Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Safety Consciousness and Consumers’ Milk Purchasing Behavior: Evidence from a Developing Country

Abstract We study the effects of food safety awareness on consumers’ milk purchasing behavior in Nepal. We conducted consumer survey and employed an instrumental variable regression. We find education, income, and social network to influence food safety consciousness (FSC). Our results indicate the positive impact of FSC on weekly milk expenditure and probability of purchasing milk from milk cooperatives. Any policy that helps to improve the FSC levels will likely increase the purchase of safe milk from the modern market outlet, and lack of such awareness raising policies has prevented the market for safe food from evolving and expanding.
Penicillium purpurogenum, Unique P Mobilizers in Arid Agro-Ecosystems

A phosphatase and phytase producing fungus, Penicillium purpurogenum, was isolated and efficiency was tested in field on the enhancement of rhizosphere enzyme activities and mobilization of native unavailable phosphorus with pearl millet as a test crop in a loamy sand soil under arid environment. Seed inoculation with the fungi has significantly improved phosphatases (acid and alkaline), phytase, and dehydrogenase activities compared to uninoculated fields. The depletion of organic P was much higher than mineral and phytin P. The relative root growth (Kr) rate was greater between 28 and 35 days. The fungal contribution was significantly more than the plant contribution to the mineralization of mineral-P. However, both pearl millet and P. purpurogenum were equally competent for hydrolysis of phytin P. A significant improvement in plant biomass (30%), root length (21%), P uptake (6%), seed (19%) and straw yield (30%), and P concentration of shoot (15%), root (6%), and seed (33%) resulted from inoculation of P. purpurogenum. It was observed that P. purpurogenum can thrive well under arid-ecosystems. The fungus releases phosphatases and phytase, resulting in more solubilization/hydrolysis of soil unavailable P into plant available form, thereby enhancing the P uptake and pearl millet production in an arid environment.
Trivial Bypassing in GPGPUs

This letter presents trivial bypassing to detect and skip execution of trivial instructions in general-purpose graphics processing units (GPGPUs). During the execution of a program, a significant number of instructions are trivial; that is, the instructions do not need functional units for execution. The outcome of such instructions can be determined based on source operands. Execution of these instructions unnecessarily wastes hardware resources and reduces energy efficiency of GPGPUs. We propose a microarchitectural technique that detects and bypasses trivial instructions dynamically and during the runtime. By bypassing trivial instructions, the energy of functional units is reduced. In addition to functional units, the other component that benefits from trivial bypassing is the register file. We exploit register renaming to remap destination field of trivial instructions. Using register renaming, logical registers with the same values share the same physical registers. This reduces the number of accesses to physical registers and enhances the energy efficiency of GPGPUs. Evaluations using a wide range of applications reveal that trivial bypassing reduces the energy of GPGPUs by 8% with negligible impact on performance.
Role of lipopolysaccharide in the receptor function for bacteriophage Ox2

The sensitivity of Salmonella typhimurium as well as of Escherichia coli to the bacteriophage Ox2 was found to require, in addition to the OmpA protein, a certain type of rough LPS. Heptoseless mutants were resistant and unable to adsorb the phage. Mutants with less defective LPS chemotype were sensitive and could, except the second most defective chemotypes, adsorb Ox2. However, isolated LPS-free OmpA protein could bind the phage, and this binding could not be increased by adding LPS.
Intelligent Fault Diagnosis Approach Based on Composite Multi-Scale Dimensionless Indicators and Affinity Propagation Clustering

In this study, a new intelligent fault diagnosis approach based on composite multi-scale dimensionless indicators (CMDIs) and affinity propagation (AP) clustering is proposed to identify working conditions of mechanical components. For this goal, CMDIs as fault features are for the first time proposed to search more informative fault information hidden in the original vibration signals and useful variational mode decomposition signals. Next, two feature evaluation methods, Fisher criterion (FC) and self-weight criterion (SW), are introduced to search the top ranked CMDIs, respectively. Subsequently, AP clustering is applied to select the important CMDIs. The most important CMDI of each cluster is chosen as the final fault feature. Finally, these important CMDIs are fed into the following supervised learning classifiers to identify working conditions of mechanical components: k-nearest neighbors, linear discriminant analysis, naive Bayesian and random forests. Two experimental examples for different fault types of mechanical components are conducted. Experimental comparison results demonstrate that the combination diagnosis model based on CMDIs-FC-AP with naive Bayesian classifier would be most suitable for complex samples with a high dimensional feature set.
Microbial quality and safety of milk and milk products in the 21st century.

Milk and milk products have been utilized by humans for many thousands of years. With the advent of metagenomic studies, our knowledge on the microbiota of milk and milk products, especially as affected by the environment, production, and storage parameters, has increased. Milk quality depends on chemical parameters (fat and protein content and absence of inhibitory substances), as well as microbial and somatic cells counts, and affects the price of milk. The effects of hygiene and effective cooling on the spoilage microbiota have shown that proteolytic and lipolytic bacteria such as Pseudomonas or Acinetobacter spp. predominate the spoilage bacterial populations. These bacteria can produce heat-stable proteases and lipases, which remain active after pasteurization and thus can spoil the milk during prolonged storage. Additionally, milk can become contaminated after pasteurization and therefore there is still a high demand on developing better cleaning and sanitation regimes and equipment, as well as test systems to (quantitatively) detect relevant pathogenic or spoilage microorganisms. Raw milk and raw milk cheese consumption is also increasing worldwide with the growing demand of minimally processed, sustainable, healthy, and local foods. In this context, emerging and re-emerging pathogens once again represent a major food safety challenge. As a result of global warming, it is conceivable that not only microbiological risks but also chemical risks relating to presence of mycotoxins or plant toxins in milk will increase. Herein, we provide an overview of the major microbial hazards occurring in the 21st century.
Evaluation of an explicit vocabulary teaching intervention for children learning English as an additional language in primary school

Many children learning English as an additional language (EAL) possess lower levels of English vocabulary knowledge relative to their non-EAL English-speaking peers. Longitudinal work suggests that this group discrepancy does not decrease markedly over time as a result of regular classroom teaching. Twelve EAL learners with English vocabulary weaknesses took part in a low-intensity, 10-week vocabulary intervention. Working one-to-one with speech and language therapy students, children took part in weekly activities designed to promote receptive and productive knowledge of 20 target words within the Tier-2 vocabulary category. When assessed on a bespoke word knowledge assessment, children made statistically significant gains between pretest and posttest in both receptive and productive knowledge of taught words, and maintained this knowledge six months later. Multi-component explicit vocabulary instruction offering opportunities for active engagement and discussion is thus presented as one potentially effective means of promoting the Tier-2 vocabulary knowledge of EAL learners in primary school in England.
On Open-Set Speaker Identification with I-Vectors

Open-set speaker identiﬁcation systems ﬁrst need to decide if an utterance belongs to one of the known so called blacklist speakers and second identify the exact blacklist speaker. In this paper, an open-set speaker identiﬁcation system based on i-vectors is presented. The system consists of an outlier detector in combination with a classical closed-set speaker identiﬁcation chain and utilizes an effective preprocessing technique for i-vectors, called linear alignment. Its overall structure is justiﬁed both theoretically and experimentally by comparing multiple outlier detectors. In experimental evaluations, our proposed system reaches an improvement of 37 . 5% for the top-S Equal Error Rate (EER) and a 50% lower top-1 EER over the baseline sys-tem of the 1st Multi-target speaker detection and identiﬁcation Challenge Evaluation and improves upon all other published re-sults obtained on this dataset.
A Multi-condition Training Strategy for Countermeasures Against Spoofing Attacks to Speaker Recognizers

In this contribution, we are concerned with the design of effective strategies to train simple-to-use detectors of spooﬁng attacks to automatic speaker recognizers, i.e., systems able to directly map data into scores indicating the likelihood of an attack, as opposed to complex pipelines involving several independent steps required for training and inference. As such, given that artiﬁcial neural networks have been responsible for the shift from pipelines to end-to-end systems within several applications, we speciﬁcally target this kind of model. The main challenge in training neural networks for the applications considered herein lies in the fact that openly available spooﬁng corpora are relatively small due to the inherent difﬁculty involved in collecting/generating this kind of data. We thus employ a data augmentation strategy enabling the introduction of training examples which signiﬁcantly improves training data in terms of size and diversity. Neural networks trained on top of augmented training data are shown to be able to attain signiﬁcant improvement in terms of detection performance when compared to standard GMM-based classiﬁers.
A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning

An accurate and effective perception of environment is important for autonomous vehicle and robot. The perception system needs to obtain the 3D information of objects, which includes objects’ space location and pose. Camera is widely equipped on autonomous vehicle because of its price advantage. However, the monocular camera cannot provide depth information which is necessary for 3D object detection. Many algorithms based on monocular 3D object detection have been developed in recent years. Deep learning is popular for perception system which transforms image data from camera into semantic information. This paper presents an overview of monocular 3D object detection algorithms based on deep Learning and summarize the contributions and limitations of these algorithms. We also compare the performance of different algorithms on different datasets.
CMOS Analog AGC for Biomedical Applications

In this paper, we present the design of an analog Automatic Gain Control with a small silicon area and reduced power consumption using a 0.5 μ m process. The design uses a classical approach implementing the AGC system with simple blocks, such as: peak detector, difference amplifier, four-quadrant multiplier, and inversor amplifier. Those blocks were realized by using a modified Miller type OPAMP, which allows indirect compensation, while the peak detector uses a MOS diode. The AGC design is simulated using the Tanner-Eda environment and Berkeley models BSIM49 of the On-Semiconductor C5 process, and it was fabricated through the MOSIS prototyping service. The AGC system has an operation frequency of around 1 kHz, covering the range of biomedical applications, power consumption of 200 μ W, and the design occupies a silicon area of approximately 508.8 μ m × 317.7 μ m. According to the characteristics obtained at the experimental level (attack and release time), this AGC can be applied to hearing aid systems.
RAFM: A Real-time Auto Detecting and Fingerprinting Method for IoT devices

In recent years, with the rapid development of Internet of Things (IoT) technology, a large number of Internet of things devices such as network printers, webcams and routers have emerged in the cyberspace. However, the situation of network security is increasingly serious. Large-scale network attacks launched by terminal devices connected to the Internet occur frequently, causing a series of adverse effects such as information leakage and property loss to people. The establishment of a set of fingerprint generation system for Internet of things devices to accurately identify the device type is of great significance for the unified security control of the Internet of things. We proposed a RAFM which is a detection and identification system of IoT. RAFM consists two major module including auto detection and fingerprinting. RAFM collects messages sent by different Internet of things devices by means of passive listening. Based on the differences in the header fields of different devices, it USES a series of multi-class classification algorithms to identify device types. Simulation experiments show that RAFM can achieve an average prediction accuracy of 93.75%.
Screening for Plant Resistance to Sorghum Head Bug, Calocoris Angustatus Leth.

Sorghum head bug, Calocoris angustatus Leth. (Hemiptera: Miridae) is an important insect pest of sorghum in the semi-arid tropics. A technique to screen for resistance to head bugs under field conditions was tested. Planting infester rows of susceptible cultivars (CSH 1 or CSH 5), 20 days before the test material or planting early (40–45 days) flowering lines (IS 802, IS 13429 and IS 24439) along with the test material, split planting of test material at 15-day intervals, grouping test material according to maturity, and use of sprinkler irrigation to maintain high humidity, were found effective in increasing the efficiency of screening and selecting for resistance to head bugs.A headcage technique to screen for resistance under no-choice conditions has also been developed. Panicles infested with 10 pairs of bugs at pre- and half-anthesis, result in maximum population build-up and grain damage under headcage. This technique is useful for confirming the resistance observed under field conditions.Under natural infestation, five genotypes harboured relatively lower head bug numbers than the susceptible checks, but only three (IS 17610, IS 17618 and IS 17645) maintained their level of resistance under the headcage. Seed germination was > 70% in these genotypes compared to < 10% in CSH 1 and CSH 5. RésuméLa punaise des panicules du sorgho, Calocoris angustatus Leth. (Hémiptères: Miridés), constitue un insecte ravageur important des régions tropicales semi-arides. Une technique de criblage au champ pour la résistance aux punaises des panicules a été normalisée. Le semis des rangs infestants de cultivars sensibles (CSH 1 ou CSH 5), 20 jours avant le matériel d’essai, ou le semis des lignées (IS 802, IS 13429 et IS 24439) à floraison nâtive (40–45 jours) au même temps que le matériel d’essai, le semis échelonné du matériel d’essai aux intervalles de 15 jours, le regroupement du matériel d’essai selon la maturité, ainsi que la mise en place de 1’irrigation par aspersion pour maintenir un niveau élevé d’humidité sont des moyens qui se sont avérés efflcaces dans l’augmentation de l’efficacité du criblage et de la sélection pour la résistance aux punaises des panicules.Une technique de panicule encagée a également été mise au point pour le criblage pour la résistance dans des conditions de choix unique. Les panicules infestées avec 10 paires de punaises avant l’anthèse ont provoqué le maximum de pullulation et de dégâts aux grains sous la cage. Cette technique est utile pour la confirmation de la résistance observée en milieu réel. En conditions naturelles, cinq génotypes entretenaient des nombres de punaises relativement inférieurs par rapport aux témoins sensibles. Mais seulement trois génotypes (IS 17610, IS 17618 et IS 17645) ont pu maintenir leur niveau de résistance sous la cage. La germination des semences était > 70% pour ces génotypes par rapport à < 10% chez CSH 1 et CSH 5.
Understanding the control of yield formation in two- and six-row winter barley varieties to target disease management

xii Acknowledgements.xv List of Figures.xvii List of Tables.xx List of Abbreviations.xxvii General Introduction.1 Background . 5 1.1 Yield formation . 5 1.2 Barley Classification . 7 1.3 Source and sink . 9 1.3. 1 Sink capacity . 10 1.3. 2 Source . 14 1.3. 3 Investigating the source-sink balance . 15 1.4 Disease management . 19 1.4. 1 Important diseases in barley . 21 1.4. 2 Pathogen effects on plant growth . 23 1.4. 3 Chemical control (fungicides) . 24 1.4. 4 Fungicide movement and direct effects on the plant . 26 1.5 Knowledge gaps & Experimental objectives. 27 Investigating if the optimum fungicide strategy differs between a hybrid six-row and conventional two-row winter barley variety . 30 2.
β‐Amyloid peptide potentiates inflammatory responses induced by lipopolysaccharide, interferon ‐γ and ‘advanced glycation endproducts’ in a murine microglia cell line

β‐Amyloid (Aβ) plaques are characteristic hallmarks of Alzheimer's disease (AD). In AD, it has been suggested that activation of microglial cells might be the link between Aβ deposition and neuronal degeneration. Activated microglia are associated with senile plaques and produce free radicals and inflammatory cytokines. However, it is still not clear whether Aβ needs a prestimulated environment to exert its proinflammatory potential. Advanced glycation endproducts (AGEs), protein‐bound oxidation products of sugars, have been shown to accumulate in senile plaques and could induce a silent but chronic inflammation in the AD brain. We tested whether Aβ acts as an amplifier of a submaximal proinflammatory response initiated by exposure to chicken egg albumin‐AGE, lipopolysaccharide or interferon‐γ. Synthetic Aβ was used to produce three different samples (Aβ‐fibrilar; Aβ‐aggregated; Aβ‐AGE), which were characterized for β‐sheeted fibrils by the thioflavin‐T test and electron microscopy. As markers of microglial activation, nitric oxide, interleukin‐6, macrophage‐colony stimulation factor and tumour necrosis factor‐α production was measured. All three Aβ samples alone could not induce a detectable microglial response. The combination of Aβ preparations, however, with the coinducers provoked a strong microglial response, whereby Aβ‐AGE and fibrilar Aβ were more potent inflammatory signals than aggregated Aβ. Thus, Aβ in senile plaques can amplify microglial activation by a coexisting submaximal inflammatory stimulus. Hence, anti‐inflammatory therapeutics could either target the primary proinflammatory signal (e.g. by limiting AGE‐formation by AGE inhibitors or cross‐link breakers) or the amplifyer Aβ (e.g. by limiting Aβ production by β‐ or γ‐secretase inhibitors).
Rate of spread of fires in Calluna vulgaris-dominated moorlands.

Summary 1. Calluna-dominated heaths occur throughout Europe but are in decline across their range. There is growing interest in using prescribed burning for their management, but environmental and social change will impact future fire regimes. Understanding fire behaviour is vital for the sustainable use of fire, but no robust models exist to inform management. 2. Shrub fuels display complex fire behaviour. This is particularly true in UK moorlands which are unusual in their fuel structure and moisture regime, being dominated by live fuel and an oceanic climate. 3. We burnt 27 experimental fires in the Scottish uplands during the legal burning season using a replicated experimental design. Plots were assigned to one of three commonly identified growth phases. We estimated a range of prefire fuel characteristics, including heterogeneity in fuel structure. We recorded wind speed and direction and estimated rate of spread (RoS). 4. Redundancy analysis was used to investigate the relationship between fire behaviour parameters as a whole and control variables. Fuel structure and heterogeneity, wind speed and canopy fuel moisture content were strongly related to variation in fire behaviour. 5. Best subsets regression was used to generate models of fire spread based on wind speed, vegetation height, canopy fuel moisture and an index of fuel heterogeneity. RoS was determined largely by wind speed, but this interacted strongly with vegetation structure. Changes in fuel horizontal continuity and vertical structure reduced rates of spread in low wind speeds. 6. Synthesis and applications. Live fuel moisture and fuel heterogeneity play an important role in dampening fire behaviour, aspects of shrub fuels that have previously not been examined in detail. Careful use of fire for moorland management increases habitat diversity and creates fire-safe landscapes. Escaped prescribed fires burn large areas, homogenize landscapes and have severe impacts on ecosystem services. The complex relationship between fuel structure and wind speed implies that changes in behaviour can be rapid and unexpected. Models can be used to assess fire hazard prior to prescribed burning and to choose fuels that can be burnt safely under prevailing or forecast conditions.