A solid structure, such as a road, building wall or envelop, used as a solar collector is considered an effective and new way to use renewable energy. This paper focused on the temperature characteristics of four structures exposed to sunshine: asphalt, red brick, composite cement and concrete road slab. Furthermore, the collected heat based on a hydraulic system was investigated experimentally. For the four structure slabs, their temperature differences are due to solar radiation absorption varied greatly by the material’s heat absorptance and color. Through the test, asphalt slab attained the highest temperature and had the weakest reflection among the structures. Compared with the others, the temperature of the asphalt slab was greater by 8.1%, 14.9% and 16.4% than the brick, composite cement and concrete, respectively. The reflection intensity growth ratio was defined and indicates the growth potential for absorbing radiation in the solid slab surface. From the experiments, it was concluded that a suitable selection of road materials can greatly improve the thermal absorption, conduction and penetration into the solid slab. The collected heat capability was approximately 250 W/m2 to 350 W/m2 in the natural summer condition. A black coating or a surface modification can collect more heat, reaching greater than 250 W/m2. The solar collecting heat efficiency with a surface configuration of the road slab can reach above 30% in the summer time.

Bacterial collections are invaluable tools for microbiologists. However, their practical use is compromised by imprecise taxonomical assignation of bacterial strains. This is particularly true for soft rotting plant pathogens of the Pectobacterium genus. To solve this difficulty, we analyzed the taxonomic status of 265 Pectobacterium strains deposited at CIRM-CFBP collection from 1944 to 2020. This collection gathered Pectobacterium strains isolated in 27 countries from 32 plant species representing 17 botanical families or from non-host environments. MLSA approach completed by genomic analysis of 15 strains was performed to update the taxonomic status of these 265 strains. Results showed that the CIRM-CFBP Pectobacterium collection harboured at least one strain of each species to the exception of P. polonicum. Yet, 6 strains could not be assigned to any of the described species and may represent at least two new species. Surprisingly, the P. versatile species, recently described in 2019, is the most prevalent species among CIRM-CFBP strains. Analysis of P. versatile strains revealed that this species is endemic all over the world on various host plants and environments. At the opposite, other species gathered strains isolated from only one botanical family or exclusively from fresh water environment. Our work also revealed new host plants for several Pectobacterium spp.

While there are millions of reptile images available online, they are not well organized and not easily findable, accessible, interoperable, or reproducible (FAIR). More importantly, they are not standardized and thus hardly comparable. Here we present a reference database of more than 14,000 standardized images of 1,045 reptile species (969 lizard and 76 snake species), that are based on preserved specimens in 20 different collections, including 533 type species of genera and type specimens of 72 species. All images were taken with standardized views, including dorsal and ventral body shots as well as dorsal, ventral, and lateral views of the heads and other body parts. Although only 11 out of the 20 collections are cross-referenced in Vertnet, some others are indexed in GBIF, and this fraction will certainly grow in the near future. The utility of this and similar image collections will further grow with additional material and further cross-referencing, e.g., to DNA sequence databases or citizen science projects. The images are searchable and freely available on Morphobank.org (Project 5121) and on Figshare.com.

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

The newly discovered coronavirus (COVID-19) has disrupted traditional methods of conducting research, particularly qualitative research. However, there remains a number of methods by which qualitative data can still be collected. These include the use of digital voice, video, and text-based tools, online surveys, and content analysis. Text-based sources can help to overcome the limitations of time and space, and also can be cost-effective. This chapter draws from data collected from 12 participants across Zimbabwe and demonstrates how these tools can be used to generate data or to sample data that is already available to satisfy research questions and meet research objectives. It recommends researchers to experiment with new ways of collecting qualitative data while also observing safety protocols and ethical considerations.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

In the face of geopolitical threats in Artsakh, the preservation of Armenia's epigraphic heritage has become a mission of both historical and cultural urgency. This project delves deep into Armenian inscriptions, employing advanced digital tools and strategies like the Oxygen text editor and EpiDoc guidelines to efficiently catalogue, analyze, and present these historical treasures. Amidst the adversities posed by Azerbaijan's stance towards Armenian heritage in Artsakh, the digital documentation and preservation of these inscriptions have become a beacon of cultural resilience. The XML-based database ensures consistent data, promoting scholarly research and broadening accessibility. Integrating the Grabar Armenian dictionary addressed linguistic challenges, enhancing data accuracy. This initiative goes beyond merely preserving stone and text; it is a testament to the stories, hopes, and enduring spirit of the Armenian people in the face of external threats. Through a harmonious blend of technology and traditional knowledge, the project stands as a vanguard in the fight to ensure that Armenia's rich epigraphic legacy and the narratives they enshrine remain undiminished for future generations.

This study aimed to assess prescription transfer and medicines collection through Wasfaty, an electronic prescription service recently introduced in Saudi Arabia. A link to a cross-sectional online questionnaire was sent to all students and staff at the University of Jeddah, targeting beneficiaries who received e-prescriptions at the University Medical Centre (n = 2067). The questionnaire comprised 20 items under the following sections; demographics, patient perceptions and satisfaction with the Wasfaty service, and the availability of medicines. Of the 217 questionnaires received, the majority were filled by females (n = 125, 57.6%). Most were satisfied with the initial registration process of Wasfaty (n = 183, 84.1%). However, almost one-third of the participants reported that they could not find the prescribed medicines (n = 64, 29.7%), and most of them had to look for another pharmacy to obtain their treatment (n = 138, 63.9%). Respondents voiced their displeasure owing to the lack of access to certain pharmaceuticals, including anti-hypertensives and antidiabetics. This pilot study identified some challenges relating to the use of the Wasfaty service. Further attention to these challenges is required from the service providers, and a large-scale national study is warranted.

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

The world is facing a major health crisis, the global pandemic of COVID-19 caused by the SARS-CoV-2 coronavirus, for which no approved antiviral agents or vaccines are currently available. Here we describe a collection of codon-optimized coding sequences for SARS-CoV-2 cloned into Gateway-compatible entry vectors, which enable rapid transfer into a variety of expression and tagging vectors. The collection is freely available via Addgene. We hope that widespread availability of this SARS-CoV-2 resource will enable many subsequent molecular studies to better understand the viral life cycle and how to block it.

Community mobilisation is a vital process for raising awareness and increasing participation in healthcare interventions, research and programs that require human sample collection and mass management. In this report, we present the community mobilisation approach undertaken for the implementation of granular schistosomiasis and soil-transmitted helminths operational mapping and assessment in Ekiti State Nigeria. The mobilisation was conducted in 177 communities/wards of the 16 Local Government Areas. A total of 15,340 urine and stool samples were collected in 34 days. The efficacy and success of the strategy was evaluated through three performance metrics: Community compliance rate, the participant response rate at community level and overall compliance response rate of the 4 most sensitive LGAs. Community compliance was 93.7% as sample collection was denied in 9 communities. Two other communities demanded return of collected samples. Participant response rate at the community level was 86.7%. Three of the 4 sensitive LGAs (based on previous assessment programs) demonstrated satisfactory compliance rates of 100%, while a response rate of 64.0% was computed for one of the LGAs. We believe our approach contributed to effective community mobilisation & awareness and the developed model has potential for improving participation rates in large healthcare assessments and intervention programs.

Music cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional music cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. In the digital age, effectively protecting and inheriting these music cultural heritages has become an urgent problem to be addressed. Therefore, this paper proposes an application method based on digital twin technology, exploring how to protect and inherit music cultural heritages through digital twin technology. By leveraging digital twin technology, a virtual museum dedicated to showcasing the richness and historical connotations of music cultures is created, preserving and simulating the soundscapes of historical music eras. Through the integration of audio archives, 3D modeling, and interactive displays, users can immerse themselves in the experience of historical music in the digital space. This paper evaluates the feasibility and cultural preservation value of this digital music history museum through the creation of music digital twin technology instances and user survey feedback, and discusses the prospects of digital twins in the field of music cultural heritage.

Proso millet is an important allotetraploid cereal crop, however, it is the least studied species of the Poaceae family as it is an under-utilized crop. Genomic resources of proso millet are very limited compared to the major crops. Understanding of genetic relationships among germplasm resources is important for future breeding endeavors. In the present study, the SSR markers were employed to assess the polymorphism and genetic diversity of 100 millet accessions from different countries, which were tested in the dry steppe zone conditions of the Akmola region from 2020 to 2022. The use of 20 SSR markers detected a total of 47 alleles with an average allele number of 2.35 per locus among these proso accessions. Nine of them were polymorphic among genotypes, which suggests that these SSR markers can be used for genetic studies. The results showed a moderate level of polymorphism information content (PIC) averaged at 0.424, ranging from 0.125 to 0.795. The markers SSR 67, SSR-82, SSR-85 and SSR-109 showed high PIC values, 0.536, 0.756, 0.795 and 0.758 respectively. Markers SSR 85 and SSR 86 significantly correlated to agronomic traits, such as productive tillering (PT) and grain yield (GY). The genetic structure, UPGMA cluster and PCoA assay indicated that the accessions originated from Central Asia had higher genetic diversity. Based on Structure (K=3), all the accessions were divided into three groups, whereas the gene pool originated from Central Asia were detected in all the three clusters. Based on principal component analysis (PCA), the accessions of Central Asian origin were genetically closer to the North Asian group.

In order to improve the charge collection efficiency, we simulated and experimentally tested the doping structure of the electron multiplication layer in EBCMOS. In this paper, we simulate the charge collection efficiency of EBCMOS under different doping methods by modeling the collisional scattering of electrons with solid atoms in semiconductor materials and combining the transport trajectories of electrons in the electron multiplication layer, the simulation results indicate that using a layered doping structure to reduce the thickness of the index heavily doped layer can effectively optimize the electric field distribution in the electron multiplier layer and reduce the recombination rate of electrons. The optimized doping structure has a significant effect on improving charge collection efficiency. Based on the simulation results, doped samples were prepared and tested. The test results showed that the charge collection efficiency obtained under the condition of a P-type silicon substrate thickness of 7μm and an index heavily doped layer thickness of 1μm was 72.65%, reducing the thickness of the index heavily doped layer to 0.1μm, the charge collection efficiency obtained can reach 86.27%, which proves that reducing the thickness of the index heavily doped layer can effectively improve the charge collection efficiency of EBCMOS devices.

The sand-dust horizontal flux is an important parameter for the study on aeolian sand transport, as well as an important foundation. In this study, a field experiment was developed to measure the data of aeolian transport and microclimate during different dust events with an auto sand sampler, a piezoelectric saltation sensor (H11-Sensit) and a 10 m high meteorological tower in Ta Zhong, the hinterland of the Taklimakan Desert from July to August in 2010. Then, the sampling efficiency of auto sand sampler and horizontal dust flux of near surface were analyzed based on observed data. The results were as follows: sand collector skip turnover increased with the increase of the intensity of dust weather frequency increases, the power function relationship y=2.115 x0.9841, R2 = 0.9206, flip frequency per minute increased from 0.2794 times to 1.3041 times, change is obvious; With the strength of the weather, time to flip the average sediment is shrinking. Sandstorm weather, skip to flip a volume of 3.7160 g, grade I flying sand weather flip a volume of 4.0275 g, the amount of class II flying sand weather turns over a 5. 0035g.The horizontal dust flux of different dust events that calculated with the equation Q=256M; the maximum of one dust event was about 190.335 kg, and the minimum was 1.2 kg. Overall, the sand transportation rate increased with wind speed. However, the changes of sand transportation rate did not quite fit in with wind speed during some dust events, and in this case the corresponding surface temperature was significantly higher. The experimental data obtained can provide theoretical basis for regional sand control and enacting effective engineering measures.

The road pavement condition affects safety and comfort, traffic and travel times, vehicles operating cost and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all the road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification and quantification phases of the procedure.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

Background: Due to the importance of Avicenna in the history of philosophy and his special views and ideas, the views of this philosopher have always been examined by scholars and researchers. This is why many researchers have been published in connection with Avicenna's philosophy. Thereby, the comprehensive study in relation to the review of previous studies about philosophy of Avicenna is important. Methods: The Web of Science Core Collection has been used to review documents, and the data has been analyzed in VOSviewer. Results: Based on the findings in the WoS and also the maps made by VOSviewer, Types of documents, languages, documents per year, countries, journals, authors, citations, and keywords were analyzed, and based on these data and analysis the limitations of studies are realized.

This study analyzes the relevance of governance structure for efficiency in revenue collection by Brazilian Water Utilities (WUs). Data was collected from the National Information System on Sanitation (SNIS) for 127 Brazilian WUs, covering a balanced longitudinal panel from 2018 to 2022. Governance structures evaluated included Ownership (public or private) and Corporatization (publicly traded or not). In scientific databases, we did not find studies on the efficiency of specific WUs in collecting customer bills for services rendered and its relationships with this type of governance; thus, this would be the main innovative contribution of the study to the previous literature. In the first stage, the study utilized the Dynamic Slack Based Model (DSBM) to assess revenue collection efficiency. In the second stage, an econometric model with Generalized Estimating Equations (GEE) to explain the efficiency. The findings revealed a global average inefficiency of 50.47% in revenue collection. Corporatization was linked to higher collection efficiencies, while ownership type was significant, but with lower collection efficiency. Factors such as tariff accessibility, urbanization, and the COVID-19 pandemic also influenced efficiency. The study suggests regulatory bodies should consider these insights to implement policies that prevent inefficiencies from affecting the tariff system for services.

Botanical living collections within botanical gardens represent a crucial global asset for plant diversity. Particular emphasis should be given to dendrological collections, considering their key role in contribution to biodiversity conservation, support scientific inquiry, enhance educational initiatives and support on involvement of the public. The introduction of plants, especially woody plants, is one of the important problems in botanical science, solved in botanical gardens and arboretums. Establishment and development of the Dendrological collections in Botanical gardens is a way to have a comprehensive representation of diverse plant species from different biogeographical regions and continents. Current paper first time presents establishment, development, current status and future perspective of dendrological collections of Yerevan Botanical Garden in the linkage to introduction of woody plants in Armenia.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

Cigarette butts (CBs) are the most diffuse waste in the world, often abandoned in the environment without proper disposal. They are dangerous because of the numerous harmful chemicals poten-tially released in the environment. There are, in literature, several technological options for CB recycling, but some critical concerns could affect their effectiveness due to the quality and quantity of CB litter collected in the proper way. The present paper focuses on policy framework social behavior, waste collection and transport and technological processes. The Extended Producer Responsibility scheme for CBs is proposed at European level as an action to tackle CB litter and encourage sustainable product development. The CB waste collection and transport is a key step for bringing CB to the recycling process. The main concern is the small quantity of CBs collected: 0.06 % of the municipal waste and 0.18 % of the unsorted waste in the administrative area of Pe-rugia. Another crucial issue is the need for behavioral interventions to increase education and awareness of smoker citizens, addressing the discrepancy between smokers’ behaviors and beliefs.

The nationwide lockdown imposed to control the spread of novel coronavirus induced dramatic alterations in different sectors of the Nepalese governance, including Solid Waste Management (SWM) practices. The study identifies SW collection gaps in seven major cities of Nepal and highlights the municipal and public households on SW management practices before and during the lockdown to emphasize the linkage between COVID-19 and SWM. It includes information on solid waste status, collection frequency and coverage, workers safety practices, types of vehicles operated for collection and alternative methods adopted by households to manage SW during the lockdown. For this, 1329 households survey and key informant interviews were conducted in seven cities of Nepal during the lockdown. It was found that although the coverage of the collection service was similar during the pandemic, there was a drastic decrease in the collection frequency leading to a collection gap of around 570 tons/day. More than 50% of the surveyed households adopted no proper alternative measures as they claimed that they stored solid waste with proper management so that municipal authorities can take it. The study reveals poor occupational health and safety practices among the solid waste workers due to the unavailability of safety gears and equipment despite being aware of the modes of transmission of the virus. The pandemic exacerbated the challenges of smooth SWM as it is an essential and needy service. This study highlights the need for a timely strategic management framework to be developed by the government to continue the smooth SWM practices during the lockdown.

This study explores the enhancement of deductive reasoning capabilities in Large Language Models (LLMs) through a strategic dual-agent framework. In this framework, one agent acts as a questioner and another as an answerer, with both employing advanced linguistic and logical processing to optimize information exchange. Utilizing a structured environment that limits query opportunities, our approach emphasizes the development of LLMs that can efficiently generate and interpret questions to deduce hidden information effectively. The models, which incorporate self-defined agents with a combination of pretraining and llama-3-8b enhancements, demonstrate a remarkable ability to navigate the complexities of logical deduction. Performance evaluations, based on a series of simulated interactions, illustrate the agents' improved precision and strategic acumen in narrowing down possibilities through targeted inquiries. These findings underscore the potential of LLMs in tasks requiring intricate reasoning and collaboration, marking a significant step towards more intelligent and autonomous systems.

The flourishing landscape of agritourism in Albania has witnessed a steady increase in destinations over the years. Recognizing their pivotal role in driving economic development in rural areas, considerable attention is directed towards the promotion and expansion of these agricultural tourism spots. To enhance interactivity with visitors, these agritourism destinations actively leverage social networks and various digital platforms. However, the wealth of information pertaining to these destinations is currently scattered across different platforms, posing a challenge to accessibility. To address this issue, these points are strategically placed on Google Maps, along with essential details collected from users, such as websites, contact numbers, visitor ratings, and comments. Nevertheless, other valuable data remains dispersed on various social networks. In response to this fragmentation, this study proposes the development of algorithms and scripts to systematically collect and analyze this dispersed information. By doing so, the goal is to centralize and streamline the data, enabling a comprehensive understanding of the agritourism landscape in Albania. This approach not only facilitates efficient access to information for potential visitors but also provides valuable insights for stakeholders involved in the development and management of these agritourism destinations. The study aims to contribute to the sustainable growth of agritourism in Albania by harnessing the power of digital platforms to create a unified and organized database. Through this integration, the research seeks to unveil patterns, trends, and opportunities that can further guide policy-making, marketing strategies, and overall decision-making processes in the realm of agritourism development.

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

Collecting relevant data is an important aspect in allowing evaluation of the performance of systems, methods, algorithms, and techniques developed in the research process. Researchers at the South African Scientific and Industrial Research Council perfoemd a study to identify biometric(s) that can be used to identify and verify young children from birth to 16 years old. Three types of biometrics were considered in this study as a resukt of a comprehensive preliminary research, namely, fingerprins, iris and ear shap. This paper presents challenges identified and lessons learned during the in trying to collect data for scientific research that require interaction with children of different ages and their parents in different environments. . The study is primarily based on researcher’s experiences. Some of the discussion points discussed are of global relevance to children and general human beings, while others relate to South Africa in particular. The discussion aims to share knowledge and experience, hoping that this will help researchers prepare for a trouble-free child data collection process in the future. Keywords: Baby biometrics; long-term data collection, baby fingerprints, baby iris, baby ears

This work is the first scientific study on the development of the composition and technology of a new antioxidant agent based on a combination of extracts of tree medicinal plants and the study of the technological parameters of a dry extract. Further draw up technological regulations for their production and their introduction into production. This study aims to the antioxidant activity of dry extract "Antioxim" was determined. In order to create this herbal collection of plant, first of all, to study the technological parameters of raw materials, such as mass size, porosity, dispersibility, free volume of layers of raw materials, water absorption coefficient of extractives, and technological process efficiency to study the effect of parameters, developed at the Department of "Industrial Technology of Medicines" of the Tashkent Pharmaceutical Institute:

Unlike their marine counterparts, tropical freshwater clupeids receive little scientific attention. However, they sustain important fisheries that may be of (inter)national commercial interest. Africa harbours over 20 freshwater clupeid species within Pellonulini. Recent research suggests their most abundant parasites are gill-infecting monogenean flatworms within Kapentagyrus. After inspecting specimens of 12 freshwater clupeids from West and Central Africa, mainly sourced in biodiversity collections, we propose 11 new species of Kapentagyrus which we describe using their haptoral and genital morphology. Because of their high morphological similarity, species delineation relies mostly on morphometrics of anchors and hooks. Specifically, earlier, molecular taxonomic work indicated that the proportion between the length of the anchor roots, and between hook and anchor length, are diagnostic. On average, about one species of Kapentagyrus exists per pellonuline species, although Pellonula leonensis harbours four species and Microthrissa congica two, while Microthrissa moeruensis and Potamothrissa acutirostris share a gill monogenean species. This study more than quadruples the number of known species of Kapentagyrus, also almost quadrupling the number of pellonuline species of which monogeneans are known. Since members of Kapentagyrus are informative about their hosts’ ecology, evolutionary history, and introduction routes, this enables a parasitological perspective on several data-poor African fisheries.

Human infection caused by the SARS-CoV-2 virus, called COVID-19, is a new pandemic with devastating effects worldwide. Science seeks the rational and systematic explanation of phenomena. In pandemics, decisions on prevention and treatment of people should be consistently taken, supported by scientific knowledge and ethical principles to produce more good than harm. At first, prospective observational studies to systematically collect patient data, correlating protective or therapeutic interventions with outcomes to assess effectiveness and safety, should be prioritized as the most appropriate type of study. The proposed protocol in this article aims to provide doctors with information on the reduction of harm in early COVID-19 patients by applying individualized interventionist or expectant therapeutic strategies, respecting the autonomy and preferences of physicians and patients in clinical decision-making. The evaluation of the clinical status, besides laboratory confirmation of COVID-19, comprises an individualized symptom score for each patient, a global self-perception scale of the severity of the disease, a clinical progression scale developed by the WHO for clinical studies in COVID-19 and, at the first consultation, doctors´ overall impression on the clinical prognosis. The analysis of anonymized data should preferably use descriptive and inferential statistical resources. The case report form is available for free use in the protocol, along with examples of patient informed consent forms for the prescription of off-label medications and authorization to use the data. Their results may be useful to indicate interventions that are candidates for efficacy trials, in randomized controlled trials, with a higher chance of success. It respects the autonomy and preferences of doctors and patients to decide the best options for treatment in uncertain situations. It also allows the gathering of useful information for future more rigorous clinical trials, trying to link science, ethics, and personal clinical experience.

There is significant need for development of a new environmentally friendly, extraction-free sample collection medium that can effectively preserve and protect genetic material for point-of-care, and/or self-collection, home-collection, and mail-back testing. Systematic Evolution of Ligands by Exponential Enrichment (SELEX) was used to create anti-ribonuclease (RNase) Deoxyribonucleic acid (DNA) aptamers against purified RNase A conjugated to paramagnetic carboxylated beads. Following 8 rounds of SELEX carried out under various stringency conditions, e.g., selection using Xtract-Free™ (XF) specimen collection medium, and elevated ambient temperature of 28 ºC, a panel of 5 aptamers was chosen following bioinformatic analysis using next-generation sequencing. The efficacy of aptamer inactivation of RNase was assessed by monitoring Ribonucleic acid (RNA) integrity by fluorometric and real-time RT-PCR analysis. Inclusion of aptamers in reaction incubations resulted in an 8,800 to 11,200-fold reduction of RNase activity, i.e., digestion of viral RNA compared to control. Thus, anti-RNase aptamers integrated into XF collection medium as well as other commercial reagents and kits have great potential for ensuring quality intact RNA for subsequent genomic analysis.

Standardised forms for capturing clinical data promote consistency in data collection and analysis across study sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1,886 hospitals and 950,064 patients were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs & symptoms (68.9% and 78.4%) and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results indicate that clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

Aiming at the problem of low pollutant concentration in the sewage treatment plant due to external water intrusion into the sewage collection system, which in turn leads to low pollutant reduction efficiency. A sewage system in Zhenjiang City is taken as an example. Analyze the situation of external water intrusion in the sewage pipe network, and determine the external water intrusion proportion based on the water quality and quantity method. First, the dry season flow rate of the sewage pipe is obtained according to the monitoring data of the flowmeter. Then, the key research areas are screened out based on the changes in the concentration of water quality characteristic factors. Furthermore, chemical oxygen demand and electrical conductivity are used as the water quality characteristic indicators to characterize shallow groundwater and river water. In addition, the proportions of groundwater and river water intrusion in the sewage pipe network are quantitatively analyzed based on the chemical mass balance equation. At the same time, the dredging detection method is used to assist in the investigation, and finally the engineering rectification of the problems found in the drainage is carried out. The results show that the water quality and quantity method can effectively identify the types of external water and analyze the proportion of external water intrusion, which is of exemplary significance for the evaluation of sewage collection systems.

The use of plastic in agriculture has become so omnipresent within the industry that it has acquired it own lexicon to become known as ‘plasticulture’. However, since, compared to other industries the quantities consumed are low, little attention is granted to this waste stream. This is the case both with local and international policies even though it bears a close connection to soil health and therefore is a prerequisite to improve the sustainability of food production processes including food security. Using Malta as a case study, the research utilises quantitative and qualitative tools to identify and quantify the most common types of agricultural plastic waste generated and determines the attitudes of farmers towards a collection and recycling scheme. While it identifies that incineration and ploughing of plastic waste as regular practices, it also ascertains the willingness of farmers to acquire more knowledge about more appropriate methods to dispose of this waste stream thus guiding policy makers towards the need to initiate educational measures for farmers to prevent these harmful practices. Through stakeholder interviews the research highlights that the present lack of data is a major hindrance for policy to take account of this waste stream.

Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR and radar, systems bring up the requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as cameras, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors , namely, camera, LiDAR and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications, also suitable for quick and large-scale practical deployment.

This paper analyzes the collection of George Viau (1855-1939) from a computational perspective. Indeed, this dental surgeon, close to the Impressionists, collected several hundred works of art in the first half of the 20th century: the auctions of his collection, in 1907, 1930 and after his death, in 1942, 1943 and 1948, accounted for 642 artworks and, during the Occupation, the 1942 sale produced a total of more than 46 million francs. Thanks to statistics, econometrics, network analysis and cartography, it is possible to understand the salient features of the collection and its place in the world of Parisian auctions.

The European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) was established after a successful grant application to the European Cooperation is Science and Technology (COST). This network aimed to assess the existing knowledge and/or experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in the European countries and worldwide, and to enhance coordinated research and health care provision in this field. The EUROMENE proposal, was based on the establishment of interrelated working groups (WGs), where the participants contributed with specific knowledge and viewpoints according to their specialties and/or areas of interest. In this paper we outline the work of a multidisciplinary team of researchers, including epidemiologists, clinicians, statisticians, biomedical scientist and heath economists, who set out their recommendations to guide data acquisition for ME/CFS research, aiming to standardise data collection and improve epidemiological research.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball distribution pattern, its density, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 1 inch to 4 inches in diameter, and the maximum clay ball depth is almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls.

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Ovariectomy appears to be the best method to control the density of wild ungulate populations. The aim of the present study was to compare two surgical approaches, via the flank and midline, in mouflon’s ovariectomy in field conditions. Twenty female mouflons were enrolled in the study. The animals were divided randomly in two groups, Group F animals were gonadectomized via flank approach, while Group A animals were sterilized via linea alba access. The parameters evaluated were duration of surgery, duration of anaesthesia, recovery time, intra- and post-operative complications, intra-operative nociception and pain in the post-operative period. There were no intra- and post-operative complications. The evaluated parameters showed a very similar trend in both groups. Both techniques are found to be effective and safe in execution.

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

Grape production worldwide is increasingly threatened by grapevine trunk diseases (GTDs). No grapevine cultivar is known to be entirely resistant to GTDs, but susceptibility varies greatly. To quantify these differences, four Hungarian grape germplasm collections containing 305 different cultivars were surveyed to determine the ratios of GTDs based on symptom expression and mortality rate. The cultivars of monophyletic Vitis vinifera L. origin were amongst the most sensitive ones, and their sensitivity was significantly (p<0.01) higher than that of the interspecific (hybrid) cultivars assessed, which are defined by the presence of Vitis species other than V. vinifera (e.g. V. labrusca L., V. rupestris Scheele, V. amurensis Rupr.) in their pedigree. We conclude that ancestral diversity of the grape confers higher degrees of resilience against GTDs.

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.

The Amazonian shrub Myrciaria dubia (Kunth) McVaugh, known as "camu-camu", produces vitamin C-rich fruits of growing commercial interest. However, sustainable utilization requires assessment and protection of the genetic diversity of the available germplasm. We hypothesized that the ex situ M. dubia germplasm bank assembled from eight river basins across the Peruvian Amazon would harbor substantial genetic diversity and have a genetic population structure. This study aimed to (1) develop new polymorphic microsatellite markers, (2) characterize genetic diversity and validate the hypothesis in the ex situ germplasm, and (3) construct a core subset representing maximum allelic variability. Sixteen polymorphic microsatellite loci were developed using an enrichment approach. The evaluation of 336 genotypes from 43 accessions originating from eight river basins of the germplasm bank corroborated this hypothesis, revealing high gene diversity, with observed heterozygosity ranging from 0.468 to 0.644 and expected heterozygosity from 0.684 to 0.817 at the river basin level. Analysis of molecular variance showed a higher genetic variation within accessions and river basins, at 73% and 86%, respectively, than among accessions and river basins, at 27% and 14 %, respectively. Bayesian clustering detected the presence of ten genetic clusters, with several degrees of admixture among river basins. The Putumayo River basin showed a clear genetic homogeneity. A core collection of 84 genotypes was constructed, thus covering 86.7% of the global allelic diversity. These results have important implications for M. dubia conservation strategies and breeding programs, in demonstrating a need for genetic connectivity between populations but preserving unique genetic resources in isolated basins. These results validate the expected levels of diversity and population subdivision in a crop and stress the need to secure genetically diverse germplasms, underscoring the importance of thorough genetic characterization for ex situ germplasm management.

The Interdisciplinary Canadian Collaborative ME Research Network (ICanCME), established in 2019, aims to foster research in Canada and contribute to finding the causes and possible treatments for myalgic encephalomyelitis /chronic fatigue syndrome (ME/CFS), thereby reducing the impact of ME/CFS on the health of Canadians. The main objectives of this paper are to suggest standards for ME/CFS research for the collection of data from participants (Recommended Data Elements) and to consider other factors, such as design, language, cultural issues, equity, and diversity. Consensus of the relevant contents of this research guideline was reached during the ICanCME working group meetings and were based on existing guidelines. Members of the working group contributed to guideline development based on their respective expertise. The proposed research guidelines could improve research quality and advance knowledge in the field of ME/CFS, and ultimately benefit ME/CFS patients.

Weather has significant impact on growth and plants development. It is important to analyze the influence of changing climate conditions on expression of plants agronomical characters. Two flax varieties were grown from 1987 to 2018 in the North-West of Russia. Whether conditions and their influence on flax agronomic characters were analyzed using variance and correlations analyses. Significant influence of year conditions on the manifestation of all evaluated characters was revealed. Starting from June high temperatures accelerate the development of plants at all stages. Prolongation of germination - flowering period is the most important for fiber productivity improvement. Oppositely, for formation of high quality fiber fast ripening in hot weather after flowering is preferable. Such data give possibility to predict the amount and quality of yield. Usage of weather conditions data also permits to compare results obtained in different years. Suggested method of year’s meteorological conditions classification can be used in other genebanks for systematization and analyses of crops field evaluation results. Correlation analyses reviled 3 correlated pleiades: (1) pleiade of productivity, (2) pleiade of fiber quality and yield, (3) pleiade of the growing season phases duration, the sums of active temperatures and precipitation during each period. Great influence of growing conditions on economically valuable traits indicates the necessity of searching for genotypes with stable characters manifestation for breeding new varieties, which will give stable yield and fiber of good quality.

A macro network loading Model for multi-flow lines, time-varying and pedestrian congestion is proposed. The station hub is abstracted as a network of different types of nodes, and the flow of passengers at each node is calculated in real time for the purpose of simulating the hub's collection and distribution process. For correct transmission of passenger flow on heterogeneous networks, three types of indexes are proposed to distinguish the nodes, and then match the corresponding fundamental diagrams. This paper divides the update process of the dynamic network loading model into multiple processes by flow lines, and improves computational speed of DNL model. The proposed model is applied to the simulation of passenger flow collection and distribution in an actual hub station with multi-flow lines. The analysis results illustrate that the model can accurately reflect the realistic congestion facilities and explain the formation process of high-density areas. A rolling passenger flow control model based on optimal control theory is proposed. The effectiveness of the control model is verified based on simulation data.

COVID-19 breakout calls for immediate research explorations. The objective of this study is to perform a bibliometric analysis of all COVID-19-related publications in Science Citation Index Expanded (SCI-EXPANDED) in the early stage of the outbreak. Analysis parameters include performances of authors, institutes, and countries as well as distributions of Web of Science categories, journals, languages, and types of publications. Results show that 32% of total papers were published as editorial materials and an overwhelming production from Chinese research institutes. An association of research indexes with the number of cases was also found.

Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still do not meet the benchmark time metrics. Additionally, smaller centers often face longer treatment times, yet studies focus primarily on larger, more advanced centers. Objectives: The aim of this study is to analyze existing literature to understand the strategies implemented in primary and comprehensive stroke centers to reduce their treatment times, and categorize the studies based on methods used to improve their processes Results: Three main categories of improvements were identified in the literature: 1) standardization of processes, 2) resource management, and 3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of processes. However, challenges such as variations in resources between hospitals and difficulties in integrating data collection software into workflow were highlighted. Additionally, many strategies to optimize resource management and data collection were conducted in comprehensive stroke centers, which can benefit primary stroke centers. Conclusions: Many existing strategies to improve treatment times are viable for both primary and comprehensive stroke centers. However, while data collection is recognized as crucial for process improvement, challenges persist in integrating data collection methods into clinical workflow. Proposed solutions include the development of easy-to-use software tailored to clinician needs.

The need for the most accurate and flexible system of revenue collection from internal sources has become a matter of extreme urgency and importance in e-governance. This need underscores the eagerness on the part of the Government to look for a new principle and policy of revenue collection or to become aggressive and innovative in the mode of collecting revenue from existing sources using the present system. The Boards of some Governments in Africa, even up to the moment are facing a lot of setbacks in performing their tasks due to the manual system of revenue collection from the public. This can be improved through an effective collection of revenue using the most accurate and flexible system. Tax is usually collected in the form of specific sales tax, general sales tax, corporate income tax, individual income tax, property tax and inheritance tax. Problems such as high cost of collection, fraud, underpayment, leakage in revenue, poor access to information, poor tracking of defaulters is at the increase. As a result of this, there is need to computerize the revenue collection system. Computerized systems have proven to introduce massive efficiencies and quick collection of revenue from the public. This research work demonstrates how to design and implement an automated system of revenue collection and how to maintain a secured database for collected tax information. This research delves into the study of how machine learning algorithms and Software-defined Networks improve the security of such automated systems.

The rapid development of new retail and the impact of COVID-19 have catalyzed the blowout growth of community group purchasing. The emergence of community group purchasing collection and delivery points (CGPCDPs) has become a new way to solve the "last mile" problem of new retail delivery. Based on the point of interest (POI) data of CGPCDPs of Nansha District, Guangzhou City, this study uses text analysis, spatial analysis, model analysis, and other methods to analyze the operation mode, spatial distribution, and influencing factors of CGPCDPs. The conclusions are as follows: CGPCDPs initiators are mainly shopkeepers. They depend mainly on wholesale and retail shops. Service targets are mainly urban and rural communities, followed by industrial areas. The distribution of CGPCDPs has apparent spatial differentiation. At the macro scale, it shows the characteristics of "central agglomeration and peripheral dispersion". It is distributed along the "northwest-southeast" direction and presents a "dual-core multi-center" pattern. At meso-micro scale, different built environments in developed areas of cities, villages in the city (ChengZhongCun) and rural areas show distinct distribution patterns. The MGWR regression model has a better fitting effect than OLS and GWR. The main influencing factors are population density, construction land, house price, supporting place, residence density, urban community, and road proximity.

Endophytic fungi colonize the inner tissues of plants and provide direct and indirect benefits to the plant. Although Mongolia is rich in medicinal plants, due to climatic and anthropogenic reasons the resources are being depleted and many species are under the threat of gradual extinction, while the endophytic fungi of Mongolian plants are largely unknown. Understanding the diversity and metabolic potential of endophytic fungi is of great significance in terms of the utilization of beneficial fungal strains in plant propagation and the discovery of bioactive compounds. In this study, a total of 24 culturable endophytic fungal strains were isolated from Cynomorium songaricum (Rupr.), a medicinal and vulnerable plant species of Mongolia. Based on the morphological characteristics and the sequences of the rDNA internal transcribed spacer (ITS) region, the isolates were identified to 6 genera: Fusarium (8), Clonostachys (7), Penicillium (6), Alternaria (1), Aspergillus (1), and Madurella (1). The antimicrobial activity was assessed by agar-diffusion method, revealing that 15 strains were able to inhibit the growth of at least one of the test organisms. Among them, 1 strain showed inhibitory activity against Escherichia coli, 12 against Bacillus subtilis, 13 against Staphylococcus aureus, 8 against Aspergillus niger, respectively. All fungal strains were screened for their ability to solubilize complex phosphorus and zinc minerals and for their production of indole-3-acetic acid (IAA). The strains were preserved in the national culture collection for further sustainable exploitation of their biotechnological potential.

Significant amounts of anthropogenic radionuclides were introduced in ocean waters following nuclear atmospheric tests and development of the nuclear industry. Dispersion of artificial dissolved radionuclides has been extensively measured for decades over the European continental shelf. The radionuclide measurement and release fluxes databases provided here represent an exceptional opportunity to validate dispersion hydrodynamic models. MARS hydrodynamic model have been applied at different scales to reproduce in realistic conditions the measured dispersion. Specific methods have been developed to obtain qualitative and quantitative results and perform model/measurement comparisons. Model validation concerns short to large scales with dedicated surveys following the dispersion: it was performed within two and three dimensions framework and from minutes and hours following a release up to several years. Results are presented concerning the dispersion of radionuclides in marine systems deduced from standalone measurements, or according to model comparisons. It allows characterising dispersion over the continental shelf, pathways, transit times, budgets and source terms. This review exhibits the main features retained from the point of view of radiotracers, hydrodynamic models and model/measurement methods with perspectives of applications in other areas or oceanographic domains.

Ecosystem surveillance monitoring is critical to managing natural resources and especially so under changing environments. Despite this importance, the design and implementation of monitoring programs across large temporal and spatial scales has been hampered by the lack of appropriately standardised methods and data streams. To address this gap, we outline a surveillance monitoring method based on permanent plots and voucher samples suited to rangeland environments around the world that is repeatable, cost-effective, appropriate for large-scale comparisons and adaptable to other global biomes. The method provides comprehensive data on vegetation composition and structure along with soil attributes relevant to plant growth, delivered as a combination of modules that can be targeted for different purposes or available resources. Plots are located in a stratified design across vegetation units, landforms and climates to enhance continental and global comparisons. Changes are investigated through revisits. Vegetation is measured to inform on composition, cover and structure. Samples of vegetation and soils are collected and tracked by barcode labels and stored long-term for subsequent analysis. Technology is used to enhance the accuracy of field methods, including differential GPS r plot locations, instrument based Leaf Area Index (LAI) measures, and three dimensional photo-panoramas for advanced analysis. A key feature of the method is the use of electronic field data collection to enhance data delivery into a publicly-accessible database.Our method is pragmatic, whilst still providing consistent data, information and samples on key vegetation and soil attributes. The method is operational and has been applied at more than 704 field locations across the Australian rangelands as part of the Ecosystem Surveillance program of the Terrestrial Ecosystem Research Network (TERN). The methodology enables continental analyses, and has been tested in communities broadly representative of rangelands globally, with components being applicable to other biomes. Here we also recommend the consultative process and guiding principles that drove the development of this method as an approach for development of the method into other biomes. The consistent, standardised and objective method enables continental, and potentially global analyses than were not previously possible with disparate programs and datasets.

Biocultural heritage preservation relies on ethnobotanical knowledge and the paleoethnobotanical data used in (re)constructing histories of human-biota interactions. Biocultural heritage, defined as the knowledge and practices of Indigenous and Local peoples and their biological relatives, is often guarded information, meant for specific audiences and withheld from other social circles. As such, these forms of heritage and knowledge must also be included in the ongoing data sovereignty discussions and movement. In this paper we share the process and design decisions behind creating an online database for ethnobotanical knowledge and associated paleoethnobotanical data, using a content management system designed to foreground Indigenous and local perspectives. Our main purpose is to suggest the Mukurtu content management system, originally designed for physical items of cultural importance, be considered as a potential tool for digitizing and ethically circulating biocultural heritage, including paleoethnobotanical resources. With this database, we aim to create access to biocultural heritage and paleoethnobotanical considerations for a variety of audiences while also respecting the protected and sensitive natures of Indigenous and local knowledges.

Vehicle routing is a critical factor in municipal solid waste (MSW) collection planning and operations. Poor routing can introduce inefficiencies and cause targeted levels of services or performance to be missed irrespective of the level of resource application. Trial and error approaches have been proven to be not the best in the planning and prediction of expected performance. This study explores various Geographic Information System (GIS) tools and analysis techniques, and how they can be applied to optimizing vehicle routes in light of challenging site conditions. Using Adentan West residential area, suburb of Accra Ghana as a case study, current performance of the trial and error method was measured and a GIS computer model was used to evaluate various optimization scenarios to determine the level of savings that can be made. Field measurements were taking with Global Positioning System (GPS) devices for waste collection activities in areas with varying characteristics and conditions, and data analysed for one selected vehicle operating four days per week. It was found that, for a scenario where only the bin collection order was optimized while route selection was restricted by the ArcGIS Network Analyst, 2.6% of travel distance and 2.21% of travel time were saved. For the second scenario where only the route selection was optimized while order of bin collection was restricted, 4.1% and 1.5% of travel distance and time respectively were saved. For a third scenario where both the order of collection and route selection were together optimized, 10.9% and 3.7% of travel distance and time respectively were saved. Lastly, by regrouping all the bins for daily collection, 4.5% and 1.2% of travel distance and time respectively were saved. The results demonstrated that there is always room for optimization of solid waste collection routing irrespective of site constraints and other challenges that the nature of bin distribution pose to drivers. In developing countries like Ghana, where there is high demand for services in the face of limited road network access, application of GIS in optimization of routes will guide providers in planning and subsequently make more savings in fuel consumption, vehicle maintenance and cost of man-hours.

Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.

In developing nations, such as Ekiti State, Nigeria, the use of remote sensing data, especially satellite and UAV imagery, remains significantly underutilized in land administration. This limits the potential of multi-resolution imagery in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping six distinct rural, peri-urban, and urban sites, and creating orthomosaics with resolutions ranging from 2.2cm to 3.39cm. Boundaries were manually delineated from multi-resolution orthomosaics to acquire coordinates, and parcel areas were calculated. Using 0.05m UAV imagery as the reference, the Root Mean Square Error (RMSE) was computed to evaluate 0.1, 0.5, and 1m orthomosaics' accuracy for boundary delineation across the six geographical contexts. The Normalized Parcel Area Error (NPAE) was also calculated to determine how the multi-resolution imageries affected the property areas. Results showed a consistent increase in error with increasing resolution (0.1m to 1m) in formal areas (Igedora, Maryland, Okebola) with physical demarcations, from 0.117m to 1.747m RMSE. In contrast, informal settlements exhibited inconsistent error values, indicating a differing impact of resolution on error between formal and informal areas. The research demonstrates the role of physical demarcations in facilitating cadastral mapping, with formal settlements demonstrating the highest suitability. This study recommends the use of imagery resolution specific to each context, offering guidance for improved land governance. Implications and recommendations include promoting awareness of settlement typologies in aerial imagery use for cadastral mapping, leveraging UAVs for high-resolution aerial imagery, and addressing regulatory challenges to promote UAV adoption. Finally, the RMSE and NPAE values can serve as metrics for assessing different resolution images’ fitness for cadastral mapping.

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amounts of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recovery at the initial stage of recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model confirms also the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess.

Municipal Solid Waste Management (MSWM) struggles with significant policy and operational challenges, particularly concerning recyclables collection routes and fleet composition. Within the European Union, phasing out traditional fuel-based vocational vehicles, like garbage trucks, in favor of zero-emission alternatives is mandatory to achieve sustainable development objectives. Integrating electric solid waste collection vehicles into municipal fleets presents challenges due to differences in range, capacity, and noise levels. The emission is even more crucial in the systems where recyclables are collected separately, which requires a greater number of trips to be performed. This study addresses MSWM’s strategic aspect by minimizing the total cost of replacing waste collection vehicles while meeting emission constraints. The Fleet Transition Problem (FTP) was formulated and solved using a MARKAL-based approach. The objective of the FTP and a mixed integer linear program used to solve it is minimizing the overall cost of fleet modernization throughout a multi-phase planning horizon so that the sustainable transition of the fleet can be assured. Computational experiments affirmed the model’s effectiveness in strategizing fleet transition. This research outlines a multi-period model for transitioning to a zero-emission fleet and demonstrates the FTP’s potential for strategic decision-making. Notably, the study observes a consistent reduction in permissible emissions across the planning horizon.

The use of Geographical Information Systems (GIS) for biodiversity monitoring and conservation, shortened to Conservation GIS, is an influential tool that has revolutionized conservation efforts by providing spatially explicit data to inform conservation decision-making. However, Conservation GIS also faces challenges related to data quality and availability, technical limitations, as well as policy and governance issues. The diverse and rising uses of GIS for conservation has resulted in a need for continued research and development of Conservation GIS, including advancements in technology and data collection, integration with other fields such as machine learning and artificial intelligence, and collaborative approaches to Conservation GIS. Conservation GIS has the potential to make a significant impact on conservation efforts, and this article emphasizes the importance of its implementation to achieve the broader goal of biodiversity conservation for planetary health. In this review, we discuss the various applications of Conservation GIS, including biodiversity conservation and monitoring, habitat mapping and restoration, climate change mitigation and adaptation, wildlife tracking and management, and protected area management. We highlight some of the key knowledge gaps or research questions that need to be addressed in the future to further advance the field of Conservation GIS. By addressing these challenges and knowledge gaps, Conservation GIS can become a powerful tool for addressing the challenges facing biodiversity and ecosystems worldwide, and for promoting sustainable land-use practices.

Cyber diplomacy is critical in dealing with evolving cybersecurity dangers and possibilities in the digital era. This article investigates the impact of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchains, and Quantum Computing on cyber diplomacy. AI holds the potential for proactive threat identification and response, while IoT enables international information sharing. Blockchains enable secure data sharing and document verification, but they also pose new threats, such as AI-driven cyber-attacks, IoT privacy breaches, blockchain vulnerabilities, and the potential for quantum computing to break encryption. This article conducts case study reviews, in combination with secondary data analysis, and emphasises the value of international cooperation in developing global norms and frameworks to control responsible technology adoption. Cyber diplomacy can promote cybersecurity, protect national interests, and foster mutual trust among nations in the digital sphere by capitalising on possibilities and reducing threats.

Pasmo is one of the most widespread diseases threatening flax production. To identify genetic regions associated with pasmo resistance (PR), a genome-wide association study was performed on 370 accessions from the flax core collection. Evaluation of pasmo severity was performed in the field from 2012 to 2016 in Morden, MB, Canada. Genotyping-by-sequencing has identified 258,873 single nucleotide polymorphisms (SNPs) distributed on all 15 flax chromosomes. Marker-trait associations were identified using ten different statistical models. A total of 692 unique quantitative trait nucleotides (QTNs) associated with 500 putative quantitative trait loci (QTL) were detected from six phenotypic PR datasets (five individual years and average across years). Different QTNs were identified with various statistical models and from individual PR datasets, indicative of the complementation between analytical methods and/or genotype × environment interactions of the QTL effects. The single-locus models tended to identify large-effect QTNs while the multi-loci models were able to detect QTNs with smaller effects. Among the putative QTL, 67 had large effects (3-23%), were stable across all datasets and explained 32-64% of the total variation for PR in the various datasets. Forty-five of these QTL spanned 85 resistance gene analogs including a large toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (TNL) type gene cluster on chromosome 8. The number of positive effect QTL (NPQTL) in accessions was significantly correlated with PR (R²=0.55), suggesting additive effects. NPQTL was also significantly associated with morphotype (R²=0.52) and major positive effect QTL were present in the fiber type accessions. The 67 large effect QTL are suited for marker-assisted selection and the 500 QTL for effective genomic prediction in PR molecular breeding.

The development of sustainable tourism in the territory requires the monitoring of all diverse aspects of the tourism sector and the creation of information technologies that provide the collection, analysis, processing and visualization of relevant information. The aim of this work is to describe the concept of the tourism monitoring service in Irkutsk Oblast as part of the Lake Baikal area, define methods and software of implementing this service, and present individual results of its prototyping. The creation of a service includes the development and software implementation of methods for collecting, processing and visualizing data, as well as ensuring the use of the functionality of existing cloud platforms for the main information processes. Data collection methods are based on Application Programming Interface and web scraping technologies, processing methods include statistical methods and artificial intelligence methods, visualization methods are based on Business Intelligent systems methods. In this paper described the concept of service, justified methods of data analysis and visualization, built an ontological model, defined the content of dashboards, implemented a prototype of a service, and demonstrated the results of data collection, processing and presentation obtained from the research. Currently, information has been collected and processed for the following tourist attractions: 685 accommodation facilities, 16 services, 809 catering establishments, 352 attractions, 94 tourist routes and excursions. The proposed technology (methods and software) of the territory tourism monitoring service has demonstrated its effectiveness.

Seed longevity is a crucial trait for the seed industry and genetic resource preservation. To develop excellent cultivars with extended seed lifespans, it is important to understand the mechanism of keeping seed germinability long-term and to find useful genetic resources as prospective breeding materials. This study was conducted to identify the best cultivars with a high and stable seed longevity trait in the germplasm of rice (Oryza sativa L.) and to analyse the correlation between seed longevity and embryonic RNA integrity. Seeds from 69 cultivars of the world rice core collection selected by the NIAS in Japan were harvested in different years and subjected to long-term storage or controlled deterioration treatment (CDT). The long-term storage (4 °C, RH under 35%, 10 years) was performed on seeds harvested in 2010 and 2013. The seeds harvested in 2016 and 2019 were used for CDT (36 °C, RH of 80%, 40 days). Seed longevity and embryonic RNA integrity were estimated by a decrease in germination rate and RNA integrity number (RIN) after long-term storage, or CDT. The RIN value was obtained by electrophoresis of total RNA extracted from seed embryos. Seeds of ‘Vandaran (Indica)’, ‘Tupa 729 (Japonica)’, and ‘Badari Dhan (Indica)’ consistently showed higher seed longevity and embryonic RNA integrity both under long-term storage and CDT conditions, regardless of the harvest year. A strong correlation (R2 = 0.93) was observed between germination rates and RIN values of the seeds after long-term storage or CDT among nine cultivars selected based on differences in their seed longevity. The study findings revealed the relationship between rice seed longevity and embryo RNA stability and suggested potential breeding materials, including both Japonica and Indica cultivars, for improving rice seed longevity.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

Limiting harm to organisms via genetic sampling is an important consideration for rare species. Nondestructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the Unionid freshwater mussel, Texas Pigtoe (Fusconaia askewi). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared to swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence) while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.

: E-commerce sales surge represents a huge challenge for urban freight transport. Parcel lockers constitute a valid solution for addressing the challenges home deliveries imply. In fact, eliminating courier-consumer contact (also relevant for health-related issues, as made evident by COVID19 pandemic) and delivering in few predefined places might help coping with missed deliveries substantially. Furthermore, this option enables consolidated shipping and reducing delivery trip costs. This paper analyses and compares consumers’ preferences for alternative collection strategies. It investigates home delivery vs parcel locker use and forecasts their future market shares. This is performed based on both customers’ socio-economic variables and attributes characterising these alternative logistic fulfilment strategies. The case study considered rests upon a stated preference survey deployed in the city of Rome. The investigation specifically targets young people (i.e., population under 30 years) since they represent early adopters. Discrete choice models allow both quantifying the monetary value of parcel lockers attributes (i.e., willingness to pay measures) and estimating the potential demand for this innovative delivery scheme. Results show that distance and accessibility are the main choice determinants. Furthermore, there is an overall high propensity to adopt parcel lockers. This research can support policymakers when implementing such solutions.

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.