellites is thus urgently needed. Given this, in this paper, a Connected Element Interferometry (CEI)-based “near-field” measurement model for low-orbit satellites is proposed. On this basis, the goniometric error formula of the model is derived, and the factors included in each error source are briefly discussed, followed by the simplification of the error formula. Furthermore, for the feasibility analysis of the proposed method, the common view time of CEI array on LEO satellites is analyzed in different regions and different baseline lengths. Finally, this paper simulates the effects of satellite–station distance, baseline length, and goniometric angle on the error coefficients in the goniometric error formula, and provides the theoretical goniometric accuracy of this model for different baseline lengths and goniometric angles. Under a baseline length of 240 km, the accuracy can reach 10 nrad. The research results of this paper could play the role of theoretical a priori in accuracy prediction in future low-orbit satellite tracking measurements.
Co-Creating Interdisciplinary Integrated Powerful Knowledge
Authors: Kurup Premnadh M. , Li Xia , Dong Yan , Bhardwaj Meenu , Yang Yunying
Published: August 2023

Interdisciplinary and integrated powerful knowledge (IIPK) is the outcome of integrating multiple disciplinary perspectives and approaches to tackle challenging real-world issues. Using many disciplinary fields, IIPK is essential for problem solving, innovation, and technical breakthroughs that require careful, imaginative, and innovative application of a wide range of disciplinary knowledge. It is especially relevant when addressing socio-scientific issues, which require a comprehensive understanding of scientific, social, and ethical dimensions. Collaborative and interdisciplinary work among people with diverse backgrounds and expertise is necessary to advance IIPK, including professionals from different academic fields, policymakers, stakeholders, and community members who bring various perspectives and values to the table. The co-creation of IIPK could inform policy making, support informed decision making, and lead to more comprehensive, effective, and sustainable solutions. Theoretical underpinnings and practical applications of co-creating IIPK are discussed here based on several principles with the potential to impact current practices. The co-creation and dissemination of IIPK could use multiple platforms, such as scholarly articles, encyclopedias, and media, including social media. These platforms provide scope for co-creating powerful knowledge through a people participatory approach, which would lead to changes in practices.
The SSR Genetic Diversity of Wild Red Fruit Lycium (Lycium barbarum) in Northwest China
Authors: Gao Xiaoge , Li Jiajia , Song Jie , Guo Qirong
Published: August 2023

Through a comprehensive regional systematic collection, we conducted a genetic diversity analysis of wild red-fruited Lycium resources across the entire northwest region of China. This study provides a valuable genetic basis for germplasm exploration and the selection of new Lycium varieties. Utilizing fluorescence capillary electrophoresis, we carefully screened 16 pairs of SSR primers exhibiting high polymorphism. Subsequently, we inferred the genetic diversity of Lycium germplasm through structure clustering, UPGMA analysis, and molecular AMOVA. The 113 Lycium barbarum samples collected from northwest China exhibited distinct subgroups, namely the Qinghai–Gansu–Ningxia subgroup and the Xinjiang subgroup. These subgroups were clearly distinguishable based on genetic clustering. The genetic diversity within the samples was remarkably rich, as indicated by a mean I value of 1.04, He value of 0.57, and PIC value of 0.73. Notably, the majority of genetic diversity (72.99%) was found within populations, signifying substantial intrapopulation variation. Furthermore, our findings revealed significant genetic differentiation among populations, with a substantial Fst value of 0.27 and gene flow Nm estimated at 0.68. This suggests that the genetic variation levels in northwest Lycium were notably high, primarily driven by pronounced genetic differentiation among populations. Nonetheless, it is important to note that genetic diversity predominantly persists within populations. The observed subpopulation structure of Qinghai–Gansu–Ningxia and Xinjiang regions in northwest China can be primarily attributed to geographical isolation. These geographical barriers have played a pivotal role in shaping the genetic differentiation and structure of Lycium populations in the region. Consequently, our study sheds light on the complex genetic landscape of Northwest Lycium and highlights the significance of considering both within-population diversity and population differentiation in conservation and breeding programs.
Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention
Authors: Andrada Maria Eduarda , Russell David , Arevalo-Ramirez Tito , Kuang Winnie , Kantor George , Yandun Francisco
Published: August 2023

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management.
Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees
Authors: Al Jawarneh Isam Mashhour , Foschini Luca , Bellavista Paolo
Published: August 2023

Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.