Miracle-dz/UniFAR-FTUs huggingface dataset:
Analisis Perpindahan Gaya SideLoad Pegas Suspensi Tipe C Menggunakan Finite Element Method (FEM)

The suspension must provide comfort and safety to the vehicle. Therefore, it is necessary to analyze the load's effect on the pressure distribution on the axle so that the spring strength from the sideload force can be simulated. This study aims to analyze the sideload force of type C suspension springs using FEM. Spring variations are the number of different threaded coils, namely 11, 12, and 13. In the FEM simulation, the spring is given a load until 50mm, 100mm, 150mm, 200mm, and 250mm displacement occurs. The loading spreads to the X, Y, and Z axes. Each direction of the axis is measured by the pressure that occurs. The results showed that the pressure values that occur on the X, Y, and Z axes decreased with each increase in the number of screw coils. The lowest X-axis pressure reduction occurs in thread 12 at 63%. Meanwhile, the pressure on the Y axis decreased by 27% on the spring screw 12. The highest decrease in pressure on the Z axis occurred by 34% on the spring screw 12 compared to the spring screw 11.
Budgeted Sequence Submodular Maximization With Uniform and Non-Uniform Costs

The problem of picking a sequence of items that maximizes a given submodular function over the item sequence exists in many practical applications. Existing studies on this problem only consider uniform costs on items. However, it is more general that items have non-uniform costs. Taking this cue, in this article, we study the problem of budgeted sequence submodular maximization (BSSM), which considers both uniform and non-uniform item costs in the sequence selection. This problem widely appears in many applications such as movie recommendation and course learning. For BSSM with uniform costs, we first prove a new approximation ratio for an existing algorithm OMEGA, and then propose an anytime randomized iterative algorithm POBM which converges faster than a state-of-the-art method POSEQSEL in obtaining approximate solutions. We also develop some optimization techniques for improving the efficiency of POBM. For BSSM with non-uniform costs, we design a greedy algorithm GBM with a guaranteed error bound, and prove that POBM can find approximate solutions in a reasonable time. The results of the experimental study on both synthetic and real datasets demonstrate the excellent performance of our algorithms.
The Effect of Including eHealth in Dietary Interventions for Patients with Type 2 Diabetes with Overweight or Obesity: A Systematic Review

eHealth has a growing impact on the delivery of healthcare, making health systems more efficient. This study examined the effect of dietary interventions using eHealth compared to face-to-face contact in patients with (pre-) type 2 diabetes (T2D) and who are overweight/obese. Literature databases were searched upon November 2022. Inclusion criteria: randomized controlled trial; duration ≥ 6 months; involving dietary interventions; performed in adults with (pre-) T2D and who are overweight/obese; using eHealth compared to face-to-face contact; and report outcomes on weight loss, glycemic regulation, and/or cost-effectiveness. Selection of articles was performed manually and using ASReviewLab. Fifteen studies were included for data extraction, investigating a wide variety of eHealth interventions. Seven studies reporting on weight loss showed a significant between-group difference (−1.18 to −5.5 kg); five studies showed a trend in favor of the eHealth programs. Eleven studies reported on HbA1c; three found a significant between-group difference (−0.23 to −0.70%) in favor of the eHealth programs and six studies showed non-significant improvements. Interaction with healthcare professionals led to better results of the dietary interventions. Two studies reported incomplete data on cost-effectiveness. In conclusion, eHealth shows better results of dietary interventions in (pre-) T2D patients compared to face-to-face, especially when combined with interaction with healthcare professionals.
Integral Transformers State Assessment Based on Entropy and the Method of Dissolved Gas Analysis

The article discussed the problem of obtaining a reliable operational assessment of the technical condition of transformers in electrical networks and power supply systems. The problem is caused by insufficient sensitivity of mathematical methods and criteria used to assess the condition when describing the dynamics of changes in signs of defects developing in the transformer. This contributes to an increase in diagnostic errors, makes it difficult to choose and reasonably apply adequate operational impacts on equipment. The authors proposed a comprehensive solution based on a combination of methods for processing and analyzing data from diagnostic monitoring of transformers. The initial data are the protocols for the analysis of dissolved gases (DGA) in transformer oil. The solution increases the informative value of signs of developing defects and the reliability of the integrated assessment of the condition of the equipment. This is achieved by combining the entropy method and the method of statistical calculations in determining and ranking the weighting factors of the integral state function (health index) of the transformer. When forming statistical samples of concentrations of diagnostic gases, arrays of DGA data of various representativeness and affiliation are applicable. A range of options is suitable from the results of long-term off-line observations of a group of equipment of the same voltage class with similar operating conditions to a single transformer with a built-in on-line gas monitoring system. One of the advantages of the developed approach is based on the use of entropy as a measure of uncertainty in calculating the weighting factors and changes in the transformer health index over time during the considered interval of its operation. The presence of a calculated example illustrated the results of research, facilitates understanding of the key provisions of the proposed solution and demonstrates its effectiveness in achieving the goal.
Role of Protein-Protein Interactions in Metabolism: Genetics, Structure, Function

Genetic variations may change the structure and function of individual proteins as well as affect their interactions with other proteins and thereby impact metabolic processes dependent on protein-protein interactions. For example, cytochrome P450 proteins, which metabolize a vast array of drugs, steroids and other xenobiotics, are dependent on interactions with redox and allosteric partner proteins for their localization, stability, (catalytic) function and metabolic diversity (reactions). Genetic variations may impact such interactions by changing the splicing and/or amino acid sequence which in turn may impact protein topology, localization, post translational modifications and three dimensional structure. More generally, research on single gene defects and their role in disease, as well as recent large scale sequencing studies suggest that a large number of genetic variations may contribute to disease not only by affecting gene function or expression but also by modulating complex protein interaction networks. The aim of this research topic is to bring together researchers working in the area of drug, steroid and xenobiotic metabolism who are studying protein-protein inter- actions, to describe their recent advances in the field. We are aiming for a com- prehensive analysis of the subject from different approaches including genetics, proteomics, transcriptomics, structural biology, biochemistry and pharmacology. Of particular interest are papers dealing with translational research describing the role of novel genetic variations altering protein-protein interaction. Authors may submit original articles, reviews and opinion or hypothesis papers dealing with the role of protein-protein interactions in health and disease.
Nutritional Components of the Standing Plankton Crop in Mississippi Sound

A study on seasonal changes in the nutritional components of standing plankton biomass was made from 20 April 1965 to 6 September 1966. Plankton were separated into net plankton and nannoplankton fractions. Nannoplankton3 standing biomass exceeded by 72 times that of net plankton. Although protein and carbohydrate levels were higher in net plankton on a unit weight basis, total nutrients available from nannoplankton were substantially higher. Some seasonal trends were apparent as changes in the standing biomass in net plankton. Nannoplankton exhibited no seasonal trends. Lipid and caloric values of net plankton showed seasonal trends similar to those shown by dry weight.
Community resilience: measuring a community's ability to withstand

Several recent studies have provided a range of perspectives on the role of resilience in policies and programs designed to address natural and man-made threats. A review of those studies reveals that there is strong agreement that the concept of resilience must play a major role in assessing the extent to which various entities—critical infrastructure and key resources, systems, communities, and regions—are prepared to deal with the full range of threats they face. As resilience assessment methodologies continue to be developed and implemented for resilience at various levels, it is critical that a framework be developed to utilize measurements of resilience at multiple levels to characterize a community’s resilience to potential hazards. Argonne National Laboratory, in partnership with the U.S. Department of Homeland Security, has developed a framework that combines existing measures of critical infrastructure and organization resilience with new measurement tools in order to characterize five subsystems of a community: the economy, physical infrastructure, government and nongovernmental organizations, emergency services, and the civilian population. The resilience of each of these subsystems will be measured through various tools, including an assessment of economic diversity; a review of community continuity of operations and government plans for essential functions; an analysis of critical infrastructure and organizational resilience to include lifeline utilities and vital private sector organizations; a study of disaster public education; communication and evacuation programs; and a review of the emergency services capabilities that a community possesses. Through the application of these measures, a community can better understand its current resilience posture, as well as implement a systematic approach to reduce vulnerabilities and consequences of potential hazards.
Are children like werewolves?

In order to verify if the full moon is associated with sleep and activity behaviors, we used a 12-country study providing 33,710 24-h accelerometer recordings of sleep and activity. The present observational, cross-sectional study included 5812 children ages 9–11 years from study sites that represented all inhabited continents and wide ranges of human development (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, United Kingdom, and United States). Three moon phases were used in this analysis: full moon ( ± 4 days; reference), half moon ( ± 5–9 days), and new moon ( ± 10–14 days) from nearest full moon. Nocturnal sleep duration, moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LPA), and total sedentary time (SED) were monitored over seven consecutive days using a waist-worn accelerometer worn 24 h a day. Only sleep duration was found to significantly differ between moon phases (~5 min/night shorter during full moon compared to new moon). Differences in MVPA, LPA, and SED between moon phases were negligible and non-sig-nificant ( < 2 min/day difference). There was no difference in the associations between study sites. In conclusion, sleep duration was 1% shorter at full moon compared to new moon, while activity behaviors were not significantly associated with the lunar cycle in this global sample of children. Whether this seemingly minimal difference is clinically meaningful is questionable.
Soil erosion and sediment yield in tropical mountainous watershed of northwest Thailand: the spatial risk assessments under land use and rainfall changes

A GIS-based method has been applied for assessing the spatial soil erosion risk and sediment yield in Mae Rim watershed, Northwest Thailand. The gross soil erosion in each watershed cell was estimated using the Revised Universal Soil Loss Equation (RUSLE) by thoroughly determining its various parameters. RUSLE results showed mean annual soil loss rate of 31 tonnes ha\(^{-1}\) yr\(^{-1}\), while the mean annual suspended sediment yield was 7.4 tonnes ha\(^{-1}\) yr\(^{-1}\). From the results of the spatial analysis between controlling factors and soil erosion, it indicated that bare land, field crop land and high steep slope were linked to extreme soil erosion (> 150 tonnes ha\(^{-1}\) yr\(^{-1}\)). With respect to soil erosion under land use and rainfall change, it was revealed that the conversion from deciduous forest to field crop area has very serious implications for soil erosion in the Mae Rim watershed. Indeed, transition from forest to agriculture may lead to erosion increase despite reduced rainfall. The results obtained from analysing scenario sensitivities identify synergistic effects on soil erosion hazard if bare land, field crop land and rainfall erosivity are increased simultaneously in the future.
Higher NMOS Single Event Transient Susceptibility Compared to PMOS in Sub-20nm Bulk FinFET

The single event transient (SET) susceptibility in the sub-20nm bulk FinFET process is studied in this letter. It is firstly found that NMOS is more sensitive to SET compared with PMOS, which is opposite to the planar CMOS process. A FinFET Technology Computer-Aid Design (TCAD) model is established to research the underlying physical mechanisms. Results show that two factors lead to the difference in SET susceptibility between NMOS and PMOS. First, the drift & diffusion (DD) plays more role than bipolar amplification (BA) in FinFET-PMOS, causing lower SET sensitivity than planar-PMOS. Second, source and drain (S/D) collect more charge in NMOS, causing more sensitivity of SET compared to PMOS.
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.
Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.
RL-OPC: Mask Optimization With Deep Reinforcement Learning

Mask optimization is a vital step in the VLSI manufacturing process in advanced technology nodes. As one of the most representative techniques, optical proximity correction (OPC) is widely applied to enhance printability. Since conventional OPC methods consume prohibitive computational overhead, recent research has applied machine learning techniques for efficient mask optimization. However, existing discriminative learning models rely on a given dataset for supervised training, and generative learning models usually leverage a proxy optimization objective for end-to-end learning, which may limit the feasibility. In this article, we pioneer introducing the reinforcement learning (RL) model for mask optimization, which directly optimizes the preferred objective without leveraging a differentiable proxy. Intensive experiments show that our method outperforms state-of-the-art solutions, including academic approaches and commercial toolkits.
ELF Electromagnetic Wave Across Sea Level from HED in Three-layered Medium

Exploring the electromagnetic wave propagation in the marine environment is extremely important in ship communication and marine information monitoring. Understanding the propagation law of electromagnetic waves in the marine environment plays a theoretical guiding role in the application related to the propagation of electromagnetic waves. This paper discusses the influence of the seabed medium in the stratified marine environment on the electromagnetic field in the air radiated from the low-frequency horizontal electric dipole (HED) located at the seawater. The conclusion shows that the existence of seabed media will affect the magnitude of electromagnetic field amplitude on the sea surface, but will not change the law of electromagnetic wave propagation in the air except different field intensity.
Cluster-based test vector re-ordering for reduced power dissipation in digital circuits

Optimizing testing power is a paramount concern in modern digital circuit design, particularly as the intricacy of circuits continues to rise. This issue becomes even more pronounced with the scaling down of feature sizes due to advancements in process technology. The increase in testing power dissipation, beyond the regular operational state, raises red flags for both designers and test engineers. This paper introduces a novel cluster-based approach, shedding light on the efficient reordering of test vectors to mitigate the heightened switching activity. This technique aims to reduce the power consumption during testing, while still ensuring efficient error detection, maintaining the test period, and preserving the original order of the scan chain. Importantly, this reordering method does not introduce any additional area or test time overhead, minimizing the risks associated with these factors. The potential impact of this approach is demonstrated through concrete examples drawn from ISCAS’89 benchmark circuits. The results showcase a notable 11% reduction in switching activity, all while preserving fault coverage levels.
GEO-INFORMATICS IN THE CONTEXT OF WATERSHED MODELING A CASE STUDY OF RAINFALL - RUNOFF MODELING

A view-shielding device for a machine designed to block the view of the machine's keypad or view screen by others positioned behind or adjacent to the user. The device includes two planar screens pivotal attached on the machine's front surface on opposite sides of the keypad or viewing screen. The screens, which are made of rigid, opaque material, are aligned vertically over the front surface of the machine. The screens are attached along their inside longitudinal edges to the front surface via hinges which enable the screens to pivot on the front surface approximately 180 degrees. By adjusting the relative positions of the screens on the front surface, the view angle of the keypad or viewing screen by others positioned behind or adjacent to the user may be blocked. In one embodiment, each screen includes a first panel structure. In an alternative embodiment, each screen includes a first panel structure and a second panel structure pivotally attached along their adjoining longitudinal edges. In still another embodiment, the hinges affixed to the machine are replaced with sliding hinges which enable the screens to slide horizontally over the front surface of the machine.
Wideband Antenna Based on Aperture-Coupled Magneto-Electric Dipole for Millimeter Wave Applications

In this paper, a wideband magneto-electric (ME) dipole antenna is designed, fabricated, and measured. The coaxial probe and aperture coupling technique are employed to excite the ME-dipole. The measured results show that the proposed antenna has the -10dB impedance bandwidth from 18 to 29.5 GHz leading to a 48.1% fractional bandwidth, and a stable gain over the whole operating bandwidth. The antenna element has a compact size of 0.58 $\lambda_{0}\times 0.45\lambda_{0}\times 0.17\lambda_{0}$. With the advantages of wide bandwidth, stable gain and miniaturization, the proposed antenna has great application potential in millimeter wave communication system.
Cloud-Assisted Security Framework for Drone-Enabled Offshore Communications

Maritime transport industry is a targeted sector for cyber security attacks as due to the inherent risks of transferring sensitive information for its smooth functioning. Digitization of maritime transport systems has led them vulnerable to various cybersecurity attacks. Vulnerable data, such as crew or passenger personal information, vessel location, route, schedule and other information can be obtained by the attackers and misused. To counter this, a novel lightweight authentication protocol has been put forward with the help of the drone technology using the 5th generation mobile network (5G) communication. The proposed scheme is analyzed to show its robustness against various security attacks, while consuming low communication and computation costs and achieving security and functionality requirements of anonymity and untraceability properties. A detailed simulation study using the network simulator (NS3) shows its impact on various network performance parameters.