Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambient RF Energy Harvesting

In this paper, we present a study of ambient RF energy harvesting techniques. The measurement of the ambient RF power density is presented. The average of the density in broadband (1GHz3.5GHz) is in the order of -12dBm/m2 (63μW/m2). Two systems have been studied to recover the RF energy. The first is a broadband system without matching circuit. The second is a narrow band system (1.8-1.9GHz) with a matching circuit. The rectifier circuit RF / DC and the choice of the load to optimize the DC power recovered are presented. The preliminary results indicate that the recovered energy is not sufficient to directly power devices but could be stored in a super-capacity or micro-batteries.
Dimensionality reduction for calibration of dynamic building simulation models

Calibrating dynamic building simulation with sub-hourly data involves comparing a high number of simulated and measured values. The predictability and informativeness of each value taken separately may be limited. Thus, dimensionality reduction, by transforming the simulation output data to a space of lower dimension while retaining signiﬁcant features, has the potential to improve the computational eﬃciency of calibration. This contribution aims at investigating the use of linear and nonlinear dimensionality reduction techniques for the calibration of dynamic building simulation models involving uncertainties pertaining to both building and heating systems. Two calibration cases are considered. The techniques used for dimensionality reduction are principal component analysis on the one hand, and on the other hand nonlinear feature extraction based on artiﬁcial neural networks. A Bayesian calibration method is then applied to the outputs with reduced dimension, and its performance is assessed with synthetic data. The results show improved performance in comparison to calibration based on daily average values.
Effects of Gene Polymorphisms, Metabolic Activity, and Content of Alcohol Dehydrogenase and Acetaldehyde Dehydrogenases on Prognosis of Hepatocellular Carcinoma Patients.

BACKGROUND Alcohol dehydrogenase and acetaldehyde dehydrogenases have been associated with hepatocellular carcinoma, but how alcohol dehydrogenase and acetaldehyde dehydrogenases alter the prognosis of hepatocellular carcinoma have not been completely elucidated. METHODS Metabolic activities, gene polymorphisms, and content of alcohol dehydrogenase and acetaldehyde dehydrogenases were determined in 68 fibrotic livers from hepatocellular carcinoma patients. These characteristics were then correlated with clinical features and prognosis in these patients. RESULTS The median survival time of the ALDH-high activity group (727 days) was increased by 128% compared with that of ALDH-low activity group (319 days), and there was a significant negative correlation between the activity of acetaldehyde dehydrogenases and the level of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase. There was no difference in survival time between ALDH2-high and ALDH2-low expression group, though the activity of acetaldehyde dehydrogenases had correlation with the content of ALDH2 (r = 0.6887, P < .001). Mutation at ALDH2rs671 significantly decreased both the activity and content of acetalde- hyde dehydrogenases, but the polymorphism had no relationship with progression of hepatocellular carcinoma patients. In addition, the activity and 3 polymorphisms of alcohol dehydrogenase had no effect on overall survival. Mutation at ADH1Crs698 significantly decreased both the activity and content of alcohol dehydrogenase (P < .05), mutation at ADH1C rs2241894 had an inverse effect, and mutation at ADH1B rs1229984 increased activity but did not affect content. The activity of alcohol dehydrogenase had a moderate cor- relation with the content of ADH1A and ADH1C in livers (P < .05). CONCLUSION Low activity of acetaldehyde dehydrogenases in livers correlates with poor prognosis and clinical progression in hepatocel- lular carcinoma patients, and both gene polymorphisms and content influence its metabolic activity.
A Usage History Information Generation and Inquiry Method for Theme, Background and Signal Music Based on Hyperledger Fabric

Theme, background and signal music is the music inserted into broadcasting contents of a broadcaster, and is recognized as a created content like normal music. Since there are lyricists and composers who have the rights of music, the copyright fee is distributed. However, there often occur problems with inaccurate monitoring results for theme, background and signal music usage due to the omission of usage details and non-transparent settlement method. In this paper, we propose a method of generating music usage monitoring information based on a blockchain, on which the usage information of music source is recorded by the usage monitoring tool based on the feature-based filtering technology of monitoring organizations. The framework proposed in this paper includes a blockchain network structure, a series of transaction procedures in which the theme, background and signal music usage history is stored and the recorded data format in the blockchain ledger. In the proposed framework, accurate music usage details can be created, details are stored in blocks without changes or omissions, and eventually transparent settlement and distribution are possible by processing smart contract, instead of previous non-transparent settlement practice.
Design of SSG‐1 gate‐based cost‐efficient reversible digital circuits using quantum‐dot cellular automata technology

In recent times, logic circuit designing with quantum‐dot cellular automata (QCA) technology is being comprehensively researched because of its inherent miniature size, ultra‐high speed, low power dissipation, and better switching frequency in the nanoregime. In this paper, QCA implementation of a 3 × 3 SSG‐1 reversible gate is first proposed. This gate is then used to design a reversible full adder, reversible 1‐bit comparator, and reversible 4:2 compressor. To evaluate the performance of the proposed QCA designs, an in‐depth comparison with existing designs has been performed. It is seen that the proposed SSG‐1 gate is efficient from the existing designs which eventually leads to efficient designs of adders, comparator, and compressor digital circuits. To further validate this, these digital circuits have also been compared with the literature and performance improvement is clearly observed in these circuits. In addition to this, energy dissipation analysis of all proposed logic gates and digital circuits is also performed for different kink energy levels using the QCA Pro tool and their respective energy dissipation maps are also obtained. Based on this, it is seen that the proposed designs are cost‐efficient and dissipate less energy which makes them more appropriate for designing higher‐order lower power circuits.
Sel Surya Fotoelektrokimia Berbasis Film Komposit Nanopartikel TiO2-MoO3 dan Dye Antosianin.

ASTUTI ALAWIYAH. Photoelectrochemical Solar Cell Based on Composite Nanoparticle TiO2-MoO3 Film and Anthocyanin Dye. Supervised by AKHIRUDDIN MADDU and SIDIKRUBADI PRAMUDITO. Research for obtained low-cost, renewable, and can used generally energy source have been depeloved, one of that is research about solar cells. Dye sensitized solar cells (DSSCs) with nanoparticle Titanium dioxide added by Molybdenum trioxide as photoanode and anthocyanin dye from Melastoma malabathricum L. fruit as photosensitizer was succesfully fabricated and charactherized. Composite nanoparticle TiO2-MoO3 films photoelectrode have been made using blade casting thecnique and characterized by X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). Nanoparticle TiO2 added by MoO3 with variation 1%, 5%, and 10%. This adding aimed to decrease energy gap between valence band and conductive band that made electron excited easier. Photoelectrochemical characterization have been testing with sunlight resources. The efficiency of pure TiO2 sample, doped by MoO3 1%, 5%, and 10% for each sample is 0.011%; 0.004%; 0.023%; dan 0.008%.
Detection of Bacillus Species with Arsenic Resistance and Plant Growth Promoting Efficacy from Agricultural Soils of Nepal

Arsenic contamination in soil and water is one of the major environmental problems in multiple countries including Nepal imposing a serious threat to the ecosystem and public health. Many soil bacteria can detoxify arsenic, including genus Bacillus. With an objective to gauge the plant growth-promoting activities of arsenic-resistant Bacillus species, 36 samples (soil, rice, cauliflower, and beans) were collected from the Terai region of Nepal. For selective isolation of Bacillus species, each sample was heated at 80°C for 15 min before the inoculation into nutrient agar (NA). Following the standard protocol, arsenic-resistant Bacillus species were screened using NA supplemented with 100 ppm sodium arsenate and sodium arsenite. Among 158 randomly selected isolates, only five isolates were able to tolerate sodium arsenite concentration up to 600 ppm. Notably, all five isolates were able to produce indole acetic acid (IAA), a plant hormone, and solubilize phosphate. Based on biochemical analysis and 16S rRNA gene sequencing, isolates N4-1, RW, KR7-12, Bhw1-4, and BW2-2 were identified as B. subtilis subsp. stercosis, B. flexus, B. licheniformis, B. cereus, and B. flexus, respectively. To the best of our knowledge, this is the first study showing the presence of arsenic-resistant B. flexus in Nepalese soil with plant growth-promoting traits. Possible utilization of these Bacillus strains could facilitate the novel bioremediation pathway to reduce the toxic effect of arsenic from the soil and water in the Terai region of Nepal.
MEMS device and fabrication method of the same

The present invention, a frame; Is formed in the same layer as the frame, the actuator coupled to the relative motion with respect to the frame; And at least one stopper (stopper) for limiting the displacement in the vertical direction of the actuator; provides a MEMS device comprising the. In another aspect, the present invention, the step of bonding a second substrate on a first substrate; Forming an actuator coupled to the relative motion with respect to the frame and the frame by partially removing the first substrate; It provides a MEMS device manufacturing method comprising: a; and the step of removing the second substrate by partially forming at least one stopper to limit the height direction of displacement of the actuator.
Ambient Air Blade‐Coating Fabrication of Stable Triple‐Cation Perovskite Solar Modules by Green Solvent Quenching

Although halide perovskite solar cell (PSC) technology reaches, in few years, efficiencies greater than 25%, the cost‐ceffective perspective is achievable only if scalable processes in real manufacturing conditions (i.e., pilot line and/or plant factory) are designed and optimized for the full device stack. Herein, a full semiautomatic scalable process based on the blade‐coating technique is demonstrated to fabricate perovskite solar modules in ambient conditions. An efficient and stable triple‐cation cesium methylammonium formamidinium (CsMAFA) perovskite is deposited in ambient air with a two‐step process assisted by air and green anti‐solvent quenching. The developed industry compatible coating process enables the fabrication of several highly reproducible small‐area cells on module size substrate with an efficiency exceeding 17% and with high reproducibility. Corresponding reproducible modules (less than 2% variability) with a 90% geometrical fill factor achieve an efficiency larger than 16% and T 80 = 750 h in light‐soaking condition at maximum power point and room temperature/ambient after encapsulation. Film deposition properties are assessed by different characterization techniques, namely, scanning electron microscopy, profilometry, UV–vis and photoluminescence (PL) spectroscopy, and PL and electroluminescence imaging. The techniques confirm less defects and local coating variations of the ambient air/bladed devices with respect to the nitrogen air/spinned devices.
No Retrenchment From Employee Empowerment: Employer Wellness Imperatives and Opportunities Emerging From the COVID-19 Pandemic

The impact of two years of the COVID-19 pandemic on the relationship between employers and employees are explored, including changing employee sensibilities with respect to future employment, work-life balance, remote and flexible work, and the great resignation. Lasting work changes induced by the pandemic expand employee empowerment and demand for greater work flexibility. Flexibility no longer provides employers a unique selling point and hiring/retention competitiveness – it has become an expected standard. Evolving workplace expectations are tied to realizations of the value of work within the broader context of employees' lives, changing business culture across many industries. Demand for increased work/employment individualization and personalization overlaps unprecedented personalization of and power of mobile technologies. Human-centered employee management in the post-COVID-19 era will become imperative, with many opportunities for employers to enable greater impact in employee wellness and health promotion driven by deploying compelling virtual-remote engagement and behavioral change technologies.
A Short-Term Load Forecasting Model Based on Self-Adaptive Momentum Factor and Wavelet Neural Network in Smart Grid

Short-term load forecasting plays an essential role in the efficient management of electrical systems. Building an optimization model that will enhance forecasting accuracy is a challenging task and a concern for electrical load prediction. Due to Artificial Neural Networks (ANNs), the final result depends on initial random weights and thresholds that affect the stability of the forecast. Although much devotion is being given to improving the forecast accuracy, convergence, complexity, and resilience need to be considered for stable predictive models. To overcome this limitation, this work has jointly considered the Wavelet Neural Network (WNN) and Self-Adaptive Momentum Factor (SAMF) to achieve fast convergence, stability, and high accuracy. The proposed hybrid model is developed by combining the Feature Engineering (FE) and SAMF with the WNN model. The FE removes the irrelevant data and shallow features to ensure high computational performance. In contrast, the SAMF combines the wavelet transform’s time and frequency domain properties and adjusts the WNN model’s corresponding parameters. This ensures the global optimum solution while returning accurate predictive results. Finally, the SAMF is used to tune the control parameters of WNN by initializing the random weights and thresholds to accelerate the convergence rate and improve the accuracy compared to the Back-Propagation (BP) method. The proposed hybrid model is tested on the real-time datasets taken from the Australian states of (New South Wales (NSW), and Victoria (VIC)). Experimental results show that the developed model outperforms other benchmark models such as WNN-IGA, BPNN, WNN-AMBA, and Enhanced WNN in terms of instability, rate of convergence, and accuracy.
Adaptive multi-paddock grazing management’s influence on soil food web community structure for: increasing pasture forage production, soil organic carbon, and reducing soil respiration rates in southeastern USA ranches

Background Measurement of two grazing management’s influence on pasture productivity, soil food web structure, soil organic carbon and soil microbial respiration efficiency was conducted on five southeastern US, across-the-fence ranch pairs to compare adaptive multi-paddock grazing (AMP) management, using short grazing events with planned, adaptive recovery periods, to conventional grazing (CG) management, with continuous grazing at low stock density. Methodology A point-in-time experimental field analysis was conducted to compare five AMP or CG ranch pairs to better understand the influence of grazing management on (a) standing crop biomass productivity; (b) soil food web community population, structure and functionality; (c) soil organic carbon accrual; and d) soil-C (CO2) respiration kinetics. Results AMP grazing systems outperformed CG systems by generating: (a) 92.68 g m−2 more standing crop biomass (SCB), promoting 46% higher pasture photosynthetic capacity (Two sample Mann-Whitney; Z = 6.1836; no DF in MW; p = 6.26 × 10−10; Effect size = 0.35) (b) a strong positive linear relationship of SCB with fungal biomass (R = 0.9915; F(1,3) = 175.35; p = 0.015); fungal to bacterial (F:B) biomass ratio (R = 0.9616; F(1,3) = 36.75; p = 0.009) and a soil food web proxy (R = 0.9616; F(1,3) = 36.75; p = 0.009) and a concurrent very strong inverse relationship with bacteria biomass (R = −0.946; F(1,3) = 25.56; p = 0.015); (c) significant predator/prey interactions with an inverse relationship with bacterial population biomass (R = − 0.946; F(1,3) = 25.56; p = 0.015) and a positive relationship with total protozoa enumeration (R = 0.9826; F(1,3) = 83.68; p = 0.003) when compared to SCB; (d) a 19.52% reduction in soil C (CO2) respiration rates (Two sample t-test; T = −2.3581; DF = 52.3541; p = 0.0221; Effect size = 0.59); and (e) a 20.6% increase in soil organic carbon (SOC) in the top 10 cm of soil profile (Two sample Mann–Whitney; Z = 2.6507; no DF in MW; p = 0.008; Effect size = 0.24). Rancher conversion to AMP grazing strategies would appear to regenerate soil food web population, structure, diversity and biological functionality helping to improve: carbon flow into plant biomass, buildup of soil carbon, predator/prey nutrient cycling and soil microbial respiration efficiency while offering improved climate resilience and a strategy to increase the capture and storage of atmospheric CO2 in soils of the world’s rangeland.
The Effects of Adding Pictorial Depth Cues to the Poggendorff Illusion

We tested if the misapplication of perceptual constancy mechanisms might explain the perceived misalignment of the oblique lines in the Poggendorff illusion. Specifically, whether these mechanisms might treat the rectangle in the middle portion of the Poggendorff stimulus as an occluder in front of one long line appearing on either side, causing an apparent decrease in the rectangle’s width and an apparent increase in the misalignment of the oblique lines. The study aimed to examine these possibilities by examining the effects of adding pictorial depth cues. In experiments 1 and 2, we presented a central rectangle composed of either large or small bricks to determine if this manipulation would change the perceived alignment of the oblique lines and the perceived width of the central rectangle, respectively. The experiments demonstrated no changes that would support a misapplication of perceptual constancy in driving the illusion, despite some evidence of perceptual size rescaling of the central rectangle. In experiment 3, we presented Poggendorff stimuli in front and at the back of a corridor background rich in texture and linear perspective depth cues to determine if adding these cues would affect the Poggendorff illusion. The central rectangle was physically large and small when presented in front and at the back of the corridor, respectively. The strength of the Poggendorff illusion varied as a function of the physical size of the central rectangle, and, contrary to our predictions, the addition of pictorial depth cues in both the central rectangle and the background decreased rather than increased the strength of the illusion. The implications of these results with regards to different theories are discussed. It could be the case that the illusion depends on both low-level and cognitive mechanisms and that deleterious effects occur on the former when the latter ascribes more certainty to the oblique lines being the same line receding into the distance.
Energy-Aware Resource Scheduling for Serverless Edge Computing

In this paper, we present energy-aware scheduling for Serverless edge computing. Energy awareness is critical since edge nodes, in many Internet of Things (IoT) domains, are meant to be powered by renewable energy sources that are variable, making low-powered and/or overloaded (bottleneck) nodes unavailable and not operating their services. This awareness is also required since energy challenges have not been previously addressed by Serverless, largely due to its origin in cloud computing. To achieve this, we formally model an energy-aware resource scheduling problem in Serverless edge computing, given a cluster of battery-operated and renewable-energy powered nodes. Then, we devise zone-oriented and priority-based algorithms to improve the operational availability of bottleneck nodes. As assets, our algorithm coins terms “sticky offloading” and “warm scheduling” in the interest of the Quality of Service (QoS). We evaluate our proposal against well-known benchmarks using real-world implementations on a cluster of Raspberry Pis enabled with container orchestration, Kubernetes, and Serverless computing, OpenFaaS, where edge nodes are powered by real-world solar irradiation. Experimental results achieve significant improvements, up to 33%, in helping bottleneck node's operational availability while preserving the QoS. With energy awareness, now Serverless can unconditionally offer its resource efficiency and portability at the edge.
Automating CPU Dynamic Thermal Control for High Performance Computing

In a production high-performance computing (HPC) data center, numerous factors, including workload compute in-tensity, cooling infrastructure failure, and the use of economized cooling can substantially increase the CPU temperature. CPU thermal design-related studies have shown that slight variances in the operational temperature can significantly impact the lifetime, durability, and performance of a CPU. Therefore, it is critical to monitor and control the operating temperature of the CPU. In this study, we design an automated and continuous CPU thermal monitoring and control methodology to maintain and control a healthy CPU thermal state. This research utilizes the Redfish protocol to monitor the CPU temperature and dynamic voltage frequency scaling to control the temperature. We developed a reference implementation and evaluated our methodology using a cluster of 150 Raspberry Pi3 nodes. We performed extensive CPU thermal analyses in different scenarios. We analyzed how quickly a CPU can attain the maximum temperature under 100% load at room temperature. Based on our experiments, the temperature of a CPU with 100% load can increase to ~72°C (161.6°F) and ~86°C (186.8°F) with the lowest and highest CPU frequency configurations, respectively. We analyzed the impact of applying thermal control at eight temperature configurations on the thermal and frequency scaling behavior of a CPU. We observed that applying thermal control at lower temperature configurations (e.g., 70°C (158°F)) is a better configuration for healing an overheated CPU. As a result of the proposed model, the CPU operating at normal temperature will consume comparatively less energy, deliver higher performance, and augment its durability.