Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceleration Breaks the Cells Defense Mechanisms against Vibration in Anthemis gilanica Calli

Vibration is a mechanical stress which happens in nature and affects many biological aspects of plants. In this research, the effect of acceleration and vibration was investigated on some physiological and biochemical responses of Anthemis gilanica in vitro. Calli were induced from leaf (LS) and root segments (RS) and were applied to different frequencies of vibrations (0, 50, and 100 Hz) and accelerations (1, 2, and 4 g) on the A. gilanica calli for 30 min. Results showed that vibration significantly increased relative water content (RWC), growth parameters, protein and proline contents, ascorbate peroxidase (APX), peroxidase (POX), and superoxide dismutase (SOD) activities and decreased total carbohydrate, malondialdehyde (MDA), H2O2 contents, and polyphenol oxidase (PPO) activity in both LS and RS calli. Inversely, increase of acceleration in vibrated calli decreased growth parameters, RWC, protein content, and POX activity and induced proline and carbohydrate accumulations, SOD, APX, and PPO activities as compared to vibration alone. Different responses of two callus types were observed, and the highest growth, protein content, and membrane stability were observed in LS calli as compared to RS calli. It found that high acceleration amplitude intensified the resonance effect of vibration by induction of lipid peroxidation and oxidative stress damage in A. gilanica.
Like
 in Korean English speech

This article focuses on the use of like by Korean speakers of English. South Korea is a fascinating context for world Englishes studies: English is generally learned as a foreign language, but deep political and historical ties facilitate a high visibility and prominent status of the language in Korean society. In this study, the use of like by Korean speakers of English with particular attention to discourse-pragmatic aspects are investigated with the help of the Spo-ken Korean English Corpus (SPOKE). Like is a high frequency item in SPOKE and clearly forms part of the Korean English repertoire across its functional range. Even though usage of like as discourse marker and particle is influenced by having spent time abroad and (self-reported) English proficiency, it forms part of the repertoire of nearly all surveyed speakers. Quotative like has also found a place in the Korean English repertoire, albeit to a lesser degree.
Looking Beyond the Provision of Injecting Equipment to People Who Use Anabolic Androgenic Steroids: Harm Reduction and Behavior Change Goals for UK Policy

Understanding of the choices and motivations of people who use anabolic androgenic steroids (AAS) for muscular enhancement has increased greatly in the past thirty years, along with understanding of a wide range of health harms associated with this form of drug use in the community. During this period the predominant public health intervention for this population in the UK has consistently remained the provision of injecting equipment to prevent blood borne virus (BBV) transmission. The study explored the health professionals’ and other stakeholders’ perceptions on: whether the current UK public health response is sufficient to address the needs of people who use AAS, and if not, what other needs they might have. This included an exploration of whether there were gaps in harm reduction strategies or other behavioral outcomes and interventions that were needed. Interviews with 27 stakeholders who provide support to people who use AAS in a variety of roles established consensus on the need for a range of interventions to reduce harm and risk in those that choose to use AAS, to prevent initiation, to motivate and support cessation, and to prevent relapse. Study findings indicate that while providing sterile injecting equipment remains essential, it should be considered a bare minimum. The challenge is to develop and deliver a range of harm reduction interventions that look beyond BBV prevention to provide appropriate support to who choose to use AAS at all points in their cycles of use and ultimately for those choosing the temporary or permanent cessation of use.
Observations on the Kinetics of Nitrification under Inhibiting Conditions Caused by Industrial Wastewater Compounds

Within the next few years nitrogen removal will be obligatory for both municipal and industrial wastewater treatment plants in Austria. During the operation of pilot plants, severe inhibition of the nitrification process has been observed in several cases. Further investigations have been carried out at two biochemical factories at temperatures in the range of 22 to 32 ±C, in one case at two pilot plants, in the other case at a full-scale plant in order to estimate kinetic parameters of nitrifying biomass under the particular inhibiting conditions of these wastewaters. In both cases competitive inhibition occurred during the start-up period for about two months. The ammonia nitrogen half-saturation coefficient for autotrophic biomass was in the range of 10 mg/l. In one case also maximum autotrophic specific growth rate varied in a wide range. It could be proved that the compounds of this wastewater have an inhibiting but no toxic effect on Nitrosomonas . The effluent ammonia concentration can be kept within the limits in case of competitive as well as non-competitive inhibition up to about 60% by increased minimum SRT. In case of severe non-competitive inhibition it is not possible to achieve feasible biological nitrification. In any case the inhibiting compounds should be located and removed from the wastewater.
Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients

INTRODUCTION: Machine learning algorithms and in silico mode ls for the COVID - 19 have been used to classify infectious people and predict their condition in time . OBJECTIVES: This study aims at creating a personalized model that combines machine learning and finite element simulation approach in order to predict development of COVID - 19 infection in patients. METHODS: The methodology combines several aspects (1) classification of patients into several classes of clinical condition (2) segmentation of human lungs in X ray images (3) finite element simulation to investigate the spreading of SARS-COV - 2 virion in the lungs. RESULTS: The findings show accuracy larger than 90% in all aspects of methodology. FE simulation has revealed that the distribution of airflow in the lung changes in time with the infectio n. CONCLUSION: The key benefit of our proposed method is that it combines several methods that will be further improved in order to create a truly unique combined methodology for predictive models in patients infected with COVID - 19.
Drying of Apricots Using a Proficient Dish Type Solar Air Heater

The present experimental work is to study drier in the form of solar air heater that dries Apricots (Prunus armeniaca). A small dish type solar air heater, connected to a drying chamber was developed in May, 2012 to evaluate its performance for drying apricots. The efficiency of this solar air heater was evaluated in May, 2012 at three air flow rates. Results showed that flow rates significantly (P< 0.000) affected the efficiency of the solar air heater. Efficiency improved from 20% at the natural flow rate of 0.01kg. sec to 42.6% 1
Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.
Exposure to Verbally and Physically Aggressive TV: In Search of a Cross-over Effect

ABSTRACT This study examined the contribution of verbally aggressive and physically aggressive TV exposure to self-reported verbal and physical aggression. Verbally aggressive TV exposure was calculated by respondents’ frequency of viewing 60 TV shows found to be popular among college students and coded for their aggressive content. A total of 637 emerging adults were surveyed for the main study. A path analyses using SPSS Amos with verbal and physical aggression as the primary endogenous variables was used to analyze the data. Exposure to physically aggressive TV programming was found to have a direct effect on self-reported verbal and physical aggression while exposure to verbally aggressive programming had an indirect effect via exposure to physically aggressive programming, thus partially supporting a “cross-over effect. ” Additionally, verbal aggression was found to be a positive predictor of physical aggression, both in real life and on the screen.
Health behaviours of nursing staff working in a shift system in John Paul II District Hospital in Wadowice

Streszczenie Cel pracy. Praca zmianowa jest bardziej szkodliwa dla organizmu niż praca w systemie dziennym – uniemożliwia respektowanie fizjologicznych rytmów okołodobowych człowieka. Aż 70% pracowników pracujących w systemie zmianowym nie jest w stanie określić dokładnego stopnia nietolerancji pracy zmianowej. Celem pracy było poznanie zachowań zdrowotnych pielęgniarek/pielęgniarzy pracujących w systemie zmianowym w zależności od wieku oraz sprawdzenie, czy istnieje zależność pomiędzy oddziałem, na jakim pracuje dana osoba, a poszczególnymi zachowaniami zdrowotnymi. Materiał i metody. Badania przeprowadzono w Szpitalu Powiatowym w Wadowicach. Objęto nimi 133 pielęgniarki i pielęgniarzy pracujących w systemie zmianowym. Badania zrealizowano metodą sondażu diagnostycznego za pomocą kwestionariusza stosowanego w badaniach psychologicznych z zakresu psychologii zdrowia – Inwentarza Zachowań Zdrowotnych autorstwa Z. Juczyńskiego. Wyniki. Średnia prawidłowych nawyków żywieniowych w badanej grupie wynosiła M = 3,37. Zachowania profilaktyczne wynosiły średnio M = 3,36, pozytywne nastawienie psychiczne M = 3,38, a praktyki zdrowotne M = 2,87. Ogólny wskaźnik nasilenia zachowań zdrowotnych wynosił w badanej grupie M = 77,87. Miejsce pracy nie miało istotnego statystycznie wpływu na zachowania profilaktyczne oraz pozytywne nastawienie psychiczne. Wnioski. Pielęgniarki/pielęgniarze pracujący w systemie zmianowym prezentują przeciętny poziom praktykowania zachowań zdrowotnych. Najwyższy poziom i największe nasilenie zachowań zdrowotnych prezentowały osoby pracujące na bloku operacyjnym. Im wyższy wiek pielęgniarek/pielęgniarzy, tym częstsze przestrzeganie prawidłowych zachowań zdrowotnych. Konieczne jest zaangażowanie kierownictwa szpitala w działania profilaktyczne ze względu na niski poziom podejmowania zachowań zdrowotnych przez personel pielęgniarski.
All‐optical control of spatial beam intensity in multimode fibres by polarisation modulation

The authors demonstrate that by injecting a combination of LP 11 modes in a few ‐ mode high ‐ birefringence fibres that all ‐ optical control of the spatial beam intensity at the fibre output is achieved by varying the polarisation at the fibre input. These results may be generalised to the cases where different and several higher order modes are employed.
Distractors Selectively Modulate Electrophysiological Markers of Perceptual Decisions

Abstract Current models of perceptual decision-making assume that choices are made after evidence in favor of an alternative accumulates to a given threshold. This process has recently been revealed in human EEG recordings, but an unresolved issue is how these neural mechanisms are modulated by competing, yet task-irrelevant, stimuli. In this study, we tested 20 healthy participants on a motion direction discrimination task. Participants monitored two patches of random dot motion simultaneously presented on either side of fixation for periodic changes in an upward or downward motion, which could occur equiprobably in either patch. On a random 50% of trials, these periods of coherent vertical motion were accompanied by simultaneous task-irrelevant, horizontal motion in the contralateral patch. Our data showed that these distractors selectively increased the amplitude of early target selection responses over scalp sites contralateral to the distractor stimulus, without impacting on responses ipsilateral to the distractor. Importantly, this modulation mediated a decrement in the subsequent buildup rate of a neural signature of evidence accumulation and accounted for a slowing of RTs. These data offer new insights into the functional interactions between target selection and evidence accumulation signals, and their susceptibility to task-irrelevant distractors. More broadly, these data neurally inform future models of perceptual decision-making by highlighting the influence of early processing of competing stimuli on the accumulation of perceptual evidence.
Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory

To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.
Digitalization for Sustainable Agri-Food Systems: Potential, Status, and Risks for the MENA Region

Digital technologies offer a potential solution to improve sustainability—economic, social, and environmental—of agri-food systems around the globe. While developed countries have led the innovation and adoption of digital agriculture, the potential impact in developing countries—including in the Middle East and North Africa (MENA) region—is massive. This article synthesizes existing evidence to review the potential and current contribution of digital technologies to the agri-food sectors in MENA. Digital agriculture shows promise in addressing the key challenges facing the agri-food sector across MENA countries. Improvements in primary production, supply chain and logistics performance, and optimized use of scarce natural resources (notably agricultural water) could be notable, if digital technologies can be implemented as envisioned. Available evidence shows that adoption of digital agriculture is at early stages, generally led by high-value agricultural production targeting domestic markets in Gulf countries and export markets in Mashreq countries. Economic sustainability appears the strongest force for current adoption, with less focus on social or environmental sustainability. Public policies should not only foster the adoption of digital technologies in MENA but also ensure equity of access, transparency of use, data protections, and labor protections. Policymakers should move beyond traditional, production-centric views to deliver also on social and environmental sustainability.
What Informs Farmers’ Choice of Output Markets? The Case of Maize, Cowpea and Livestock Production in Northern Ghana

This study analyses the main determinants of output market choices by rural farmers in northern Ghana amidst growing concerns of lack of lucrative markets for smallholder farmers. Using recent survey data collected from 448 households, the study applied the multinomial logistic regression (MLR) model with village markets as the base outcome. The findings revealed that association membership, access to storage facilities, openness to new production and marketing methods, access to financial services, knowledge of sustainable intensification (SI) practices, access to guaranteed market, availability of quality market services and distance to output market would likely influence the choice of a farmer to sell at a farm gate over village markets. Access to extension services was found significant in influencing farmers’ decisions to sell by the roadside. Also, gender, association membership, access to processing facilities, availability of quality market services and distance to output market would likely influence the choice of a farmer to do private sales. Furthermore, association membership, access to processing facilities, access to extension services and market information significantly influences the decision of farmers to use other market outlets (e.g., regional/district markets). The study concludes that the choice of market outlet used by farmers depends much on institutional and channel-specific characteristics. These findings have policy implications for the development of market policies, providing rural market infrastructure services, promoting SI practices and strengthening extension service delivery.
Prenatal diagnosis of cloacal anomaly in trisomy 21

The cloacal malformation is an extremely rare non-hereditary fetal anomaly that presents as a variety of defects. It predominantly affects females with a prevalence of 1 in 50,000 births. Here, we present the case of a 27-year-old primigravida at 32 weeks of gestation showing a large midline abdominopelvic cystic mass with a septum, bilateral hydroureteronephrosis with oligohydramnios in a female fetus on antenatal ultrasonography. Suspicion of the cloacal anomaly was made which was confirmed postnatally. Postpartum analysis of the placenta revealed trisomy 21.
GKEEP: An Enhanced Graph‐Based Keyword Extractor With Error‐Feedback Propagation for Geoscience Reports

As the amount of published geoscience literature grows, reading and summarizing texts of large collections has become a challenging task. Publication keywords can be considered basic components of knowledge structure representations and have been used to reveal knowledge concerning research domains. In contrast to data used in other research domains, the works on textual geoscience data that entail keyword extraction are limited. In this paper, we propose an unsupervised algorithm, the graph‐based keyword extractor with error‐feedback propagation (GKEEP), that enhances graph‐based keyword extraction approaches by using an error‐feedback mechanism similar to the concept of backpropagation. The proposed approach comprises the following steps. A preprocessed document is used as the input of the proposed model and is represented as a weighted undirected graph, where the vertices represent words and the edges represent the cooccurrence relationship between the words constrained by a window size. Subsequently, its nodes are ranked by their importance scores calculated by a graph‐based ranking algorithm. Consequently, all the words have their own scores, and they are used to compute the scores of keyword candidates. Subsequently, the Word2Vec method is applied to recalculate the scores of keyword candidates and rank the keyword candidates to select the final keyword. It also utilizes error feedback to boost the rankings of the most salient terms that would otherwise be deemed less important. With empirical experiments on two real data sets (including our newly built data set), the proposed GKEEP model outperforms state‐of‐the‐art unsupervised models and the existing graph‐based ranking models. The proposed method can effectively reflect intrinsic keyword semantics and interrelationships.
Long Short Term Memory network for Recognition of Daily Human Activity

: P reviously Human Activity Recognition has been solved by using the engineered features, but the main problem with this approach is that it requires specific domain knowledge. Adopting classical machine learning models has been effective but these methods completely ignore the time signal obtained from the sensors. But due to recent advancements, deep learning techniques like Long Short Term Model and recurrent neural networks have been effectively used to provide good results and classify human activities correctly as compared to machine learning models. So in this paper, we propose a LSTM framework to classify these activities and also compare its performance with classical machine learning models.
GHG Balance of Agricultural Intensification & Bioenergy Production in the Orinoquia Region, Colombia

Energy crop expansion can increase land demand and generate displacement of food crops, which impacts greenhouse gas (GHG) emissions mainly through land-use change (LUC). Increased agricultural productivity could compensate for this. Our study aims to evaluate the regional combined GHG emissions of increasing agricultural yields for food crop and beef production and using the generated surplus land for biomass production to replace fossil fuels in the Orinoquia region of Colombia until 2030. The results show that surplus land for biomass production is obtained only when strong measures are applied to increase agricultural productivity. In the medium and high scenario, a land surplus of 0.6 and 2.4 Mha, respectively, could be generated. Such intensification results in up to 83% emission reduction in Orinoquia’s agricultural sector, largely coming from increasing productivity of cattle production and improving degraded pastures. Biofuel potential from the surplus land is projected at 36 to 368 PJ per year, with a low risk of causing indirect LUC, and results in GHG emission reductions of more than 100% compared to its fossil fuel equivalent. An integrated perspective of the agricultural land use enables sustainable production of both food and bioenergy.