Miracle-dz/UniFAR-FTUs huggingface dataset:
The Formation of Acetoin and Diacetyl during Fermentation, and the levels found in Wines

Acetylmethylcarbinol (acetoin) formed during yeast fermentations of sugar reaches a maximum of about 25 to 100 ppm about midway through fermentation, and then largely disappears, presumably by reduction to 2,3-butanediol. The peak of acetoin content coincided with the maximum level of acetaldehyde formation, which follows a similar pattern of rise and decline. The acetoin content of sweet (dessert) wines produced by the addition of wine spirits to half fermentation was much higher (50 to 200 ppm) than corresponding dry wines produced by complete fermentation (about 5 to 20 ppm). Significantly higher levels of acetoin (up to 350 ppm) were found in sherry samples produced by the submerged flor yeast fermentation than in those of comparable baked-type sherry. However, diacetyl was not higher (less than 1 ppm) as a result of the oxidative conditions of the submerged flor process. The creatine-αnaphthol colorimetric method was used for acetoin for measurement, but it gave erroneously high values for diacetyl in the presence of acetoin, so diacetyl was determined by a colorimetric nickel dimethylglyoxime method. The possible significance of acetoinic compounds in wines is discussed.
Experimental Framework and Graphical User Interface for Research in Bioinformatics: GRAY – Gene Rapid Analysis

This paper has presented an experimental framework based on artificial neural networks such as the multilayer perceptron. The aim is to represent and classify genes with the use of deep learning algorithms. It is aimed at reducing the time needed for training such a network in addition to lowering the hardware requirements for its virtualization without sacrificing accuracy. Futhermore, this paper has proposed an open source, easy to modify graphical user interface using modern technologies for researchers within the field.
Perceived Risk in Online Purchase Intention

The purpose of this study was examining the effect of six risk factors in online transactions on consumer purchase intentions. Data collected through surveys and multiple linear regression used to test hypotheses. One hundred two respondents participated in the online survey, and the data is valid and reliable. The results of the study prove that the six risk factors perceived by consumers in shopping online have a negative and significant impact on consumer purchase intentions. Among the factors studied, time/psychological risk factors are the main contributors to consumers in considering making an online purchase.
Mapping Human Activity Volumes Through Remote Sensing Imagery

The spatial concentration of the human activity is a crucial indication of socioeconomic vitality. Accurately mapping activity volumes is fundamental to support the regional sustainable development. Current approaches rely on mobile positioning data, which record information about human daily activity but are inaccessible in most cities due to privacy and data sharing concerns. Alternative methods are needed to provide more generalized predictions on extensive areas while maintaining low cost. This study demonstrates how remote sensing imagery can be used through an end-to-end deep learning framework for reliable estimates of human activity volumes. The neighbor effect, representing the inherent nature of spatial autocorrelation in the volumes, is incorporated to improve the network. The proposed model exhibits strong predictive power and demonstrates great explainability of physical environment on variations of activity volumes. Landscape interpretations based on hierarchical features provide both object-based and region-based insights into the coevolvement of landscape and human activity. Our findings indicate the possibility of extensively predicting activity volumes, especially in areas with limited access to mobile data, and provide support for the promising framework to better comprehend broad aspects of the human society from observable physical environments.
Energy Efficient Transmission Power Control Policy of the Delay Tolerable Communication Service

In recent years, the development of wireless communication leads to an explosive growth of energy demand, widely application of smart devices and rapid emergence of services. So, the energy efficient communication is expected urgently to save power and prolong the lifetime of the resource-constrained terminal devices. Especially in 5G age, the excellent spectrum efficiency provides more opportunity to save power by adjusting the transmission power of the delay tolerable (DT) service. Meanwhile, although the tradeoff between energy efficiency and service delay plays a non-negligible role in the energy efficient communication, it is not exploited sufficiently due to the time variation and randomness of wireless communication channel. For this reason, the fundamental tradeoff between energy efficiency and delay of the DT service is investigated and analyzed. And, the optimal problem of energy efficient communication for DT service is formulated as a Markov Decision Process (MDP) which can be solved effectively by statistical dynamic programming (SDP) since the perfect channel state information (CSI) is hard to obtain. To improve the utility of research, the approximate SDP (ASDP) and Q-learning are also investigated to overcome the limitation of the curse of dimensionality and model-based algorithm respectively.
A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments

Speaker verification (SV) has recently attracted considerable research interest due to the growing popularity of virtual assistants. At the same time, there is an increasing requirement for an SV system: it should be robust to short speech segments, especially in noisy and reverberant environments. In this paper, we consider one more important requirement for practical applications: the system should be robust to an audio stream containing long non-speech segments, where a voice activity detection (VAD) is not applied. To meet these two requirements, we introduce feature pyramid module (FPM)-based multi-scale aggregation (MSA) and self-adaptive soft VAD (SAS-VAD). We present the FPM-based MSA to deal with short speech segments in noisy and reverberant environments. Also, we use the SAS-VAD to increase the robustness to long non-speech segments. To further improve the robustness to acoustic distortions (i.e., noise and reverberation), we apply a masking-based speech enhancement (SE) method. We combine SV, VAD, and SE models in a unified deep learning framework and jointly train the entire network in an end-to-end manner. To the best of our knowledge, this is the first work combining these three models in a deep learning framework. We conduct experiments on Korean indoor (KID) and VoxCeleb datasets, which are corrupted by noise and reverberation. The results show that the proposed method is effective for SV in the challenging conditions and performs better than the baseline ${i}$ -vector and deep speaker embedding systems.
A New Dual-PM Excited Doubly Salient Machine for Traction Applications

This paper proposes a new dual-PM excited doubly salient machine (DPM-DSM) in which the PMs are employed in both the stator yoke and the slot openings. The proposed design is derived from the existing variable flux PM machine with PMs in stator yoke by adding another layer of PMs in the slot openings. The PMs in both the stator yoke and the slot openings can interact with the armature field to generate electromagnetic torque, and consequently the torque capability of the proposed machine can be improved. Therefore, the proposed machine is very suitable for traction applications which require large torque density. Meanwhile, modular design of the stator can be achieved, which can simplify the manufacturing. The rotor only has salient poles, which is mechanically robust and works in high speed. The FEM-TS coupled method, namely the finite element method coupled with a Tabu search algorithm, is used to find the optimal design of the proposed DPM-DSM. The electromagnetic performance of the proposed machine is comprehensively investigated using FEM. The results show that with the aid of slot PMs the electromagnetic torque of the proposed machine can be increased by 62%.
Concept of an autonomous mobile robotic system for bridge inspection

In the next decade, many old bridges will be exposed to increasing traffic loads and destructive environmental conditions. Measurement methods like laser scanning, infrared thermography, photogrammetry, ground penetrating radar, or ultrasonic scanning are used on single robotic systems to partially support the inspectors. However, time-consuming manual inspections for crack detection, measurement, and documentation are still necessary. This paper describes the concept of an autonomous mobile robotic bridge inspection system. The proposed concept for an unmanned ground vehicle (UGV) is achieved by a trade-off of different mobile platforms, sensor systems for mapping, localization and inspection, and fist tests assessing the feasibility. We use a small concrete bridge in Freiburg (Germany) with various cracks for testing the sensors, the UGV concept, and initial tests of the mobile platform. This results in the choice of selecting the weatherproof version of the mobile robotic platform Husky from Clearpath Robotics. It is equipped with Swift Navigation's Duro real-time kinematic (RTK) system, a heading system, an inertial measurement unit (IMU), a base station, and software for semi-autonomous navigation. In the next step, we compare different sensor systems. For mapping and localization, we decide to use the 360 spherical camera Ladybug 5+ from FLIR Systems and a Velodyne VLP-16 light detection and ranging (LiDAR). High-resolution cameras allow recording damages on the bridge's surface. We perform first tests using monochrome and colour cameras. After evaluating different sensor integration concepts, we present a preliminary design of the UGV including integrated sensors.
The Effects of Regulatory Focus on Anticipated Emotions under the Different Anticipatory Results: Take Coastal Universities in China as an Example

ABSTRACT Li, Z.Z. and Zhang, M. , 2020. The effects of regulatory focus on anticipated emotions under the different anticipatory results: Take coastal universities in China as an example. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 956–959. Coconut Creek (Florida), ISSN 0749-0208. We explored the effects of regulatory focus on anticipated emotions under the different anticipatory results. We used an experiment to conduct the study with 32 college students in coastal China. The results indicated that anticipated emotions differed according to the current motivational strategy which are related to an anticipatory result; focusing on promotion rather than prevention led to stronger anticipated positive emotions and less anticipated negative emotions in response to regardless of success or fail anticipatory results; and also anticipatory results have significant effects on anticipated emotions, anticipate successful results rather than anticipatory fail results led to more intense anticipated positive emotions and less anticipated negative emotions. Implications of these results are also discussed in this study
The Influence Mechanism of Consumers Participating in the Value Co-creation of Crowdsourcing Based on Hierarchical Model and Content Analysis in the Coastal Basin

ABSTRACT Bai, C.X. and Yu, B.Q. , 2020. The influence mechanism of consumers participating in the value co-creation of crowdsourcing based on hierarchical model and content analysis in the coastal basin. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 717–723. Coconut Creek (Florida), ISSN 0749-0208. The business model of the crowdsourcing value co-creation highlights greater advantages and potential with the further development of coastal basin economy under the sharing economy. This paper sorts out the concepts of the value co-creation and crowdsourcing and the influencing factors of the value co-creation. Then the samples are selected by purposive sampling and direct extraction based on zbj.com and 680.com from the perspective of the public participants. It identifies the influencing factors through QSR Nvivo11.0 and SPSS23.0. And the model of influence mechanism of consumers participating in the value co-creation of crowdsourcing is proposed by clustering and classification. On this basis, this paper uses a hierarchical model to construct a judgment matrix, perform hierarchical ranking and consistency checking, and obtain the weight value and ranking of influencing factors. While further enriching the value Co-creation theory, it also provides corresponding evidence and suggestions for how to set appropriate crowdsourcing value to create content and how to adopt effective incentive measures in the sharing economy.
CoDaRR: Continuous Data Space Randomization against Data-Only Attacks

The widespread deployment of exploit mitigations such as CFI and shadow stacks are making code-reuse attacks increasingly difficult. This has forced adversaries to consider data-only attacks against which the venerable ASLR remains the primary deployed defense.Data-Space Randomization (DSR) techniques raise the bar against data-only attacks by making it harder for adversaries to inject malicious data flows into vulnerable applications. DSR works by masking memory load and store instructions. Masks are chosen (i) to not interfere with intended data flows and (ii) such that masking likely interferes with unintended flows introduced by malicious program inputs. In this paper, we show two new attacks that bypass all existing static DSR approaches; one that directly discloses memory and another using speculative execution. We then present CoDaRR, the first dynamic DSR scheme resilient to disclosure attacks. CoDaRR continuously rerandomizes the masks used in loads and stores, and re-masks all memory objects to remain transparent w.r. t. program execution. Our evaluation confirms that CoDaRR successfully thwarts these attacks with limited run-time overhead in standard benchmarks as well as real-world applications.
Anonymous Identity Authentication Scheme for VANETs

ABSTRACT Sun, M. ; Zhang, J., and Guo, Y.Y. , 2020. Anonymous identity authentication scheme for VANETs. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 203–209. Coconut Creek (Florida), ISSN 0749-0208. The application of vehicular ad hoc networks (VANETs) is of great significance to traffic optimization and traffic safety. The network security of VANETs becomes the key to restrict the development of VANETs. In this paper, we propose an anonymous authentication scheme for VANETs, in which we avoid using certificates in the authentication process between the RSU and the vehicle. This identity-based authentication scheme solves the problem of certificate storage and management in the authentication protocols based on the public key infrastructure(PKI). In our scheme, the system key isn't stored in the roadside unit (RSU) or the tamper-proof device (TPD) of the vehicle like some other schemes. When the RSU communicates with the vehicles, the local key generated by the RSU is used to sign the transmitted message, so that even if the local key of the RSU has been leaked, the security of the entire system will not be affected. And the RSU can update the local key to improve the security of the communication. The scheme uses timestamps in authentication, key agreement and message signing, which effectively avoids replaying attacks. The analysis shows that the proposed scheme can satisfy the security and privacy requirements of VANETs. The message authentication computation costs and communication overhead are improved.
Training Effectiveness Evaluation System of Migrant Workers along Yangtze River Valley

ABSTRACT Mo, J.; Mao, C.M. , and Zuo, Z.Y. , 2020. Training effectiveness evaluation system of migrant workers in coastal cities. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 984–987. Coconut Creek (Florida), ISSN 0749-0208. Based on Kirkpatrick's four-level model ,combined with the principle of index selection and the characteristics of migrant workers coastal cities, the paper initially establishes the evaluation index of training effect on the four levels of reaction layer, learning layer, behavior layer and result layer. Using the method of AHP and fuzzy comprehensive to evaluate the training effect of migrant workers along Yangtze River valley. Finally, the paper put forward the way to improve the training effectiveness of migrant workers along Yangtze River valley.
Performance of sheep on New Zealand hill country: III. Growth and composition of wethers born in three further years and slaughtered at three ages

The body weights and carcass composition of wether lambs and hoggets of different breeds and crosses run on steep New Zealand hill country and slaughtered at one of three ages (5-6 mth, 10 mth, 15 mth), were measured. The breeds and crosses examined in the trial were Merino x Romney, Romney, Drysdale x Romney, Drysdale, Cheviot, Coopworth, Perendale, and Poll Dorset x Romney. Published results for Merinos and Corriedales from a previous year of the trial were incorporated into overall breed growth rankings. When comparing the mean dressed carcass weights of the superior breeds with those of the Romney for the years that these other breeds were present for comparison, the Cheviot wethers averaged + 3.3 kg, the Coopworth + 3.3 kg, the Perendale + 3.9 kg, and the Dorset x Romney +4.8 kg heavier. Most lambs of these breeds could have been drafted for slaughter off the hills at acceptable carcass weights and grades before their first winter, whereas animals of the remaining breeds and crosses were sui.
Mesenchymal Stromal Cells’ Therapy for Polyglutamine Disorders: Where Do We Stand and Where Should We Go?

Polyglutamine (polyQ) diseases are a group of inherited neurodegenerative disorders caused by the expansion of the cytosine-adenine-guanine (CAG) repeat. This mutation encodes extended glutamine (Q) tract in the disease protein, resulting in the alteration of its conformation/physiological role and in the formation of toxic fragments/aggregates of the protein. This group of heterogeneous disorders shares common molecular mechanisms, which opens the possibility to develop a pan therapeutic approach. Vast efforts have been made to develop strategies to alleviate disease symptoms. Nonetheless, there is still no therapy that can cure or effectively delay disease progression of any of these disorders. Mesenchymal stromal cells (MSC) are promising tools for the treatment of polyQ disorders, promoting protection, tissue regeneration, and/or modulation of the immune system in animal models. Accordingly, data collected from clinical trials have so far demonstrated that transplantation of MSC is safe and delays the progression of some polyQ disorders for some time. However, to achieve sustained phenotypic amelioration in clinics, several treatments may be necessary. Therefore, efforts to develop new strategies to improve MSC’s therapeutic outcomes have been emerging. In this review article, we discuss the current treatments and strategies used to reduce polyQ symptoms and major pre-clinical and clinical achievements obtained with MSC transplantation as well as remaining flaws that need to be overcome. The requirement to cross the blood-brain-barrier (BBB), together with a short rate of cell engraftment in the lesioned area and low survival of MSC in a pathophysiological context upon transplantation may contribute to the transient therapeutic effects. We also review methods like pre-conditioning or genetic engineering of MSC that can be used to increase MSC survival in vivo, cellular-free approaches—i.e., MSC-conditioned medium (CM) or MSC-derived extracellular vesicles (EVs) as a way of possibly replacing the use of MSC and methods required to standardize the potential of MSC/MSC-derived products. These are fundamental questions that need to be addressed to obtain maximum MSC performance in polyQ diseases and therefore increase clinical benefits.
Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged – therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables’ energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.