Miracle-dz/UniFAR-FTUs huggingface dataset:
Metal–Oxide Nanowire Molecular Sensors and Their Promises

During the past two decades, one–dimensional (1D) metal–oxide nanowire (NW)-based molecular sensors have been witnessed as promising candidates to electrically detect volatile organic compounds (VOCs) due to their high surface to volume ratio, single crystallinity, and well-defined crystal orientations. Furthermore, these unique physical/chemical features allow the integrated sensor electronics to work with a long-term stability, ultra-low power consumption, and miniature device size, which promote the fast development of “trillion sensor electronics” for Internet of things (IoT) applications. This review gives a comprehensive overview of the recent studies and achievements in 1D metal–oxide nanowire synthesis, sensor device fabrication, sensing material functionalization, and sensing mechanisms. In addition, some critical issues that impede the practical application of the 1D metal–oxide nanowire-based sensor electronics, including selectivity, long-term stability, and low power consumption, will be highlighted. Finally, we give a prospective account of the remaining issues toward the laboratory-to-market transformation of the 1D nanostructure-based sensor electronics.
Predicting Environmental Chemical Toxicity using a New Hybrid Deep Machine Learning Method

Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures.
Map-view restorations of the South Island, New Zealand: a reappraisal of the last 10 Myr of evolution of the Alpine and Wairau faults

ABSTRACT Retro-deformation of horizontal and vertical displacements is used to reconstruct the evolution of the South Island plate boundary faults since the Late Miocene. Finite dextral offset across the Alpine Fault is estimated from a system of N-S reverse faults displaced from Otago to the Glenroy-Matakitaki area, where the N-S tectonic trends are generally interpreted as a ‘bend’ of the Alpine Fault. Increasing dextral offset on the Alpine Fault was accompanied by a NE-migrating wave of exhumation and uplift of its hanging wall, tracked by syntectonic terrestrial deposits. Strike-slip and dip-slip components both decrease near the ‘bend’, where deformation is transferred to the Wairau and Marlborough faults. The Wairau Fault offsets dextrally by c. 50 km the N-S faults of the Glenroy-Matakitaki area, correlated with the Waimea-Flaxmore Fault System of Nelson. Finite dextral separation along the Alpine Fault is 410 km, and the N-S ‘bend’ is interpreted as a transpressive stepover between the Alpine and the Wairau faults. The lack of connection between the Alpine and Wairau faults, combined with slip transfer to the Hope Fault over the last 2 Myr, do not favour propagation of coseismic rupture from the Alpine Fault to the Wairau Fault across the ‘bend’.
Research on case preprocessing based on deep learning

Considering the problem of missing fields in the criminal case system, this article proposes a deep learning algorithm to extract the features of the case description and fill in the missing value. Due to Chinese expressions and characteristics of criminal cases, we make both character vectors and word vectors to present text embedding. Character vectors are from bert model. Word vector is trained by long short‐term memory model with attention. The experiment uses 13,890 data totally. This work is an extension of our short conference proceeding paper. The results show that the combination of characters and words can effectively improve the accuracy of the conference paper by 9%. This is the first time to cascade the character and word dimensions on the criminal case information preprocess and it can provide higher quality data especially for the crime data mining.
The Study of Adsorption Process of Pb Ions Using Well-Aligned Arrays of ZnO Nanotubes as a Sorbent

Abstract Well-structured ZnO nanotubes are obtained by a self-selective etching method with lowering temperatures of growth during the hydrothermal process. The structural and optical properties of the obtained nanostructures are investigated by various conventional methods. The goal of the research is to compare the efficiency of ZnO nanotubes to that of ZnO nanorods during lead adsorption process from aqueous solution and demonstrate that hollow nanostructures are more effective than solid nanostructures of the same morphology due to their larger effective surface. Both nanotubes and nanorods are obtained under similar growth conditions: neither growth solution composition, nor concentration is changed. ZnO morphology is switched only by changing temperature during the growth process. The measurements are carried out to assess the efficiency of the adsorption per unit weight of ZnO nanorod and nanotube capacity of static adsorption.
Bronze Age to Medieval vegetation dynamics and landscape anthropization in the central pre-Pyrenees

The varved sediments of Lake Montcortès (central Pre-Pyrenees) have provided a continuous high-resolution record of the last ca. 3000 years. Previous chronological and sedimentological studies of this record have furnished detailed paleoenvironmental reconstructions. However, palynological studies are only available for the last millennium, when the landscape around the lake had already been transformed by humans. Therefore, the primeval vegetation of Montcortès and the history of its anthropogenic transformations remains unknown. This paper presents a palynological analysis of the interval between the Late Bronze Age and the Early Medieval period, aimed at recording the preanthropic conditions, the anthropization onset and the further landscape transformations. During the Late Bronze Age (ca. 1100 BCE to 770 BCE), the vegetation did not show any evidence of human impact. The decisive anthropogenic transformation of the Montcortès catchment vegetation and landscape started at the beginning of the Iron Age (770 BCE) and continued during Roman and Medieval times in the form of recurrent burning, grazing, cultivation, silviculture and hemp retting. Some intervals of lower human pressure were recorded, but the original vegetation never returned. The anthropization that took place during the Iron Age did not cause relevant changes in the sediment yield to the lake, but a significant limnological shift occurred, as manifested in the initiation of varve formation, a process that has been continuous until today. Climatic shifts seem to have played a secondary role in influencing vegetation and landscape changes. These results contrast with previous inferences of low anthropogenic impact until the Medieval Period, at a regional level. It could be interesting to verify whether the same pattern – i.e., Iron Age anthropization and Early Medieval intensification of human pressure – may be a recurrent pattern for mid-elevation Pyrenean landscapes below the tree line.
Promoting Social-Emotional Development In Children Experiencing Economic Hardship Using TBRI® Nurture Group©

ABSTRACT The purpose of this study was to examine the effect of occupational therapy group intervention using Trust Based Relational Intervention® (TBRI®) Nurture Groups© on preschool children’s social, emotional, and behavioral development in a Head Start program. This study used a retrospective mixed method, quasi-experimental design with non-equivalent groups and pretest-posttest measures. The intervention site teaching staff participated in 16 hours of TBRI® Caregiver training and the children participated in 12 weeks of Nurture Group© interventions. The Strengths and Difficulties Questionnaire (SDQ) and Teaching Strategies GOLD® (TSG) assessment measured child development in social, emotional, and behavioral domains. The intervention site teaching staff participated in a focus group to determine their perceptions of the intervention’s effectiveness. This data was evaluated using grounded theory methodology. SDQ: The children experienced statistically significant changes in the emotional symptoms and prosocial behavior domains. TSG: Results indicated a statistically significant main effect of site on objectives of Forms Relationships with Adults, Responds to Emotional Cues, and Balances Needs & Rights of Self and Others. Themes identified from qualitative analysis illustrated how the teachers highly valued the TBRI® training and adapted their teaching strategies to incorporate the TBRI® principles of empowering, connecting, and correcting. Teaching staff reported increased perceived social-emotional skills in the children and detailed how the children’s behavior changed to demonstrate these skills. Occupational therapy group intervention supports the social-emotional development of children attending Head Start. Occupational therapy practitioners need to engage in universal and targeted approaches to promote resiliency in this population of at-risk children.
THE EFFECT OF THE ACTIVITIES WITHIN THE SCOPE OF THE 2023 EDUCATION VISION DOCUMENT ON TEACHERS’ PROFESSIONAL IMAGE PERCEPTIONS

This research aimed to present the effect of the activities conducted within the context of the 2023 Education Vision Document on the perceived teacher image by determining teachers’ perceptions in regards to the teacher image existing in today’s society. The qualitative research was conducted with the phenomenology design. The research group was composed of teachers (n=57) who completed a non-thesis master’s degree program in the field of Classroom Teaching at Amasya University, Institute of Social Sciences during 2016-2018 academic years. The research data were obtained from teachers’ written statements provided for the open-ended questions in the question form developed by the researcher. Content analysis method was used in the analysis of the collected data. While the participating teachers generally emphasized the sanctity of the teaching profession in their answers, they also stated that the perceived image of the teaching profession in today’s society was basically negative. The variables affecting the perception of the teaching profession in society were classified under the following themes: sociological changes, local practices, employee rights, policy makers, media, parents, students, teachers, teacher training and qualification of prospective teachers. Teachers’ suggestions to contribute to the image of the teaching profession were gathered under the themes of teacher motivation, teacher training, employee rights, media, innovative practices and training support for teachers. In the context of the obtained data, it can be argued that some of the actions have been put forward so far in regards to 2023 Education Vision Document, and the practices expected to be implemented in the near future will positively contribute to the perceived image of the teaching profession in society. Keywords: teacher image, teacher’ image perceptions, 2023 education vision document
Evaporizing-deposition to synthesize micro- and nanoscale spherical anatase TiO2 film

Micro-and nanoscale spherical anatase TiO2 films have been successfully prepared via evaporizing–deposition route. The morphology and the crystalline structure of the samples were characterized in more detail with field emission scanning electron microscopy (FE–SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The TiO2 films with micro-and nanoscale surface structures can be constructed by simply adjusting the evaporation condition of TiCl4 in the air after being annealed. These surfaces can be turned from superhydrophilic to super-hydrophobic after modification with a thin layer of hydrophobic silicone, mainly depending on the surface morphology.
Ischemic Stroke Prediction by Exploring Sleep Related Features

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.
Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach

Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances.
Hydrogenotrophic Denitrification of Groundwater Using a Simplified Reactor for Drinking Water: A Case Study in the Kathmandu Valley, Nepal

High nitrate-nitrogen (NO3−–N) content is a typical feature of groundwater, which is the primary water source in the Kathmandu Valley, Nepal. Considering the Kathmandu Valley’s current problem of water scarcity, a user-friendly system for removing NO3−–N from groundwater is promptly desired. In this study, a simplified hydrogenotrophic denitrification (HD) reactor was developed for the Kathmandu Valley, and its effectiveness was evaluated by its ability to treat raw groundwater. The reactor operated for 157 days and showed stability and robustness. It had an average nitrogen removal efficiency of 80.9 ± 16.1%, and its nitrogen loading rate and nitrogen removal rate varied from 23.8 to 92.3 g–N/(m3∙d) and from 18.3 to 73.7 g–N/(m3∙d), respectively. Compared to previous HD reactors, this simplified HD reactor is a more user-friendly option for the Kathmandu Valley, as most of the materials used for the reactor were locally available and require less maintenance. The reactor is recommended for groundwater treatment at the household level. It has a current treatment capacity of 40 L/d, which can fulfill the daily requirements for drinking and cooking water in a household with 4–5 people.
An Insight into the Diffusion of Direct Dyes into Cellulosic Materials: Effect of Fabric Characteristics, Dye Structure, and Bath Composition

ABSTRACT The comprehension of capillarity phenomenon is important in chemical, printing, and textile dyeing. The current paper provides an insight into the diffusion of direct dyes into cellulosic materials with regard to the change of fabric characteristics, dye structure, and bath composition. First, an experimental device was developed and series of experiments were carried out using cotton as textile fabric model and direct dyes (Direct Orange 39, Direct Red 79, and Direct Red 80) as wicking liquids. Data were analyzed based on the evolution of diffusion rate [D (cm2/s)] versus C/C0. Results exhibited that the capillary ascension of dyes depends on weft count, weave structure, yarn type, angle contact, wet agent, dye structure, and dye concentration. The D (cm2/s) values were much higher than for twisted yarn. The tortuosity lowered the diffusion of the dye along with fabrics. The rate of water transport increased with weft count and it became more important when the structure of fabric is more tightened. Both inter-yarn space and vertical position of the fabric limited the ascension of the liquid. In brief, along this study, we demonstrated that the proposed apparatus could provide a potential evaluation of wicking phenomenon along with textile materials.
The Cultural and Historical Trail Map of Peranakan Heritage in the Andaman Provincial Cluster in Thailand

This study explored the trace of Peranakan heritage in the Andaman provincial cluster and presented the Cultural and Historical Trail Map of Peranakan heritage in Andaman provincial cluster. The concept of development communication objectives was used to create a research framework. Research results in the "complement" phase showed 117 traces of Peranakan heritage in the Andaman provincial cluster. As for the "facilitate" phase, 83 sites were qualified. An analysis of the relationship of qualified sites with Peranakan culture revealed five significant connections between each location and culture: 1) Peranakan ancestral monument, 2) Peranakan shrine, 3) Sino-European architectural building, 4) Peranakan residential areas, and 5) Peranakan cultural and historical learning center. Upon completion of the last phase, "extend," 6 tourist maps were created to display 8 cultural sites in Ranong province, 15 sites in Phang Nga province, 33 sites in Phuket province, 8 sites in Krabi province, 14 sites in Trang province, and 4 sites in Satun province, respectively. This study, thus, has produced a visual map in each province which is a catalytic communication tool to promote tourism development at the regional level and national level.
Ladin, varieties of Val di Fassa

Ladin (ISO 639-3: lld) is a Romance language spoken in the Italian Central-Eastern Alps by a community of about 30,000 speakers (Dell’Aquila 2010). The classification of Ladin within Western Romance has been the subject of a long-lasting scientific and at times ideological debate, particularly because at the end of the 19th century the region was contested between the new-born Italian state and the Habsburg empire. The varieties of Ladin share phonetic-phonological, morphological, syntactic and lexical features with the other languages spoken in the Central-Eastern Alps, such as Friulian and Romansh, thus leading to the identification of the Rhaeto-Romance group (Haiman & Benincà 1992). However, in Ladin there are still many linguistic phenomena that connect it to the Romance dialects of Northern Italy. Therefore, a clear assignment of Ladin to a group is by no means a simple and uncontroversial operation (Salvi 2016).
From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.
Effects of Long-Term Use of Compost on N2O and CO2 Fluxes in Greenhouse Vegetable Systems

ABSTRACT This study was based on a long-term field trial of three different greenhouse vegetable growing systems which were started in March 2002. Fertilizer nitrogen and organic carbon inputs in three treatments in the spring of 2015 were as follows: (1) conventional system (CON): cow manure and dry chicken manure composts (about 146 kg N ha−1 and 1483 kg C ha−1) and urea (about 525 kg N ha−1); (2) low-input system (LOW): cow manure and dry chicken manure composts (about 325 kg N ha−1 and 3160 kg C ha−1) and urea (about 263 kg N ha−1); and (3) organic system (ORG): cow manure and dry chicken manure composts (about 651 kg N ha−1 and 5456 kg C ha−1). N2O and CO2 fluxes were monitored in the growing season of 2015 (from March to August) using static chamber and gas chromatography techniques. Results showed that: (1) The N2O and CO2 fluxes in three treatments all reached their peaks within 4 days and 8 days after irrigation, respectively. N2O emissions were positively correlated with soil temperature, soil water-filled pore space (WFPS), and NO3−-N contents, while strong association between CO2 emissions and soil temperature was observed. (2) Lower cumulative N2O emission and direct emission factor (p < 0.05) were observed in ORG than that from LOW and CON. (3) No significant differences of cumulative CO2 emissions were observed in three treatments, but CO2 emissions proportion of fertilizer carbon inputs and soil organic carbon stock in ORG was significantly lower (p < 0.05).