bluuebunny/crossref_metadata_2025 huggingface dataset:
KANPHOS: A Database of Kinase-Associated Neural Protein Phosphorylation in the Brain
Authors: Ahammad Rijwan Uddin , Nishioka Tomoki , Yoshimoto Junichiro , Kannon Takayuki , Amano Mutsuki , Funahashi Yasuhiro , Tsuboi Daisuke , Faruk Md. Omar , Yamahashi Yukie , Yamada Kiyofumi , Nagai Taku , Kaibuchi Kozo
Published: December 2021

Protein phosphorylation plays critical roles in a variety of intracellular signaling pathways and physiological functions that are controlled by neurotransmitters and neuromodulators in the brain. Dysregulation of these signaling pathways has been implicated in neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder and schizophrenia. While recent advances in mass spectrometry-based proteomics have allowed us to identify approximately 280,000 phosphorylation sites, it remains largely unknown which sites are phosphorylated by which kinases. To overcome this issue, previously, we developed methods for comprehensive screening of the target substrates of given kinases, such as PKA and Rho-kinase, upon stimulation by extracellular signals and identified many candidate substrates for specific kinases and their phosphorylation sites. Here, we developed a novel online database to provide information about the phosphorylation signals identified by our methods, as well as those previously reported in the literature. The “KANPHOS” (Kinase-Associated Neural Phospho-Signaling) database and its web portal were built based on a next-generation XooNIps neuroinformatics tool. To explore the functionality of the KANPHOS database, we obtained phosphoproteomics data for adenosine-A2A-receptor signaling and its downstream MAPK-mediated signaling in the striatum/nucleus accumbens, registered them in KANPHOS, and analyzed the related pathways.
Evaluation of Endothelial Dysfunction and Autophagy in Fibromyalgia-Related Vascular and Cerebral Cortical Changes and the Ameliorative Effect of Fisetin
Authors: Ghoneim Fatma Mohamed , Abo-Elkhair Salwa Mohamed , Elsamanoudy Ayman Zaky , Shabaan Dalia A.
Published: December 2021

Fibromyalgia (FM) is a common chronic pain syndrome that affects 1% to 5% of the population. We aimed to investigate the role of endothelial dysfunction and autophagy in fibromyalgia-related vascular and cerebral cortical changes in a reserpine-induced rat model of fibromyalgia at the histological and molecular levels and to study the ameliorative effect of fisetin. Forty adult female albino rats were divided into four groups (10 each): two control groups, the reserpine-induced fibromyalgia group, and the fisetin-treated group. The carotid arteries and brains of the animals were dissected. Frozen tissue samples were used for total RNA extraction and qPCR analysis of eNOS, caspase-3, Bcl-2, LC-3, BECN-1, CHOP, and TNF-α expression. Histological, immunohistochemical (eNOS), and ultrastructure studies were conducted. The carotid arteries revealed excessive autophagy and endothelial, vascular, and apoptotic changes. The cerebral cortex showed similar findings apart from endoplasmic reticulum stress. Additionally, there was decreased gene expression of eNOS and Bcl-2 and increased expression of caspase-3, LC-3, BECN-1, CHOP, and TNF-α. In the fisetin-treated rats, improvements in the histological and molecular results were detected. In conclusion, oxidative stress, enhanced apoptosis, and excessive autophagy are fundamental pathophysiologic mechanisms of reserpine-induced fibromyalgia. Moreover, fisetin has an ameliorative effect against fibromyalgia.
Electroless Ni–P Plating on Mullite Powders and Study of the Mechanical Properties of Its Plasma-Sprayed Coating
Authors: Chen Kaiwang , Zhang Penglin , Sun Pengfei , Niu Xianming , Hu Chunlian
Published: December 2021

To effectively improve the properties of a mullite coating and its interfacial bonding with the substrate, a Ni–P layer is deposited on the surface of mullite powders by electroless plating. The original mullite powders and coated mullite powders are then deposited onto stainless-steel substrates by plasma spraying. The growth mechanism of the Ni–P layer during the plating, the microstructures of the coated powders and mullite coating and the properties of the mullite coatings are characterized and analyzed. The results indicate that the Ni–P layer on the surface of the mullite powder has cell structures with a dense uniform distribution and grows in layers on the surface of the mullite powder. The crystallization behavior of Ni-P amorphous layer is induced by heat treatment. Compared to the original mullite coating, the coating prepared by the coated mullite powders has better manufacturability, stronger adhesion to the substrate, lower porosity (7.40%, 65% of that of the original coating), higher hardness (500.1 HV, 1.2 times that of the original coating), and better thermal cycle resistance (two times that of the original coating). The method of preparation of high-temperature thermal barrier coatings with coated mullite powders has a high application value.
Design and Fabrication of a Cost-Effective Optical Notch Filter for Improving Visual Quality
Authors: Tien Chuen-Lin , Lin Hong-Yi , Cheng Kuan-Sheng , Chiang Chun-Yu , Cheng Ching-Ying
Published: December 2021

This study presents a multilayer design and fabrication of an optical notch filter for enhancing visual quality. A cost-effective multilayer design of notch filter with low surface roughness and low residual stress is proposed. A 9-layer notch filter composed of SiO2 and Nb2O5 with a central wavelength of 480 nm is prepared by electron beam evaporation combined with ion-assisted deposition. The optical transmittance, residual stress, and surface morphology are measured by a UV/VIS/NIR spectrophotometer, Twyman-Green interferometer and field emission scanning electron microscopy (FE-SEM). The transmittance of the notch filter at the central wavelength is above 15%, and the average transmittance of the transmission band is about 80%. The residual stress of the notch filter is −0.235 GPa, and the root mean square surface roughness is 1.85 nm. For improving the visual quality, a good image contrast can be obtained by observing the microscopic image using the proposed notch filter.
The Tribological Adaptability for Ventral Scales of Dinodon rufozonatum in Dry/Wet/Rough Environments
Authors: Hu Song , Shi Ge , Guo Qinggang , Zheng Long , Ren Luquan , Su Chao
Published: December 2021

The ventral scales of Dinodon rufozonatum were investigated to understand the outstanding tribological adaptability in various environments. The coefficient of friction (COF) of ventral scales was measured and changed with the contact conditions. It was discovered that the COF of scales under water-lubrication conditions (WLC) was larger than that under dry conditions (DC). More interestingly, the COF increased first and then decreased as the substrate roughness reduced. The abrasion marks on scales were then observed. The results indicated that the scales in DC wore more gently than that in WLC. Moreover, the degree of wear reduced with the decrease of substrate roughness. The frictional performance of ventral scales enabled the snakes to move more efficiently, quickly, and flexibly in multiple environments.
Reflection Efficiency and Spectra Resolutions Ray-Tracing Simulations for the VOXES HAPG Crystal Based Von Hamos Spectrometer
Authors: De Leo Veronica , Scordo Alessandro , Curceanu Catalina , Miliucci Marco , Sirghi Florin
Published: December 2021

The VOXES collaboration at INFN National Laboratories of Frascati developed a prototype of a high resolution Von Hamos X-ray spectrometer using HAPG (Highly Annealed Pyrolytic Graphite) mosaic crystals. This technology allows the employment of extended isotropic sources and could find application in several physics fields. The capability of the spectrometer to reach energy precision and resolution below 1 and 10 eV, respectively, when used with wide sources, has been already demonstrated. Recently, the response of this device, for a ρ = 206.7 mm cylindrically bent HAPG crystal using CuKα1,2 and FeKα1,2 XRF lines, has been investigated in terms of reflection efficiency by a dedicated ray-tracing simulation. Details of the simulation procedure and the comparison with the experimental results are presented. This study is crucial in order to retrieve information on the spectrometer signal collection efficiency, especially in the energy range in which the standard calibration procedures cannot be applied.
Heat Transfer and Flow Characteristics of Pseudoplastic Nanomaterial Liquid Flowing over the Slender Cylinder with Variable Characteristics
Authors: Hussain Azad , Rehman Aysha , Ahmed Naqash , El-Shafay Ahmed S. , Najati Sahar A. , Almaliki Abdulrazak H. , Sherif El-Sayed M.
Published: December 2021

The present article investigates heat transfer and pseudoplastic nanomaterial liquid flow over a vertical thin cylinder. The Buongiorno model is used for this analysis. The problem gains more significance when temperature-dependent variable viscosity is taken into account. Using suitable similarity variables, nonlinear flow equations are first converted into ordinary differential equations. The generating structure is solved by the MATLAB BVP4C algorithm. Newly developed physical parameters are focused. It is observed that the heat transfer rate and the skin friction coefficient is increased remarkably because of mixing nano-particles in the base fluid by considering γb=1, 2, 3, 4 and λ=1, 1.5, 2, 2.5, 3. It is found that the temperature field increases by inclining the values of thermophoresis and Brownian motion parameters. It is also evaluated that the velocity field decreases by increasing the values of the curvature parameter, Weissenberg number and buoyancy ratio characteristics.
Classification Systems of Endometrial Cancer: A Comparative Study about Old and New
Authors: Coada Camelia Alexandra , Dondi Giulia , Ravegnini Gloria , De Leo Antonio , Santini Donatella , De Crescenzo Eugenia , Tesei Marco , Bovicelli Alessandro , Giunchi Susanna , Dormi Ada , Di Stanislao Marco , Morganti Alessio G. , De Biase Dario , De Iaco Pierandrea , Perrone Anna Myriam
Published: December 2021

Endometrial cancer is the most common gynecological malignancy of the female reproductive organs. Historically it was divided into type I and type II, until 2013 when the Cancer Genome Atlas molecular classification was proposed. Here, we applied the different classification types on our endometrial cancer patient cohort in order to identify the most predictive one. We enrolled 117 endometrial cancer patients available for the study and collected the following parameters: age, body mass index, stage, menopause, Lynch syndrome status, parity, hypertension, type of localization of the lesion at hysteroscopy, type of surgery and complications, and presence of metachronous or synchronous tumors. The tumors were classified according to the European Society for Medical Oncology, Proactive Molecular Risk Classifier for Endometrial Cancer, Post-Operative Radiation Therapy in Endometrial Carcinoma, and Cancer Genome Atlas classification schemes. Our data confirmed that European Society for Medical Oncology risk was the strongest predictor of prognosis in our cohort. The parameters correlated with poor prognosis were the histotype, FIGO stage, and grade. Our study cohort shows that risk stratification should be based on the integration of histologic, clinical, and molecular parameters.
Echocardiographic Parameters as Predictors for the Efficiency of Resynchronization Therapy in Patients with Dilated Cardiomyopathy and HFrEF
Authors: Pescariu Silvius-Alexandru , Şoşdean Raluca , Tudoran Cristina , Ionac Adina , Pop Gheorghe Nicusor , Timar Romulus Zorin , Pescariu Sorin , Tudoran Mariana
Published: December 2021

Cardiac resynchronization therapy (CRT) represents an increasingly recommended solution to alleviate symptomatology and improve the quality of life in individuals with dilated cardiomyopathy (DCM) and heart failure (HF) with reduced ejection fraction (HFrEF) who remain symptomatic despite optimal medical therapy (OMT). However, this therapy does have the desired results all cases, in that sometimes low sensing and high voltage stimulation are needed to obtain some degree of resynchronization, even in the case of perfectly placed cardiac pacing leads. Our study aims to identify whether there is a relationship between several transthoracic echocardiographic (TTE) parameters characterizing left ventricular (LV) performance, especially strain results, and sensing and pacing parameters. Between 2020–2021, CRT was performed to treat persistent symptoms in 48 patients with a mean age of 64 (53.25–70) years, who were diagnosed with DCM and HFrEF, and who were still symptomatic despite OMT. We documented statistically significant correlations between global longitudinal strain, posterolateral strain, and ejection fraction and LV sensing (r = 0.65, 0.469, and 0.534, respectively, p < 0.001) and LV pacing parameters (r = −0.567, −0.555, and −0.363, respectively, p < 0.001). Modern imaging techniques, such as TTE with cardiac strain, are contributing to the evaluation of patients with HFrEF, increasing the chances of CRT success, and allowing physicians to anticipate and plan for case management.
Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology Referral
Authors: Carvalho Rafaela , Morgado Ana C. , Andrade Catarina , Nedelcu Tudor , Carreiro André , Vasconcelos Maria João M.
Published: December 2021

Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.
Chronic Kidney Disease of Unknown Etiology (CKDu) in Sri Lanka: Hematological Changes and Pro-Inflammation Suggest Likely Predictors of Advance Disease, as Renal Outcomes Show Prevalent Normoalbuminuria
Authors: Gunawickrama S. H. Nandana P. , Hewavitharana K. Imesha G. , Nanayakkara P. G. Chandra L. , Gunawickrama K. B. Suneetha
Published: December 2021

CKDu needs to be characterized in fundamental areas to improve etiological understanding and disease management. In a cross-sectional study, blood cell profile and plasma inflammatory cytokines were followed by automated analysis and sandwich ELISA, respectively. Disease development stages and proteinuria were ascertained by eGFR and UACR. Comparison among control and stages (ANOVA/Dunnett’s MRT) revealed time-specific changes (p < 0.05), including decreased erythrocytes (G5) and hematocrit (G5), and increased MCHC (G3b, G4), MCV (G5), and MCH (G5). CKDu decreased (p < 0.05) lymphocytes (G3b, G4, G5), monocytes (G3b), MPV (G3b, G4, G5), and plateletcrit (G3b, G4), and increased basophils (G3a, G3b, G4), N/L (G4) and PLR (G4–G5). MCHC and aforesaid leukocyte variables were in correlation (rho > ±0.03, p < 0.05, Pearson’s test) with disease development. MCP-1 and IL-6 spiked (p > 0.05) at G3b. Multivariate analyses confirmed that MCP-1, lymphocytes, and BMI were related to renal dysfunction, pointing to inflammation, compromised immunity, and muscle wasting as CKDu effects. Nonproteinuric CKDu was prevalent (23.2–35.6% of total CKDu) with (p < 0.05) elevated basophils (G3a), N/L (G4), and depleted lymphocytes (G4). In both forms, G1–G2 were unaffected, and the earliest change was G3a basophils. Results suggest that MCP-1, lymphocyte count, N/L, and PLR may verify the stage and predict impending ESRD in advance proteinuric CKDu.
Entropy Effects in Intermolecular Associations of Crown-Ethers and Cyclodextrins with Amino Acids in Aqueous and in Non-Aqueous Media
Authors: Usacheva Tatyana , Terekhova Irina , Alister Diana , Agafonov Mikhail , Kuranova Natalya , Tyurin Dmitry , Sharnin Valentin
Published: December 2021

The analysis of the ratios of entropy and enthalpy characteristics and their contributions to the change in the Gibbs energy of intermolecular interactions of crown ethers and cyclodextrins with amino acids is carried out. Two different types of macrocycles were chosen for examination: crown ethers with a hydrophilic interior and cyclodextrins with a hydrophobic inner cavity and a hydrophilic exterior. The thermodynamics of complex formation of crown ethers and cyclodextrins with amino acids in water and aqueous-organic solvents of variable composition was examined. The contributions of the entropy solvation of complexes of 18-crown-6 with glycine, alanine, phenylalanine to the change in the entropy of complexation in water-ethanol and water-dimethyl sulfoxide solvents was calculated and analyzed. It was found that the ratios of the entropy and enthalpy solvation of the reagents for these systems have similar trends when moving from water to aqueous-organic mixtures. The relationship between the thermodynamic characteristics and structural features of the complexation processes between cyclodextrins and amino acids has been established. The thermodynamic enthalpy–entropy compensation effect was revealed, and its features for complexation of cyclodextrins and 18-crown-6 were considered. It was concluded that, based on the thermodynamic parameters of molecular complexation, one could judge the mode of the formation of complexes, the main driving forces of the interactions, and the degree of desolvation.
Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems
Authors: Tang Xiafei , Zhou Yuyang , Zou Yiqun , Zhang Qichun
Published: December 2021

This paper investigates the randomness assignment problem for a class of continuous-time stochastic nonlinear systems, where variance and entropy are employed to describe the investigated systems. In particular, the system model is formulated by a stochastic differential equation. Due to the nonlinearities of the systems, the probability density functions of the system state and system output cannot be characterised as Gaussian even if the system is subjected to Brownian motion. To deal with the non-Gaussian randomness, we present a novel backstepping-based design approach to convert the stochastic nonlinear system to a linear stochastic process, thus the variance and entropy of the system variables can be formulated analytically by the solving Fokker–Planck–Kolmogorov equation. In this way, the design parameter of the backstepping procedure can be then obtained to achieve the variance and entropy assignment. In addition, the stability of the proposed design scheme can be guaranteed and the multi-variate case is also discussed. In order to validate the design approach, the simulation results are provided to show the effectiveness of the proposed algorithm.
Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems
Authors: Xiao Hongjian , Mandic Danilo P.
Published: December 2021

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.
Network Bending: Expressive Manipulation of Generative Models in Multiple Domains
Authors: Broad Terence , Leymarie Frederic Fol , Grierson Mick
Published: December 2021

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.
Hydro-Meteorological Incident and Disaster Response in Sri Lanka. Case Study: 2016 May Rain Events
Authors: Tillekaratne Hiran I. , Werellagama Induka , Madduma-Bandara Chandrasekara M. , Bandara Thalakumbure W. M. T. W. , Abeynayaka Amila
Published: December 2021

This paper investigates hydro-meteorological hazards faced by Sri Lanka, a lower-middle-income island country in Asia. It provides a case study of a major hydro-meteorological disaster incident that resulted in one of the largest landslides in the history of the country, the Post-Disaster Needs Assessment (PDNA) process, and the national disaster response. Rainfall and flood inundation data are provided for the whole country. The fact that data are held by several government agencies (namely Department of Meteorology, Department of Irrigation, and NBRO), somewhat coordinated by the Disaster Management Center (DMC) is shown. The need for more streamlined coordination of hydro-met data with online access of data for researchers is emphasized. The flood disaster situation and disaster declaration of the Western Province (which contributes nearly 40% of the GDP) is looked at, and evidence is presented to recommend a smaller governance unit for future disaster declarations, in order to bring aid to the places where it is needed and leaving other areas of the province to carry on with the normal economic activity. An example of the use of climate change scenarios in rainfall prediction is provided from a developed island nation (New Zealand). The need for Sri Lanka to increase its spending for hydro-met services (both infrastructure and skills) is highlighted (the global norm being 0.02 of GDP), as the return on such investment is tenfold.
Early Warning Early Action for the Banking Solvency Risk in the COVID-19 Pandemic Era: A Case Study of Indonesia
Authors: Hidayat Taufiq , Masyita Dian , Nidar Sulaeman Rahman , Ahmad Fauzan , Syarif Muhammad Adrissa Nur
Published: December 2021

The COVID-19 pandemic has affected people’s lives and increased the banking solvency risk. This research aimed to build an early warning and early action simulation model to mitigate the solvency risk using the system dynamics methodology and the Powersim Studio 10© software. The addition of an early action simulation updates the existing early warning model. Through this model, the effect of policy design and options on potential solvency risks is known before implementation. The trials conducted at Bank BRI (BBRI) and Bank Mandiri (BMRI) showed that the model had the ability to provide an early warning of the potential increase in bank solvency risk when the loan restructuring policy is revoked. It also simulates the effectiveness of management’s policy options to mitigate these risks. This research used publicly accessible banking data and analysis. Bank management could also take advantage of this model through a self-stimulation facility developed in this study to accommodate their needs using the internal data.
Using Environmental Physiology to Teach Physiological Regulation
Authors: Simonson Shawn R.
Published: December 2021

Environmental Physiology at Boise State University, Idaho, is a multidisciplinary course that expands students’ understanding of human regulatory physiology through acute and chronic responses to environmental extremes. Students explore the physics (pressure, fluid dynamics, gas laws, sound, and light) of the underwater environment, marine flora and fauna adaptations to this environment, and the human experience within this environment. Included is completion of the Professional Association of Dive Instructors (PADI) Open Water Scuba Certification. The course culminates in an international dive trip where course concepts are further demonstrated and explored, and conservation activities are undertaken.
English as a Vocational Passport: Japanese Medical Students and Second Language Learning Motivation
Authors: Mathis Bryan J. , Mayers Thomas , Miyamasu Flaminia
Published: December 2021

Recently, Japanese medical students are expected to acquire a high degree of English proficiency with the tacit understanding that it will feature in their future profession through interactions with non-Japanese patients and/or engagement with medical research. However, to the best of our knowledge, the motivation of Japanese medical students to learn English as a second language (L2) has not been studied. Using quantitative and qualitative questionnaires, we investigated the L2 learning motivation of second-year Japanese medical students and the degree to which the students have international posture, i.e., their awareness and openness to internationalization. The results revealed that the students were primarily instrumentally and vocationally motivated; in other words, their chief motivation for L2 learning came from an understanding of the usefulness of L2 proficiency for their future profession. The results also showed that the students had strong international posture, as evidenced by a deep desire to communicate internationally and an understanding of the role of English as a tool for global communication. The findings suggest that, to sustain or improve L2 learning motivation, educators should employ study materials that are pertinent to students’ future needs and professions.
Improvement of Accuracy and Precision of the LiDAR System Working in High Background Light Conditions
Authors: Nguyen Thanh-Tuan , Cheng Ching-Hwa , Liu Don-Gey , Le Minh-Hai
Published: December 2021

Background light noise is one of the major challenges in the design of Light Detection and Ranging (LiDAR) systems. In this paper, we build a single-beam LiDAR module to investigate the effect of light intensity on the accuracy/precision and success rate of measurements in environments with strong background noises. The proposed LiDAR system includes the laser signal emitter and receiver system, the signal processing embedded platform, and the computer for remote control. In this study, two well-known time-of-flight (ToF) estimation methods, which are peak detection and cross-correlation (CC), were applied and compared. In the meanwhile, we exploited the cross-correlation technique combined with the reduced parabolic interpolation (CCP) algorithm to improve the accuracy and precision of the LiDAR system, with the analog-to-digital converter (ADC) having a limited resolution of 125 mega samples per second (Msps). The results show that the CC and CCP methods achieved a higher success rate than the peak method, which is 12.3% in the case of applying emitted pulses 10 µs/frame and 8.6% with 20 µs/frame. In addition, the CCP method has the highest accuracy/precision in the three methods reaching 7.4 cm/10 cm and has a significant improvement over the ADC’s resolution of 1.2 m. This work shows our contribution in building a LiDAR system with low cost and high performance, accuracy, and precision.
Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm
Authors: Muoka Pascal , Onwuchekwa Daniel , Obermaisser Roman
Published: December 2021

Adaptation in time-triggered systems can be motivated by energy efficiency, fault recovery, and changing environmental conditions. Adaptation in time-triggered systems is achieved by preserving temporal predictability through metascheduling techniques. Nevertheless, utilising existing metascheduling schemes for time-triggered network-on-chip architectures poses design time computation and run-time storage challenges for adaptation using the resulting schedules. In this work, an algorithm for path reconvergence in a multi-schedule graph, enabled by a reconvergence horizon, is presented to manage the state-space explosion problem resulting from an increase in the number of scenarios required for adaptation. A meta-scheduler invokes a genetic algorithm to solve a new scheduling problem for each adaptation scenario, resulting in a multi-schedule graph. Finally, repeated nodes of the multi-schedule graph are merged, and further exploration of paths is terminated. The proposed algorithm is evaluated using various application model sizes and different horizon configurations. Results show up to 56% reduction of schedules necessary for adaptation to 10 context events, with the reconvergence horizon set to 50 time units. Furthermore, 10 jobs with 10 slack events and a horizon of 40 ticks result in a 23% average sleep time for energy savings. Furthermore, the results demonstrate the reduction in the state-space size while showing the trade-off between the size of the reconvergence horizon and the number of nodes of the multi-schedule graph.
Online Trajectory Planning Method for Double-Pendulum Quadrotor Transportation Systems
Authors: Qi Juntong , Ping Yuan , Wang Mingming , Wu Chong
Published: December 2021

This study investigates the trajectory planning problem for double-pendulum quadrotor transportation systems. The goal is to restrain the hook swing and payload swing while achieving precise positioning. An online trajectory planning method with two capabilities—precise positioning and swing suppression—is proposed. The stability and convergence of the system are proved using the Lyapunov principle and the LaSalle’s invariance theory. Simulation results show that the proposed method has excellent control performance.
Detailed Placement and Global Routing Co-Optimization with Complex Constraints
Authors: Huang Zhipeng , Huang Haishan , Shi Runming , Li Xu , Zhang Xuan , Chen Weijie , Wang Jiaxiang , Zhu Ziran
Published: December 2021

With several divided stages, placement and routing are the most critical and challenging steps in VLSI physical design. To ensure that physical implementation problems can be manageable and converged in a reasonable runtime, placement/routing problems are usually further split into several sub-problems, which may cause conservative margin reservation and mis-correlation. Therefore, it is desirable to design an algorithm that can accurately and efficiently consider placement and routing simultaneously. In this paper, we propose a detailed placement and global routing co-optimization algorithm while considering complex routing constraints to avoid conservative margin reservation and mis-correlation in placement/routing stages. Firstly, we present a rapidly preprocessing technology based on R-tree to improve the initial routing results. After that, a BFS-based approximate optimal addressing algorithm in 3D is designed to find a proper destination for cell movement. We propose an optimal region selection algorithm based on the partial routing solution to jump out of the local optimal solution. Further, a fast partial net rip-up and rerouted algorithm is used in the process of cell movement. Finally, we adopt an efficient refinement technique to reduce the routing length further. Compared with the top 3 winners according to the 2020 ICCAD CAD contest benchmarks, the experimental results show that our algorithm achieves the best routing length reduction for all cases with a shorter runtime. On average, our algorithm can improve 0.7%, 1.5%, and 1.7% for the first, second, and third place, respectively. In addition, we can still obtain the best results after relaxing the maximum cell movement constraint, which further illustrates the effectiveness of our algorithm.
A Sensor-Based Drone for Pollutants Detection in Eco-Friendly Cities: Hardware Design and Data Analysis Application
Authors: De Fazio Roberto , Dinoi Leonardo Matteo , De Vittorio Massimo , Visconti Paolo
Published: December 2021

The increase in produced waste is a symptom of inefficient resources usage, which should be better exploited as a resource for energy and materials. The air pollution generated by waste causes impacts felt by a large part of the population living in and around the main urban areas. This paper presents a mobile sensor node for monitoring air and noise pollution; indeed, the developed system is installed on an RC drone, quickly monitoring large areas. It relies on a Raspberry Pi Zero W board and a wide set of sensors (i.e., NO2, CO, NH3, CO2, VOCs, PM2.5, and PM10) to sample the environmental parameter at regular time intervals. A proper classification algorithm was developed to quantify the traffic level from the noise level (NL) acquired by the onboard microphone. Additionally, the drone is equipped with a camera and implements a visual recognition algorithm (Fast R-CNN) to detect waste fires and mark them by a GPS receiver. Furthermore, the firmware for managing the sensing unit operation was developed, as well as the power supply section. In particular, the node’s consumption was analysed in two use cases, and the battery capacity needed to power the designed device was sized. The onfield tests demonstrated the proper operation of the developed monitoring system. Finally, a cloud application was developed to remotely monitor the information acquired by the sensor-based drone and upload them on a remote database.
Memory Characteristics of Thin Film Transistor with Catalytic Metal Layer Induced Crystallized Indium-Gallium-Zinc-Oxide (IGZO) Channel
Authors: Han Hoonhee , Jang Seokmin , Kim Duho , Kim Taeheun , Cho Hyeoncheol , Shin Heedam , Choi Changhwan
Published: December 2021

The memory characteristics of a flash memory device using c-axis aligned crystal indium gallium zinc oxide (CAAC-IGZO) thin film as a channel material were demonstrated. The CAAC-IGZO thin films can replace the current poly-silicon channel, which has reduced mobility because of grain-induced degradation. The CAAC-IGZO thin films were achieved using a tantalum catalyst layer with annealing. A thin film transistor (TFT) with SiO2/Si3N4/Al2O3 and CAAC-IGZO thin films, where Al2O3 was used for the tunneling layer, was evaluated for a flash memory application and compared with a device using an amorphous IGZO (a-IGZO) channel. A source and drain using indium-tin oxide and aluminum were also evaluated for TFT flash memory devices with crystallized and amorphous channel materials. Compared with the a-IGZO device, higher on-current (Ion), improved field effect carrier mobility (μFE), a lower body trap (Nss), a wider memory window (ΔVth), and better retention and endurance characteristics were attained using the CAAC-IGZO device.
Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning
Authors: Schlachter Henning , Geißendörfer Stefan , von Maydell Karsten , Agert Carsten
Published: December 2021

Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control.
Companies’ Stock Market Performance in the Time of COVID-19: Alternative Energy vs. Main Stock Market Sectors
Authors: Wielechowski Michał , Czech Katarzyna
Published: December 2021

The paper aims to detect the differences in stock market performance between companies from the alternative energy sector and main stock market sectors in the first and second years of the COVID-19 pandemic. We used Global Industry Classification Standard to analyse eleven main stock market sectors and the alternative energy sector. Based on the one-factor variance analysis—ANOVA, we reveal the statistically significant differences between the analysed stock market sectors in both 2020 and 2021. The analysis implied that the performance of stock market companies during COVID-19 is sector-specific. Tukey’s Honestly Significant Difference (HSD) test for pairwise comparison indicates that the alternative energy sector shows the most differentiation. Its average rate of return in 2020 is the highest and is significantly different for all eleven stock market sectors, while the top constituents from the conventional energy and financial sectors suffered the most. In 2021, a reverse trend in the stock prices can be observed. Companies from the conventional energy and financial sectors achieved the highest positive average weekly rates of return among all of the analysed stock market sectors, while the alternative energy sector performed significantly worse than the other sectors did. Nevertheless, throughout the entire analyses period of 2020–2021, the companies from the alternative energy sector turned out to be the biggest stock market beneficiaries. This study might imply that the COVID-19 pandemic has not hampered but has instead accelerated growing concerns about the environment and climate change.
Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid
Authors: Powroźnik Piotr , Szcześniak Paweł , Piotrowski Krzysztof
Published: December 2021

Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.
Comparison of Support Programs for the Development of Photovoltaics in Poland: My Electricity Program and the RES Auction System
Authors: Kulpa Jarosław , Olczak Piotr , Surma Tomasz , Matuszewska Dominika
Published: December 2021

Poland has great potential for the development of renewable energy sources. The implementation of support systems dedicated to renewable sources has resulted in the installation of over 10,500 MW of installed capacity. At present, with high electricity prices, stimulated by the costs of CO2 emissions and the costs of fuel purchase, renewable energy sources are of particular importance in the transformation of the Polish power industry. The RES auction system and the My Electricity Program contributed to the growth of entrepreneurship and the development of the economy. Energy consumers, from passive ones, have become active market participants—prosumers. The RES auction system alone contributed to the creation of approx. 5 GWp of installed capacity of photovoltaics (PV) sources in 2016–2021, while the My Electricity Program contributed to the creation of approx. 2 GWp of installed capacity in PV installations in 2019–2021. The aim of the study is to compare the economic and social costs of two photovoltaic development programs, My Electricity and the RES auction system, from the point of view of the country (in support distribution costs—subsidies) and investors, renewable energy installations operators and prosumers to which these programs are targeted, namely, individuals and enterprises.
A New Testing Facility to Investigate the Removal Processes of Indoor Air Contaminants with Different Cleaning Technologies and to Better Assess and Exploit Their Performances
Authors: Ciccioli Piero , Pallozzi Emanuele , Guerriero Ettore , Iannelli Maria Adelaide , Donati Enrica , Lilla Laura , Rinaldi Carmine , Svaldi Paolo , Ciccioli Paolo , Mabilia Rosanna
Published: December 2021

Residential air cleaners exploiting different technologies re commonly used today to remove air contaminants from indoor environments. Different methods have been developed in the USA and Europe to test their efficiency. The one used in the USA provides a more comprehensive view of indoor processes, because testing is performed in a large simulation chamber (28.5 m3), using anthropogenic emissions, such as cigarette smoke, to generate pollution. Testing rooms are also important to investigate new removal technologies, or to improve them. Since no such testing facilities exist in Italy, one of 12.4 m3 was built in which cigarette smoke, resuspended dust from agricultural soil and, for the first time, diesel exhaust emissions were used to generate indoor pollution. Performances were tested with two air cleaning systems, exploiting completely different removal technologies. Accurate values of decay rates of indoor pollutants were obtained using a suite of on-line and out-of-line monitors for the measurement of particulate matter, volatile organic compounds (VOCs) and some inorganic gases. Proton-transfer mass spectrometry (PTR-MS) provided an almost real-time detection of several VOCs and H2S, at trace levels (0.01 ppbv). A method using a common in vitro bioassay was developed to assess the ability of air cleaners to remove indoor toxic substances.
Promoting Landscape-Level Forest Management in Fire-Prone Areas: Delegate Management to a Multi-Owner Collaborative, Rent the Land, or Just Sell It?
Authors: Martins Ana , Novais Ana , Santos José Lima , Canadas Maria João
Published: December 2021

Forest management at the landscape level is a requirement for reducing wildfire hazard. In contexts where non-industrial private forest ownership prevails, the collaboration among multiple owners has been proposed as the way forward to reach consistent fuel management at that level. The current literature has been focused on identifying the factors that lead to collaboration among owners. In this study we explored other ways to reach landscape-level management in addition to the collaborative way, such as those that may be promoted through land renting or selling. Different contexts and owner types may require different solutions. Thus, we explicitly asked which alternative would be chosen by a given forest owner, from the following set: keeping individual management, entering a multi-owner collaborative arrangement where they delegate management, renting to a pulp company; or selling the land. In a context of small-scale ownership and high recurrence of wildfires in Portugal, a face-to-face survey was carried out to a sample of landowners. Our results suggest that there is not an a priori generalized unwillingness of owners to delegate management, rent or sell the land and thus they seem prone to align themselves with policy strategies to promote management at the landscape level. Multinomial logit regression modelling allowed us to explain and predict owners’ choices among the aforementioned set of alternative management options. We found that choosing multi-ownership collaboration, as opposed to keeping current individual management, is associated with passive management under harsher conditions, by non-residents without bonding capital. The identified factors of owners’ choices show the limited scope of tenancy and land-market mechanisms to promote landscape-level management. The best policy option was found to depend on the owner profiles prevailing in the target area. This suggests that studying the existing context and owner types is required to design effective policies.