Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrashort pulse laser dicing of thin Si wafers: the influence of laser-induced periodic surface structures on the backside breaking strength

High power electronic chips are usually fabricated on about 50 µm thin Si wafers to improve heat dissipation. At these chip thicknesses mechanical dicing becomes challenging. Chippings may occur at the cutting edges, which reduce the mechanical stability of the die. Thermal load changes could then lead to sudden chip failure. Ultrashort pulsed lasers are a promising tool to improve the cutting quality, because thermal side effects can be reduced to a minimum. However, laser-induced periodic surface structures occur at the sidewalls and at the trench bottom during scribing. The goal of this study was to investigate the influence of these periodic structures on the backside breaking strength of the die. An ultrafast laser with a pulse duration of 380 fs and a wavelength of 1040 nm was used to cut a wafer into single chips. The pulse energy and the number of scans was varied. The cuts in the wafer were investigated using transmitted light microscopy, the sidewalls of the cut chips were investigated using scanning electron and confocal microscopy, and the breaking strength was evaluated using the 3-point bending test. The results indicated that periodic holes with a distance of about 20–30 µm were formed at the bottom of the trench, if the number of scans was set too low to completely cut the wafer; the wafer was only perforated. Mechanical breaking of the bridges caused 5 µm deep kerfs in the sidewall. These kerfs reduced the breaking strength at the backside of the chip to about 300 MPa. As the number of scans was increased, the bridges were ablated and the wafer was cut completely. Periodic structures were observed on the sidewall; the roughness was below 1 µm. The surface roughness remained on a constant level even when the number of scans was doubled. However, the periodic structures on the sidewall seemed to vanish and the probability to remove local flaws increases with the number of scans. As a consequence, the breaking strength was increased to about 700 MPa.
Optimizing Tomato Water and Fertilizer Uses in Smallholder Farms in South Africa Using the Piloten Model

The objectives of this paper are twofold: (i) to calibrate and validate the PILOTEN crop model for tomato (Solanum lycopersicum L.), and (ii) to use the model to examine the response of tomato yield to different irrigation and fertilization scheduling scenarios under varying climatic conditions in order to draw up management recommendations for smallholder farms. The calibration and validation were performed by using data collected from field experiments carried out in the north‐east of South Africa, on shallow Ferrosol and Luvisol soils. The scenarios dealt with: (i) the initial soil‐water availability (fulfilling or not the soil‐water reserve at planting); (ii) the irrigation dose frequency (high versus low irrigation doses ranging from 10 to 40 mm per application); (iii) the fertilization dose frequency (full versus split fertilization application). The results indicated that only the fertilization scheduling may significantly affect tomato yields, while irrigation scheduling has a minor role. However, compared to the local official irrigation guidelines, it is recommended to slightly increase the irrigation doses during the first 4 weeks of growth for tomato grown between early September and mid‐December, and to slightly decrease the doses in the second part of the growing cycles starting in February and May. It is also suggested to avoid applying the entire fertilizer dose after seedling planting for tomato grown between mid‐February and late June. Copyright © 2020 John Wiley & Sons, Ltd.
Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each.
A rough set approach to recognize images of handwritten digits

This paper reports our current effort to explore the feasibility of applying the hierarchy of the probabilistic decision tables to recognize images of handwritten single digits. Such a hierarchy is built under the framework of the variable precision rough set model and potentially used to classify the unknown digits into appropriate categories. In this paper, we adopted the methodology similar to the one we used in facial recognition but with simplifications and modifications. The experiments were employed with the images from MNIST database. According to the results, how many different digits the images in the training set came from had the most important impact on the performance of this methodology. It always worked better when given training sets consisting of the images from fewer digits.
Learning FX trading strategies with FQI and persistent actions

Automated Trading Systems are constantly increasing their impact on financial markets, but learning from historical data, detecting interesting patterns and producing profitable strategies are still challenging objectives for autonomous agents. This holds true especially in the intraday Foreign Exchange market, where prices are heavily affected by random noise and high non-stationarity. In this volatile market, opportunities are present at many time-scales, but not all of them can be easily learnt. The signal-to-noise ratio has, indeed, a critical impact on the ability of autonomous agents to learn effectively. In this paper, we formulate multi-currency trading as a Markov Decision Process and we train an agent via Fitted-Q Iteration, a Reinforcement Learning value-based algorithm. Focusing on a three-currencies framework, we study the importance of tuning the control frequency, in order to obtain effective trading policies. We backtest the developed approaches on real data from the FX market considering two currency triplets, comparing results employing either a single pair or both ones at the same time.
Evaluating fork after withholding (FAW) attack in Bitcoin

Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network. This paper aims to quantitatively examine the profitability of FAW attack in Bitcoin system with block propagation delay. We first establish a novel analytic model, which can analyze FAW attack in the Bitcoin system. Then we apply the model to design metric formulas for the Bitcoin system. These formulas can be used to evaluate the miner profitability (in terms of miner reward) and the impact of FAW attack on system throughput (in terms of transactions per second). We make a comparison of FAW attack and other attacks (including selfish mining and selfholding attacks). Experimental results reveal that FAW adversaries can get more rewards in the network with propagation delay than without delay. The results of the comparison of selfish mining and FAW attacks show that adversaries with large computational power can conduct selfish mining or selfholding attack to get more rewards, but they can conduct FAW attack to profit more when their computational power is small. Our work can be used to analyze Bitcoin-like blockchain systems and help design and evaluate security mechanisms.
SIMD support to improve eclipse OpenJ9 performance on the AArch64 platform

Just-in-time (JIT) compilers achieve application portability and improved management of large code-bases by abstracting the architecture specific details from programmers. Eclipse OMR and Eclipse OpenJ9 invest extensively in JIT technology to efficiently execute architecture-neutral Java bytecode. OMR is a robust language runtime builder, and OpenJ9 is a managed language runtime that consumes OMR. The targeted domains of OMR and OpenJ9 include AArch64, a 64-bit version of the ARM architecture. AArch64 is a popular member of the embedded computing market, where computing infrastructure resources (e.g., CPU, memory) are constrained. The concept of SIMD (Single Instruction, Multiple Data) instructions primarily evolved to accelerate the performance of multimedia applications such as motion video, real-time physics and graphics where repetitive operations were involved on large arrays of numbers. This paper discusses the steps taken to add SIMD support to OMR for AArch64. The implementation of advanced SIMD and floating-point instructions are also discussed, which cover vectorized mathematical operations, including addition, subtraction, multiplication, and division for supported data-types. We validate our implementation through relevant OMR tril tests, present two microbenchmarks VectorizationMicrobenchmark and Sepia Tone Filter and a set of standard benchmarks, which leverage the OpenJ9 autovectorization process in AArch64. The AArch64 vectorized operations are evaluated against non-vectorized, but similar operations using Eclipse OpenJ9. We demonstrate an improvement of up to four times in execution speed of certain vector arithmetic operations.
Learning regulator influence on internal risk weights

Current financial regulation allows banks to use internal models for determining their risk-based capital requirements. Economic theory suggests that banks have incentives to report downward-biased risk weights, a problem that regulators aim to address by auditing banks' models. We study whether different audit standards by different regulators affect internal risk weights, indicating that banks subject to laxer standards may report too low risk weights. We present a supervised learning method to study this question that accounts for the fact that risk weights for the same portfolio could vary across banks even if there was no misreporting. Our approach is based on a generalized least squares technique that learns an individual model for each bank, which we combine with Group Lasso shrinkage to study the importance of different features. We analyze a large sample of internal risk weights for European banks. Our results show that regulator characteristics do have an economically significant impact on risk weights and that their importance is similar to that of relevant economic variables. We conclude that this provides empirical support for the claim that banks do indeed report biased risk weights, thereby undermining regulations aimed at preventing another financial crisis.
Nature‐based solutions in soil restoration for improving agricultural productivity

Soil is a living and dynamic body, which is prone to degradation under conventional agricultural practices. Healthy soil is one of the most important pillars of sustainability as it delivers several ecosystem services along with its control on microbial activity, nutrient recycling, and decomposition. Nature‐based solutions can play an important role in restoring soil quality for enhanced agricultural productivity and sustainability. This article discusses various nature‐based options available for improving soil quality. Indigenous practices such as sheep penning, tank silt application, green manuring, and refuse from croplands and households have the potential to restore and maintain soil fertility. Biofertilizers can add nutrients (N fixers), fixe up to 300 kg N ha through biological nitrogen fixation (BNF) and facilitate (nutrient solubilizer and mobilizers) nutrient availability in the soil. Biochar, a commonly used product, can increase soil moisture availability by 8%–10% and aids in mitigating climate change through C‐sequestration. Biochar may have a climate change mitigation potential of 1.8 Pg CO2‐C equivalent per year. When added to the soil, it not only acts as a nutrient source; but also acts as a soil amendment. Biogas slurry, the effluent from biogas reactors, contains various nutrient elements that can enrich soil fertility. The holistic approach in a farming system, through integration of different enterprises, reduces dependence on off‐farm resources. Soil management through nature‐based options will maintain crop productivity and sustainability in the long run without any adverse effects on the environment.
Effects of Population Age on the Annual Net Primary Productivity and Seed Yield of Artificial Grasslands on the Eastern Qinghai-Tibetan Plateau

The forage yield and quality of artificial grasslands typically decline rapidly over time after planting. Moreover, the reason for this decline of artificial grassland remains unclear. Here, we evaluated the relationship between years since planting and annual net primary productivity (ANPP), seed yield, and reproductive allocation (ie., the ratio of seed yield to annual net primary productivity) for eight common gramineous species separately planted in monoculture artificial grasslands in the Qinghai-Tibetan Plateau. We utilized T 50 values (the time when ANPP or seed yield drop to 50% of their maximum values) to quantify the decline rates of eight artificial grasslands respectively, and additionally evaluated the influence of evolutionary history and other plant attributes on T 50 (ANPP) and T 50 (seed yield). We found that both ANPP and seed yield decreased linearly over time following planting for all species. ANPP generally declined more rapidly than did seed yield. The reproductive allocation of Deschampsia cespitosa, Festuca kirilowii, Festuca kryloviana, and Festuca sinensis increased significantly over time after planting. We did not observe any association of evolutionary history, seed mass, plant height, reproductive allocation, ANPP (second year), seed yield (second year) with T 50 (ANPP) and T 50 (seed yield). In addition, clonal propagation mode may influence declines of artificial grassland, and evolutionary history may shape shifts in reproductive allocation with plant population aging. This study was the first to utilize T 50 values for the study of artificial grassland declines. Our research framework thus has the potential to guide future quantitative studies of artificial grassland declines.
Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.
Plant Kinases in the Perception and Signaling Networks Associated With Arthropod Herbivory

The success in the response of plants to environmental stressors depends on the regulatory networks that connect plant perception and plant response. In these networks, phosphorylation is a key mechanism to activate or deactivate the proteins involved. Protein kinases are responsible for phosphorylations and play a very relevant role in transmitting the signals. Here, we review the present knowledge on the contribution of protein kinases to herbivore-triggered responses in plants, with a focus on the information related to the regulated kinases accompanying herbivory in Arabidopsis. A meta-analysis of transcriptomic responses revealed the importance of several kinase groups directly involved in the perception of the attacker or typically associated with the transmission of stress-related signals. To highlight the importance of these protein kinase families in the response to arthropod herbivores, a compilation of previous knowledge on their members is offered. When available, this information is compared with previous findings on their role against pathogens. Besides, knowledge of their homologous counterparts in other plant-herbivore interactions is provided. Altogether, these observations resemble the complexity of the kinase-related mechanisms involved in the plant response. Understanding how kinase-based pathways coordinate in response to a specific threat remains a major challenge for future research.
Profiling CPU Behavior for Detection of Android Ransomware

Android devices continue to dominate the market for global smartphone users, thus making them an ideal target for malicious software developers. In the past, side-channel attacks have been used for malicious purposes where attackers monitor system data such as power consumption, electromagnetic emissions, and CPU timing to infer sensitive user information. Likewise, researchers have begun to use mathematical models and machine learning techniques on side-channel data as a means to detect malicious activity or malware. In this paper we look at an alternative method for malware detection using side-channel analysis of CPU frequency data on Android smartphones. We perform a case-study analysis to validate feasibility of the technique and use a nonlinear phase space analysis (NLPSA) for detecting anomalous behavior based on power variations. Our study looks at a single ransomware artifact (WannaLocker) evaluated on a single Android device and operating system. As a contribution, we are the first to utilize the Perfetto system tracing tool as a potential data provider, which now comes standard on platforms since Android 9 Pie OS. Our case study results show that our NLPSA approach can classify and detect execution of the ransomware sample on a real Android device with perfect prediction accuracy in training, thus giving an initial validation to the approach.
Fuzzy Artificial Immune System based Generators Preventive Maintenance Scheduling

Preventive Maintenance Schedule (PMS) is a technique used for the scheduling of electric generators in power systems. PMS increases system reliability, and decreases the overall system production and maintenance costs. Recently, expert systems, fuzzy logic sets, simulated annealing, genetic algorithms, and evolutionary programming were developed and used to solve PMS problems. In this work, a new technique based on a fuzzy artificial immune system is used to solve the PMS problem. The fuzzy logic is used to handle the uncertainty associated with the PMS problem, such as the uncertainty in load, production cost, and the spinning reserve. The idea is to mimic the human immune system response due to an antigen attack. Once feasible solutions (candidates) are determined, the fuzzy technique will be applied to identify the candidate that best satisfies the constraints related to the PMS problem. Results from the proposed technique were compared with complementary decision variable technique to check the efficacy of the fuzzy immune system technique. Test results demonstrate the viability of the proposed technique to solve the PMS problem considering uncertainties.
Finding Suitable Gate Insulators for Reliable 2D FETs

Field-effect transistors (FETs) based on two-dimensional (2D) materials hold the promise to allow for ultimately scaled channel thicknesses of single monolayers. Due to their atomically small thicknesses, the gate control is enhanced while sizable mobilities in the 2D semiconductors are maintained. Thus, in recent years considerable research efforts have focused on 2D FETs and explored various 2D semiconductors and their properties. However, the key challenge of finding suitable gate insulators for 2D FETs has yet received little attention. In this paper, criteria are formulated for identifying suitable gate insulators for reliable 2D FETs. The criteria are grouped into three main categories, these are scaling requirements, reduction of insulator-related charge traps, and technological requirements for the insulator deposition. In addition, possible candidates for gate insulators for 2D FETs are evaluated with respect to the formulated criteria, and important research questions for future investigations are identified.