bluuebunny/crossref_metadata_2025 huggingface dataset:
Pigments and Inks Applied in Juan Vespucci’s Portolan Chart (1520)
Authors: Kriznar Anabelle , Ager Francisco , Macías Luis Robles , Ortega Feliu Inés , Tubío Blanca Gómez , Respaldiza Miguel Ángel
Published: December 2022

Not many manuscript maps have been the object of material analysis so far. A portolan chart, signed and dated by Juan Vespucci in 1520, was studied in this research, conserved at the Archivo General de Indias in Seville (Spain). It is made on parchment and depicts the coasts and islands of Europe and Africa. It is the oldest portolan chart made in Seville, being unusual in applying hand stamp for decorative figures. The map was analysed by different non-invasive techniques: infra-red and ultraviolet light, digital microscope and X-ray fluorescence (XRF). The main goals of this study were to identify the materials used, as well as to detect retouching or restoration work. Results showed that the entire parchment was first covered with a white layer made of lead white (Pb), calcite or gypsum (Ca). The principal pigments used were vermilion (Hg), yellow ochre (Fe), azurite (Cu) and a copper-based green pigment (Cu) and carbon black. The letters were probably written with an iron-gall ink (Fe, Cu). Very thin golden leaves were applied on a mixtion glue for gilded wind roses. Several retouches from the 19th/20th centuries were found using zinc and titanium whites and probably cobalt blue.
Challenges of IoT Identification and Multi-Level Protection in Integrated Data Transmission Networks Based on 5G/6G Technologies
Authors: Dik Gennady , Bogdanov Alexander , Shchegoleva Nadezhda , Dik Aleksandr , Kiyamov Jasur
Published: December 2022

This paper illustrates the main problematic issues of minimizing technological risks in the construction of an integrated architecture for the protection of a “smart habitat” (SH). We analyze the use of the IoT to identify both object hazards and the categorization of switching detection in information collection and processing centers. The article proposes wired and wireless data-transmission systems for the required level of efficiency as well as SH protection. Particular attention is paid to the organization of multi-level protection of promising 5G/6G cellular networks based on the analysis of the security threat landscape.
Evaluation of Tolerance and Trichological Efficacy of a Food Supplement in Men and Women with Telogen Effluvium-like Disorder
Authors: Rinaldi Fabio , Marzani Barbara , Pinto Daniela
Published: December 2022

Hair thinning is a very common problem in dermatology, affecting both men and women, and can strongly impact the quality of life of subjects. In this view, therapies that aim to reduce the appearance of thinning by delaying, arresting, or reversing the course of hair thinning are highly desirable. A novel nutraceutical product (NS) containing active botanicals, a patented composition comprising rutin and polyunsaturated fatty acids (PUFAs), zinc, biotin, and ornithine, has been developed to improve hair growth in subjects with hair thinning. Sixty subjects with telogen effluvium-like hair loss were randomized 1:1 in two groups (NS vs. placebo) and treated for three months and evaluated at the baseline visit (T0), and after 15 days (T1) and one (T2), two (T3), and three (T4) months of treatment. Two follow-up visits one month (T5) and two months after the last assumption (T6) were also included in the protocol. Subjects were evaluated for the percentage of hair in the anagen phase, density of hair in the anagen phase (n/cm2), vellus hair (miniaturized, smaller, and thinner hair), and the entity of hair loss in the telogen phase (pull test). NS supplementation produced a statistically significant (p < 0.05) increase in anagen hair and hair density versus baseline from 15 days (T1) of treatment and this effect was not detectable for placebo. Moreover, a clinically/statistically significant (p < 0.05) decrease in vellus hair was also reported, suggesting not only a rapid reduction of the process of miniaturization of the hair but also a transformation of vellus hair into terminal hair. The NS treatment also showed a significant (p < 0.05) increase in tensile strength till T6 (vs. placebo). A higher percentage of subjects who took the NS noted a reduction in daily hair loss, an increase in brightness, and the presence of stronger hair. No side effects were reported. The present study confirms the clinical efficacy and safety of novel nutraceutical supplements in men and women with hair thinning, acting as a multi-targeted therapeutic approach to hair thinning due to TE-like phenomena.
Heterotridentate Organomonophosphines in Pt(κ3–X1P1X2)(Y) (X1,2 = N1,2 or S1,2), Pt(κ3–P1N1X1)(Y) (X1 = O, C, S or Se) Pt(κ3–P1S1Cl1)(Cl) and Pt(κ3–P1Si1N1)(OL)—Structural Aspects
Authors: Melník Milan , Mikuš Peter
Published: December 2022

This review covers twenty four Pt(II) complexes of the inner coordination sphere Pt(κ3–P1 N1N2)(Y), (Y = Cl, CL); Pt(κ3–P1N1X1)(Y), (X1 = O1 and Y = P2L, Cl, I); (X1 = C1 and Y = NL, Cl); (X1 = S1 and Y = Cl, I); (X1 = Se1 and Y = Cl); Pt(κ3–N1P1N2)(Cl), Pt(κ3–S1P1S2)(Cl), Pt(κ3–P1S1Cl1)(Cl) and Pt(κ3–P1Si1N1)(OL). These complexes are crystallized in three crystal classes: monoclinic (16 examples), triclinic (5 examples), and orthorhombic (3 examples). Each κ3–ligand creates two metallocyclic rings with various combinations of the respective metallocyclic rings. If the common central ligating atom is N1, the 5 + 5 membered, 5 + 5, 5 + 6, 6 + 5, and 6 + 6; if the common central ligating atom is P1: 5 + 5, and 6 + 6; if the common central ligating atom is S1 or Si1, 5 + 6-membered. The structural parameters (Pt-L, L-Pt-L) are analysed and discussed with an attention to the distortion of a square-planar geometry about the Pt(II) atoms as well as trans-influence. The sums of the Pt-L (x = 4) bond distances the growing with the covalent radius of the Y atoms. Noticeably, the distortion of the square-planar geometry is growing with the decreasing size of the inner coordination sphere about the Pt(II) atom. There is a relation between the degree of distortion (Ʈ4) and the numbers of the metallocycles rings. The distortion diminishing is in the order of: 0.058 (5 + 5) > 0.037 (6 + 5) > 0.023 (5 + 6) > 0.022 (6 + 6) membered.
Changes in the Optical Properties of an M-Doped (M = Pt, Ti) hBN Sheet and CO2 Capturing
Authors: Ramírez-de-Arellano Juan Manuel , Jiménez-González Ali Fransuani , Magaña Luis Fernando
Published: December 2022

We performed ab initio DFT calculations to explore the optical properties of a hexagonal boron nitride (hBN) monolayer, doped with a Ti or a Pt atom. Ti doping increases the adsorption capability of the boron nitride surface for capturing CO2. Both doping types increase the optical absorption and reflectivity of the hBN surface in the infrared and visible regions. For the UV region, a B vacancy increases the absorption of the hBN sheet. Captured CO2 bears substantial changes in the optical absorption and reflectivity spectra of the system considered.
Personality Traits and Coping Strategies Relevant to Posttraumatic Growth in Patients with Cancer and Survivors: A Systematic Literature Review
Authors: Knauer Klara , Bach Anne , Schäffeler Norbert , Stengel Andreas , Graf Johanna
Published: December 2022

The possibility of positive psychological changes after cancer, namely, posttraumatic growth, is a growing field of research. Identifying personality traits and coping strategies related to posttraumatic growth may help find vulnerable individuals as well as promote helpful coping strategies to help more patients make positive changes at an early stage. The aim of this systematic literature review is to provide an overview of the quantitative data on coping strategies and personality traits associated with posttraumatic growth in patients with cancer and cancer survivors as well as the methods used in included studies. A systematic literature search was conducted using five databases (PubMed, PubPsych, PsycInfo, Web of Science, and PSYNDEXplus). The 70 reports of included studies assessed posttraumatic growth using questionnaires in a sample of patients with cancer or survivors. In addition, associations with a personality trait or coping strategy had to be examined cross-sectionally or longitudinally. All 1698 articles were screened for titles and abstracts by two authors, after which disputed articles were reviewed by a third author. Afterwards, articles were screened for full texts. Most studies had a cross-sectional design and used a sample of patients with breast cancer. Coping strategies have been researched more than personality factors. The personality traits of resilience, hardiness, dispositional positive affectivity, and dispositional gratitude seem to be related to posttraumatic growth, while the Big Five personality traits (openness to experience, conscientiousness, extraversion, agreeableness, neuroticism) have been less researched and/or seem to be unrelated. The use of social support, religious coping, positive reframing, and reflection during illness as coping strategies seems to be related to posttraumatic growth. The findings can be used for the development of interventions. Future studies should investigate associations longitudinally.
Characterizing Regional Variability in Lung Cancer Outcomes across Ontario—A Population-Based Analysis
Authors: Mullin Monica L. , Shellenberger Jonas , Whitehead Marlo , Brundage Michael , Eisenhauer Elizabeth A. , Lougheed M. Diane , Parker Christopher M. , Digby Geneviève C.
Published: December 2022

Background: Lung cancer (LC) is the leading cause of cancer-related mortality. In Ontario, Canada, there are significant survival differences for patients with newly diagnosed LC across the 14 provincial regions. Methods: A population-based retrospective cohort study using ICES databases from 01/2007–12/2017 identified patients with newly diagnosed LC through the Ontario Cancer Registry and those with LC as the cause of death. Descriptive data included patient, disease, and system characteristics. The primary outcome was 5-year survival by region. Results: 178,202 patient records were identified; 101,263 met inclusion criteria. LC incidence varied by region (5.6–14.6/10,000), as did histologic subtype (adenocarcinoma: 27.3–46.1%). Five-year cancer-specific survival was impacted by age, rurality, pathologic subtype, stage at diagnosis, and income quintile. Timely care was inversely related to survival (fastest quintile: HR 3.22, p < 0.0001). Adjusted 5-year cancer-specific survival varied across regions (24.1%, HR 1.12; 34.0%, HR 0.89, p < 0.001). Conclusions: When adjusting for confounders, differences in survival by health region persisted, suggesting a complex interplay between patient, disease, and system factors. A single approach to improving patient care is likely to be ineffective across different systems. Quality improvement initiatives to improve patient outcomes require different approaches amongst health regions to address local disparities in care.
Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5
Authors: Schlagenhauf Tobias , Wolf Jan , Puchta Alexander
Published: December 2022

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.
Multicenter, Open Label, Randomized Controlled Superiority Trial for Availability to Reduce Nocturnal Urination Frequency: Study Protocol for a TOP-STAR Study
Authors: Nakajima Hanako , Okada Hiroshi , Kogure Akinori , Osaka Takafumi , Tsutsumi Takeshi , Tanaka Toru , Hasegawa Goji , Mogami Shinichi , Mitsuhashi Kazuteru , Kitagawa Noriyuki , Hashimoto Yoshitaka , Yano Miho , Tanaka Muhei , Kitamura Akane , Ishii Michiyo , Nakamura Naoto , Kishi Akio , Ushigome Emi , Hamaguchi Masahide , Fukui Michiaki
Published: December 2022

Nocturia is a common disease in patients with type 2 diabetes mellitus that can reduce the quality of life. Sodium glucose co-transporter 2 (SGLT2) inhibitors increase the urine volume and are often discontinued when polyuria occurs, although tofogliflozin, which has a short half-life in the blood, may improve nocturia by managing hyperglycemia and hypertension, without aggravating nocturia. As excessive sodium intake worsens nocturia and increases urine volume, sodium restriction is also effective in managing nocturia. This multicenter, open-label, randomized parallel-group trial will examine 80 patients with type 2 diabetes who experienced nocturia. After the baseline examination, the patients are randomly stratified into two groups and receive tofogliflozin treatment with or without sodium restriction for 12 weeks. The primary outcome is nocturia frequency at 12 weeks. The secondary outcomes are the frequency of daytime urine, changes in urine volume, and changes in home blood pressure.
Candida Species Isolation from Hospitalized Patients with COVID-19—A Retrospective Study
Authors: Ioannou Petros , Kofteridis Diamantis P. , Alexakis Konstantinos , Koutserimpas Christos , Papakitsou Ioanna , Maraki Sofia , Samonis George
Published: December 2022

Coronavirus disease 2019 (COVID-19), a disease characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has so far led to hundreds of millions of infections and millions of deaths. Fungal infections are known to complicate COVID-19 patients and are associated with significant morbidity and mortality. The aim of this study was to assess the incidence of positive cultures for Candida spp. among patients hospitalized with COVID-19, describe their characteristics and identify factors associated with overall mortality in this patient population. Hospitalized COVID-19 patients with Candida spp. isolation were retrospectively assessed and their clinical, laboratory and microbiological characteristics were assessed and evaluated. In total, 69 patients with COVID-19 had a positive culture for Candida spp., representing a rate of 4.5% among all hospitalized COVID-19 patients. Their median age was 78 years (IQR 67–85 years) and 44.9% were male. Hospitalized patients with COVID-19 and Candida spp. isolation who died were older, were more likely to have a diagnosis of dementia, and had higher Charlson comorbidity index, higher Candida score and higher 4C score. Candida score was identified with a multivariate logistic regression analysis model to be independently associated with mortality. The most commonly identified Candida species was C. albicans, followed by C. tropicalis and C. glabrata and the most common source was the urine, even though in most cases the positive culture was not associated with a true infection. Thus, Candida score may be used in COVID-19 patients with isolation of Candida spp. from different body specimens for mortality risk stratification.
Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data
Authors: Srinivasu Parvathaneni Naga , Shafi Jana , Krishna T Balamurali , Sujatha Canavoy Narahari , Praveen S Phani , Ijaz Muhammad Fazal
Published: December 2022

The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.
Palmoplantar Psoriasis: A Clinico-Pathologic Study on a Series of 21 Cases with Emphasis on Differential Diagnosis
Authors: Broggi Giuseppe , Failla Maria , Palicelli Andrea , Zanelli Magda , Caltabiano Rosario
Published: December 2022

Palmoplantar psoriasis (PP) is a relatively uncommon variant of psoriasis that affects palms and soles, and that frequently shares both clinical and histologic features with chronic eczema, hyperkeratotic hand dermatitis and allergic contact dermatitis. The present study aims to characterize the histologic features of PP on a series of 21 cases. The following morphological features and their distribution were included: parakeratosis, dilated vessels in papillary dermis, psoriasiform acanthosis with elongation of rete ridges, perivascular lymphocytic infiltrate, decrease/loss of granular layer, Munro’s microabscesses, spongiform pustules of Kogoj, spongiosis and lymphocytic exocytosis. The main diagnostic clues and histologic differential diagnoses are also discussed.
Predictability of Invisalign® Clear Aligners Using OrthoPulse®: A Retrospective Study
Authors: Levrini Luca , Carganico Andrea , Deppieri Alessandro , Saran Stefano , Bocchieri Salvatore , Zecca Piero Antonio , Bertini Sara , D’Apote Anna , Segù Marzia
Published: December 2022

This preliminary retrospective study evaluates how effective the OrthoPulse® (Biolux Technology, Austria) is in increasing the predictability of orthodontic treatment in patients treated with Invisalign® clear aligners (Align Technology Inc., Tempe, AZ, USA). A group of 376 patients were treated with Invisalign® orthodontic clear aligners in association with an OrthoPulse®. The OrthoPulse® was prescribed for 10 min a day for the entire duration of the orthodontic treatment. The OrthoPulse® App remotely tracked the percentage compliance of each patient. The number of aligners planned with the ClinCheck software at the beginning of the treatment and the number of total aligners (including the adjunctive aligners) used to finish the treatment were then considered. After applying inclusion/exclusion criteria, a total of 40 patients remained in the study and were compared with a control group of 40 patients with the same characteristics as the study group. A statistical analysis was carried out to investigate whether using OrthoPulse® led to a statistical reduction in the number of adjunctive aligners, thus leading to a more accurate prediction of the treatment. The statistical analysis showed that patients who used OrthoPulse® needed fewer finishing aligners and a greater predictability of the treatment was obtained. In fact, in the treated group the average number of additional aligners represented 66.5% of the initial aligners, whereas in the control group 103.4% of the initially planned aligners were needed. In conclusion, in patients treated with clear aligners, OrthoPulse® would appear to increase the predictability of orthodontic treatment with clear aligners, thus reducing the number of finishing phase requirements.
Simultaneous Astronaut Accompanying and Visual Navigation in Semi-Structured and Dynamic Intravehicular Environment
Authors: Zhang Qi , Fan Li , Zhang Yulin
Published: December 2022

The application of intravehicular robotic assistants (IRA) can save valuable working hours for astronauts in space stations. There are various types of IRA, such as an accompanying drone working in microgravity and a dexterous humanoid robot for collaborative operations. In either case, the ability to navigate and work along with human astronauts lays the foundation for their deployment. To address this problem, this paper proposes the framework of simultaneous astronaut accompanying and visual navigation. The framework contains a customized astronaut detector, an intravehicular navigation system, and a probabilistic model for astronaut visual tracking and motion prediction. The customized detector is designed to be lightweight and has achieved superior performance (AP@0.5 of 99.36%) for astronaut detection in diverse postures and orientations during intravehicular activities. A map-based visual navigation method is proposed for accurate and 6DoF localization (1~2 cm, 0.5°) in semi-structured environments. To ensure the robustness of navigation in dynamic scenes, feature points within the detected bounding boxes are filtered out. The probabilistic model is formulated based on the map-based navigation system and the customized astronaut detector. Both trajectory correlation and geometric similarity clues are incorporated into the model for stable visual tracking and trajectory estimation of the astronaut. The overall framework enables the robotic assistant to track and distinguish the served astronaut efficiently during intravehicular activities and to provide foresighted service while in locomotion. The overall performance and superiority of the proposed framework are verified through extensive ground experiments in a space-station mockup.
A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
Authors: Hu Bangtao , Li Xue , Xue Linshan
Published: December 2022

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.
Detection of Bottle Marine Debris Using Unmanned Aerial Vehicles and Machine Learning Techniques
Authors: Tran Thi Linh Chi , Huang Zhi-Cheng , Tseng Kuo-Hsin , Chou Ping-Hsien
Published: December 2022

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures.
Cyclical Trends of Network Load Fluctuations in Traffic Jamming
Authors: Tadić Bosiljka
Published: December 2022

The transport of information packets in complex networks is a prototype system for the study of traffic jamming, a nonlinear dynamic phenomenon that arises with increased traffic load and limited network capacity. The underlying mathematical framework helps to reveal how the macroscopic jams build-up from microscopic dynamics, depending on the posting rate, navigation rules, and network structure. We investigate the time series of traffic loads before congestion occurs on two networks with structures that support efficient transport at low traffic or higher traffic density, respectively. Each node has a fixed finite queue length and uses next-nearest-neighbour search to navigate the packets toward their destination nodes and the LIFO queueing rule. We find that when approaching the respective congestion thresholds in these networks, the traffic load fluctuations show a similar temporal pattern; it is described by dominant cyclical trends with multifractal features and the broadening of the singularity spectrum regarding small-scale fluctuations. The long-range correlations captured by the power spectra show a power-law decay with network-dependent exponents. Meanwhile, the short-range correlations dominate at the onset of congestion. These findings reveal inherent characteristics of traffic jams inferred from traffic load time series as warning signs of congestion, complementing statistical indicators such as increased travel time and prolonged queuing in different transportation networks.
Interaction between an Impurity and Nonlinear Excitations in a Polariton Condensate
Authors: Jia Chunyu , Liang Zhaoxin
Published: December 2022

Exploring the dynamics of a mobile impurity immersed in field excitations is challenging, as it requires to account for the entanglement between the impurity and the surrounding excitations. To this end, the impurity’s effective mass has to be considered as finite, rather than infinite. Here, we theoretically investigate the interaction between a finite-mass impurity and a dissipative soliton representing nonlinear excitations in the polariton Bose–Einstein condensate (BEC). Using the Lagrange variational method and the open-dissipative Gross–Pitaevskii equation, we analytically derive the interaction phase diagram between the impurity and a dissipative bright soliton in the polariton BEC. Depending on the impurity mass, we find the dissipative soliton colliding with the impurity can transmit through, get trapped, or be reflected. This work opens a new perspective in understanding the impurity dynamics when immersed in field excitations, as well as potential applications in information processing with polariton solitons.
Supply and Demand Model for a Chili Enterprise System Using a Simultaneous Equations System
Authors: Rachmaniah Meuthia , Suroso Arif Imam , Syukur Muhamad , Hermadi Irman
Published: December 2022

The supply and demand of various fresh chili types correspond to one another simultaneously because they can be complementary or substitute products. This study aimed to simultaneously examine the supply and demand model of red chili and cayenne peppers to support innovative chili enterprise system development. The methodology is the simultaneous equation system panel data using the years 2010–2020 time-series and provincial cross-sectional data combined with data mining techniques. The two-stage least square method was used for the estimation and simulation of the models. The results of the models comprise three identities and ten structural equations. The resulting models are all statistically fit with some drawbacks owing to the micro-level data availabilities. The simulations on the labor wages, prices, demand, and production for red chili and cayenne peppers affected the other variables within the model. In conclusion, the chili supply and demand models could support the development of the innovative chili enterprise system; however, this requires proactive collaboration and participation from all the relevant institutions and actors. Moreover, this research model is potentially applicable to other horticultural commodities.
A Joint Design of Radar Sensing, Wireless Power Transfer, and Communication Based on Reconfigurable Software Defined Radio
Authors: Wu Zhouyi , Qaragoez Yasser , Volskiy Vladimir , Huangfu Jiangtao , Ran Lixin , Schreurs Dominique
Published: December 2022

This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support its operation and provides two-way communication. The sensor node sends uplink information through backscattering using the third order intermodulation (IM3) product of the rectification. In the radar mode, a single-tone continuous wave (CW) is used to monitor if there is a moving target in the static environment. If a speed is detected, the transmit signal to the node is stopped, while the single-tone CW excitation will continue until the speed of the target is zero, and then the base station transmits a stepped frequency continuous wave (SFCW) signal to measure the distance of the target. The repeat between the two radar waveforms continues until the target is undetectable within the detection range. The software defined radio PlutoSDR is adopted as the base station. The system can wirelessly supply power and bi-directionally communicate with a CO2 sensor node 2 m away. It gives a range resolution of 2.5 cm and a minimum detectable speed of 0.25 m/s in the radar mode.
ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI
Authors: Unlersen Muhammed Fahri
Published: December 2022

The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the path loss adapted, and exponential transformed) to train and test an artificial neural network (ANN). A systematic approach to the determination of neuron numbers in the hidden layers and activation functions of ANN is provided. The ANN is trained by the artificial bee colony algorithm. Five ML methods have been employed for estimation. The best performance has been achieved with ABC-ANN by the path loss adapted database with the MAE of 1.01 m. The estimation done using processed RSSI values has better performance than raw RSSI values. In addition, 33% less error occurs with the mentioned method compared to the data set source study.
Multi-UAV Clustered NOMA for Covert Communications: Joint Resource Allocation and Trajectory Optimization
Authors: Qin Xiaofei , Wu Xu , Xiong Mudi , Liu Ye , Zhang Yue
Published: December 2022

Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-assisted communication networks have been widely used in civil and military fields. However, the open accessibility of wireless channels brings a huge risk of privacy disclosure to UAV-based networks. This paper considers a multi-UAV-assisted covert communication system based on Wireless Powered Communication (WPC) and Clustered-Non-Orthogonal-Multiple-Access (C-NOMA), aiming to hide the transmission behavior between UAVs and legitimate ground users (LGUs). Specifically, the UAVs serve as aerial base stations to provide services to LGUs, while avoiding detection by the ground warden. In order to improve the considered covert communication performance, the average uplink covert rate of all clusters in each slot is maximized by jointly optimizing the cluster scheduling variable, subslot allocation, LGU transmit power and multi-UAV trajectory subject to covertness constraints. The original problem is a mixed integer non-convex problem, which are typically difficult to solve directly. To solve this challenge, this paper decouples it into four sub-problems and solves the sub-problems by alternating iterations until the objective function converges. The simulation results show that the proposed multi-UAV-assisted covert communication scheme can effectively improve the average uplink covert rate of all clusters compared with the benchmark schemes.
Optimization Research of Directed Fuzzing Based on AFL
Authors: Feng Tao , Liu Jinkun
Published: December 2022

Fuzz testing is the process of testing programs by continually producing unique inputs in order to detect and identify security flaws. It is often used in vulnerability mining. The most prevalent fuzzing approach is grey-box fuzzing, which combines lightweight code instrumentation with data-feedback-driven generation of fresh program input seeds. AFL (American Fuzzy Lop) is an outstanding grey-box fuzzing tool that is well known for its quick fork server execution, dependable genetic algorithm, and numerous mutation techniques. AFLGO proposes and executes power scheduling based on a simulated annealing process for a more appropriate energy allocation to seeds, however it is neither reliable nor successful. To tackle this issue, we offer an energy-dynamic scheduling strategy based on the algorithm of the fruit fly. Adjusting the energy of the seeds dynamically controls the production of test cases. The findings demonstrate that the approach suggested in this research can test the target region more rapidly and thoroughly and has a high application value for patch testing and vulnerability replication.
A Study on Reduction of Cogging Torque and Magnet Usage through Intersect Magnet Consequent Pole Structure
Authors: Song Si-Woo , Hong Min-Ki , Lee Ju , Kim Won-Ho
Published: December 2022

Owing to the shortage of rare-earth magnetic materials, various methods are being examined to reduce the use of magnets. One of these is a consequent pole. The consequent pole model can reduce the use of magnets by 50% using only one pole of the magnet and replacing the other pole with iron. However, the consequent pole has the disadvantage of generating back EMF asymmetry and a high cogging torque. In this study, an intersect magnet consequent pole structure is proposed to overcome the disadvantages of the existing consequent pole. Two methods have been proposed to improve axial leakage magnetic flux caused by the intersect magnet consequent pole structure. Finally, we propose a method to reduce the cogging torque and minimize the use of magnets with the same performance standard. For motor design, two-dimensional and three-dimensional finite element analysis was used, and comparative analysis was performed via simulations for several models. The existing model and the final model were compared and verified.
Industrial Policy and Technological Innovation of New Energy Vehicle Industry in China
Authors: Li Hongchen , Qi Huijun , Cao Hongjian , Yuan Li
Published: December 2022

Promoting the development of new energy vehicles is one of the important measures to ensure energy security and deal with global warming. Technological innovation is an inexhaustible driving force for the development of the new energy vehicle industry. This study considered listed enterprises in China’s new energy vehicle industry as research samples and used the fixed effect model to study the impact of government subsidies on the quantity and quality of technological innovation in the new energy vehicle industry. The empirical results show that government subsidies have a significant positive impact on the quantity of technological innovation in the new energy vehicle industry; however, government subsidies have no significant impact on the quality of technological innovation. Government subsidies increase the quantity of technological innovation in the new energy vehicle industry by increasing R&D investment, mitigating financing constraints, and improving the external attention of enterprises. Compared to downstream enterprises in the industrial chain, government subsidies have a better incentive effect on the technological innovation of upstream enterprises, which increases the number of patents and enhances the quality of utility model patents. Government subsidies have a better effect on promoting the quantity of technological innovation in large enterprises.
Study on the Performance of Variable Density Multilayer Insulation in Liquid Hydrogen Temperature Region
Authors: Li Kecen , Chen Jie , Tian Xueqin , He Yujing
Published: December 2022

The storage of hydrogen is important for the development of hydrogen energy, especially for the storage of liquid hydrogen, which has been receiving more and more attention recently. In order to study the thermal insulation performance of variable-density multilayer insulation (VDMLI) structures under different working conditions at liquid hydrogen temperatures without incorporating a composite structure, we established a heat transfer model based on a layer-by-layer calculation method. Then, we carried out numerical calculations to analyze the influence of the total number of layers, the thermal boundary temperature, and vacuums on the performance of MLI at liquid hydrogen temperatures. To investigate the optimization of variable-density configurations on the thermal insulation performance of VDMLI and to obtain accurate variable-density configurations, we proposed a variable-density configuration method based on the control variable method and the insertion by region method. The results indicate that the optimal variable-density configuration is the insertion of 4 layers of radiation shields in the low-density region, 15 layers in the medium-density region and 38 layers in the high-density region. Compared with a uniform-density structure, the heat flux is reduced by 8.6%.
An Analytical Feasibility Study for Solar Panel Installation in Qatar Based on Generated to Consumed Electrical Energy Indicator
Authors: Banibaqash Ayed , Hunaiti Ziad , Abbod Maysam
Published: December 2022

The main objective of this study is to establish analytical feasibility for the deployment of solar panels in Qatar houses and other organisations; to calculate, by the use of analytical means, solar panel deployment scenarios with different panel sizes, efficiency, and sun per day, in order to estimate generated energy and compare that with actual consumption over a period of twelve months. In addition, the study aims to provide a comparative indicator on the ratio of generated energy to consumed energy and consumed electrical energy (GtoC), to establish the possibility of using the GtoC ratio as a new renewable energy rating index, for use in renewable energy investment and sales forecasting, in maintaining comparisons between different installation scenarios, and in upgrade planning and decision making. This study’s analytical solutions might be correlated with data from in situ solar panel installation scenarios in order to fully establish the performance under operational scenarios. The study will be beneficial to support roadmaps to foster solar panel deployment in Qatar, through demonstrating scenarios that can enable economic and environmental incentives. In addition, the study can be useful for other Gulf Cooperation Council (GCC) states with similar weather and economic conditions. Moreover, GtoC indicator can be used as a new source of data to establish a data-driven model for suitable clean energy projects.
Thermal Performance of Load-Bearing, Lightweight, Steel-Framed Partition Walls Using Thermal Break Strips: A Parametric Study
Authors: Santos Paulo , Lopes Paulo , Abrantes David
Published: December 2022

Thermal bridges are a very relevant issue for lightweight steel-framed (LSF) construction systems given the high thermal conductivity of steel, which can negatively compromise their thermal behaviour, reduce their durability, and decrease the building energy efficiency. Several thermal bridge mitigation strategies exist, including the attachment of thermal break strips (TBS) to the steel studs’ flanges as one of the most widely employed techniques. In this research, the relevance of TBS to the thermal performance improvement of load-bearing LSF partition walls was assessed by performing a parametric study, making use of a validated 2D numerical model. A sensitivity analysis was performed for five different key parameters, and their importance was evaluated. The assessed parameters included the number of TBS and their thickness, width, and thermal conductivity, as well as the vertical steel stud spacing. We found that these parameters were all relevant. Moreover, regardless of the TBS thermal conductivity, it is always worth increasing their thickness. However, the increase in the TBS width does not always lead to increased thermal resistance; a thermal performance reduction was noted when increasing the width of the TBS at higher thermal conductivities. Therefore, it was concluded that it is more efficient to increase TBS thickness than their width.
LCA of Mixed Generation Systems in Singapore: Implications for National Policy Making
Authors: Khoo Hsien H.
Published: December 2022

The decarbonization of electrical power generation systems is one of Singapore’s national political agendas to reduce national greenhouse emissions. LCA is applied to assess the trade-offs of national implementation of electricity generation from conventional fossil-fuel power plants, compared to low-carbon alternatives. The first aim of LCA is to quantify the emission inventory of national electrical generation within the geographical boundary of Singapore, and next to generate the potential environmental impacts of Global Warming Potential, Acidification, and Eutrophication. Various scenarios are tested for a projected diversity of fuel resource mixes considered for years 2030 and 2040 and a hypothetical scenario where 100% renewable energy is employed and imported as the nation transitions towards a low-carbon energy future. Further discussions on the additional LCA model indicators should be included for the potential of low-carbon hydrogen application.
Toward Models of Impact and Recovery of the US Western Grid from Earthquake Events
Authors: Weinmann Riley , Cotilla-Sanchez Eduardo , Brekken Ted
Published: December 2022

A Cascadia Subduction Zone (CSZ) earthquake will cause widespread damage to numerous lifelines and infrastructure along the northern US west coast. The goal of the presented research is to provide a bottom up estimate of the impact on and subsequent recovery of a Cascadia Subduction Zone earthquake on the US western grid to supplement and enhance the expert opinion estimates provided to date. The scope is limited to only consideration of shaking damage to utility substation equipment components of a power system model. The analysis utilizes probabilistic models of damage and recovery for substation power system assets, along with graph techniques for modeling connectivity, and Monte Carlo quasi steady state power flow solutions. The results show that a conservative estimate of the initial damage and loss of load is approximately 4000 MW, with a recovery estimate of 230 days.
Progress in Graphene Oxide Hybrids for Environmental Applications
Authors: Joel Edith Flora , Lujanienė Galina
Published: December 2022

Graphene-oxide-based metal hybrids (GM) are used for the rapid and efficient reduction and removal of toxic adulterants in the environment. The exceptionally high specific surface area, versatile surface chemistry, and exceptional customization efficiency of graphene oxide nanosheets combined with the adaptable chemistry of metal nanoparticles enable the formation of GM hybrid nanocomposites. However, little is known about the architecture of GM nanocomposite engineering, interaction mechanisms, and environmental compatibility. This review aims to describe the environmental performance of graphene oxide–metal hybrids for the removal of environmental pollutants, carbon capture, EMI shielding efficiency, and microbial elimination of engineered graphene oxide composites anchored with metal particles. We also developed an essential link between the material properties of GM nanohybrids and their performance, which identified the fundamental parameters that influence the contaminant removal capability and EMI resistance efficiency. The influence of the thermodynamic parameters of GM on the adsorption of radioisotopes, heavy metals, organic pollutants, and dyes was considered. Finally, we comment on the remaining challenges and provide suggestions for future developments in this field.
Modeling the Missing DBHs: Influence of Model Form on UAV DBH Characterization
Authors: Tinkham Wade T. , Swayze Neal C. , Hoffman Chad M. , Lad Lauren E. , Battaglia Mike A.
Published: December 2022

The reliability of forest management decisions partly depends on the quality and extent of the data needed for the decision. However, the relatively high cost of traditional field sampling limits sampling intensity and data quality. One strategy to increase data quality and extent, while reducing the overall sample effort, is using remote sensing-based data from unmanned aerial vehicles (UAV). While these techniques reliably identify most tree locations and heights in open-canopied forests, their ability to characterize diameter at breast height (DBH) is limited to estimates of a fraction of trees within the area. This study used UAV-derived DBHs and explanatory variables to test five model forms in predicting the missing DBHs. The results showed that filtering UAV DBHs using regionally derived height to DBH allometries significantly improved model performance. The best predicting model was slightly biased, with a 5.6 cm mean error and a mean absolute error of 6.8 cm. When applied across the stand, the number of trees was underestimated by 26.7 (3.9%), while the basal area and quadratic mean diameter were overestimated by 3.3 m2 ha−1 (13.1%) and 1.8 cm (8.3%), respectively. This study proposes a pathway for remotely sensed DBHs to predict missing DBHs; however, challenges are highlighted in ensuring the model training dataset represents the population.