The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

Access to nasopharyngeal swabs for sampling remain a bottleneck in some regions for COVID-19 testing. This study develops a distributed manufacturing solution using only an open source manufacturing tool chain consisting of two types of open source 3-D printing and batch UV curing, and provides a parametric fully free design of a nasopharyngeal swab. The swab was designed using parametric OpenSCAD in two components (a head with engineered break point and various handles), which has several advantages: i) minimizing print time on relatively slow SLA printers, ii) enabling the use of smaller print volume open source SLA printers, iii) reducing the amount of relatively expensive UV resin, and iv) enabling production of handle on more accessible material extrusion 3-D printers. A modular open source UV LED box was designed, fabricated for $45 and tested for batch curing. Swabs can be fabricated for $0.06-$0.12/swab. The results of the mechanical validation tests showed that the swabs could withstand greater forces than would be expected in normal clinical use. The swabs were also able to absorb a significant amounts of synthetic mucus materials and passed abrasion and handling tests. The results show the open source swab are promising candidates for clinical trials.

Rehabilitation following anterior cruciate ligament reconstruction with hamstring graft allows the patient to regain his functional capacities and to support him in the resumption of sports activities. Rehabilitation also aims to minimize the risk of recurrence, which is why it ensures that the patient's muscular capacities develop properly until they return to sport. Isokinetics helps strengthen and assess the strength of muscle groups in the thigh, but controversy exists as to its use by resistance to the open kinetic chain knee extension that would cause the transplant to distend. The objective of this study is to determine the influence of isokinetic muscle strengthening on the possible laxity of the anterior cruciate ligament and to be able to determine risk factors. The study relates to a population having benefited from anterior cruciate ligament reconstruction with hamstring graft from 3 to 6 months after surgery. Two groups are differentiated, one group exposed to isokinetism during rehabilitation, the other group, named unexposed, undergoes rehabilitation without the use of isokinetism. An anterior knee laxity test is performed 6 months postoperatively using the GNRB® machine for all subjects according to the same protocol. The test results were statistically analyzed to determine a relative risk of transplant distension for each group in the study. Comparison of the results of each group by univariate analysis did not reveal any significant result. Multivariate analysis showed interactions in the two strata of the study. It was argued that the use of isokinetics seems to have no effect on the risk of developing distension for the majority of subjects in the exposed group. A tendency towards transplant protection was perceived for each variable except the age under 25 years (RRa = 1.07). The use of isokinetics does not appear to be a cause of transplant distension in patients undergoing an anterior cruciate ligament reconstruction when this method is introduced 3 months postoperatively.

Nervous systems and brains’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signaling originated in cells’ communication with the outside world and with each other, in order to cooperate toward adaptive construction and repair of multicellular bodies. Here we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering, cognitive science, and even machine learning and artificial intelligence. One of the predictions of this view is that regeneration and regulative development are able to restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states - in this case, pattern memories. Based on this idea, we propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution, and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology, and biomedicine of information processing in vivo.

Autonomous navigation of spacecraft necessitates innovative technologies, methods, and algorithms, particularly when orbiting in proximity of other space objects. Optimization methods are useful for autonomous spacecraft navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some proposed methods use noisy sensor data to learn the optimal guidance and control solutions real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aims to highlight efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimalization with singular switching plus nonlinear transport theorem decoupling proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. State tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (e.g., fuel) is reduced eighty four percent, while computational burden in reduced ten percent simultaneously.

This research is related to the eco-hydrological problems of herbaceous wetland drying and biodiversity loss in the floodplain lakes of the Middle Basin of the Biebrza river (Poland). An experiment was set up, whose main goals were: (i) mapping the vegetation types and the temporarily or permanently flooded areas, and (ii) comparing the usefulness of C-band Sentinel-1A (S1A) and X-band TerraSAR-X/TanDEM-X (TSX/TDX) for mapping purposes. The S1A imagery was acquired on a regular basis using the dual polarization VV/VH and the Interferometric Wide Swath Mode. The TSX/TDX data were acquired in quad-pol, a fully polarimetric mode, during the Science Phase. The paper addresses the following aspects: i) wetland mapping with S1A multi-temporal series; ii) wetland mapping with fully polarimetric TSX/TDX data; iii) comparing the wetland mapping using dual polarization TSX/TDX subsets, i.e. HH-HV, HH-VV and VV-VH; iv) comparing wetland mapping using S1A and TSX/TDX data based on the same polarization (VV-VH); v) studying the suitability of the Shannon Entropy for wetland mapping; and vi) assessing the contribution of interferometric coherence for wetland classification. The experimental results show main limitations of the S1A dataset, while they highlight the good accuracy that can be achieved using the TSX/TDX data, especially those taken in fully polarimetric mode.

Spirit of science is one of the important components of science education theory. It not only directly affects the level of science education, but also indirectly affects the selection, education and evaluation of scientific researchers, and the development of science and technology in a country or region. Although more than a century ago, the researchers began to discuss the topic of spirit of science. However, to date, the research of the definition, attribute, structural model and its characteristics of the spirit of science has not yet made a breakthrough. Based on the detailed literature review, the related theoretical analysis and the research of the structural model of the spirit of science, this paper puts forward the new definition, the attribute and the establishment of the structural model of the spirit of science, and analyzes the match the situation from new structural model of the spirit of science and the scientific nature published by the American Society for the Advancement of Science. The results of this study are of great significance for raising the level of scientific education and cultivating future scientific researchers, enhancing their motivation and skills in innovation in scientific research and promoting the development of future scientific undertakings.

Over the last twenty years, library publishing has emerged in higher education as a new class of publisher. Conceived as a response to commercial publishing practices that have strained library budgets and prevented scholars from openly licensing and sharing their works, library publishing is both a local service program and a broader movement to disrupt the current scholarly publishing arena. It is growing both in numbers of publishers and numbers of works produced. The commercial publishing framework which determines the viability of monetizing a product is not necessarily applicable for library publishers who exist as a common good to address the needs of their academic communities. Like any business venture, however, library publishers must develop a clear service model and business plan in order to create shared expectations for funding streams, quality markers, as well as technical and staff capacity. As the field is maturing from experimental projects to full programs, library publishers are formalizing their offerings and limitations. The anatomy of a library publishing business plan is presented and includes the principles of the program, scope of services, and staffing and governance requirements. Other aspects include production policies, financial structures, and measures of success.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

Since its inception in the late 2000s, blockchain has emerged as a powerful tool for creating trust without intermediaries to incentivize global communities for working for a common goal, such as the improvement of its very ecosystem, its applications and community adoption. While first blockchains were mainly devised for confirming transactions of their innate cryptocurrencies like Bitcoin, smart-contract blockchains like Ethereum can interface with the real-world through so-called “oracles”, which feed trustful off-chain information. This paper introduces digital twins of physical objects and processes as computational oracles to effectively unleash the tremendous opportunity offered by blockchain to the realm of fundamental science, research and technology development (RTD). The crowdsourcing concept is illustrated with the example of centrifugal flow control in microfluidic “Lab-on-a-Disc” (LoaD) systems.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Abstract: Mass spectrometry research laboratories reported multiple probes for ambient ionization in the last years. Combining them with a mechanical moving stage enables automated sampling and imaging applications. We developed a robotic platform, which is based on RepRap 3D-printer components, and therefore easy to reproduce and to adopt for custom prototypes. The minimal step width of the Open LabBot is 12.5 μm, and the sampling dimensions (x, y, z) are 18 × 15 × 20 cm. Adjustable rails in an aluminium frame construction facilitate the mounting of additional parts such as sensors, probes, or optical components. The Open LabBot uses industry-standard G-code for its control. The simple syntax facilitates the programming of the movement. We developed two programs: 1) LABI-Imaging, for direct control via a USB connection and the synchronization with MS data acquisition. 2) RmsiGUI, which integrates all steps of mass spectrometry imaging: The creation of G-code for robot control, the assembly of imzML files from raw data and the analysis of imzML files. We proved the functionality of the system by the automated sampling and classification of essential oils with a PlasmaChip probe. Further, we performed an ambient ionization mass spectrometry imaging (AIMSI) experiment of a lime slice with laser desorption low-temperature plasma (LD-LTP) ionization, demonstrating the integration of the complete workflow in RmsiGUI. The design of the Open LabBot and the software are released under open licenses to promote their use and adoption in the instrument developers’ community.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Modern medicine adopted four presumptions when it evolved from ancient experienced-based mind-body medicine. To understand its failure in finding cures for chronic diseases, we examined four presumptions, and found that statistical population of health properties does not exist for most research purposes, mathematical models are misused to model intensive properties, synthetic drugs are inherently more dangerous than nature-made medicines under their respective application conditions, and reductionist treatments are inferior and inherently dangerous. We found that clinical trials are valid only for research where treatment effect is much stronger than the total effects of all interfering or co-causal factors or errors introduced by misused mathematical models can be tolerated. In all other situations, clinical trials introduce excessive errors and fail to detect treatment effects, or produce biased, incorrect or wrong results. We further found that chronic diseases are manifestation of small departures in multiple process attributes in distinctive personal biological pathways networks, that modern medicine lacks required accuracy for accurately characterizing chronic diseases, and that reductionist treatments are good at controlling symptoms and safe for short term uses. For all stated reasons, as long as modern medicine continues relying on the flawed presumptions, it can never find predictable cures for chronic diseases. By implication, predictable cures to chronic diseases are adjustments to lifestyle, dietary, emotional, and environmental factors to slowly correct departures in process attributes responsible for chronic diseases.

Purpose: To compare the therapeutic efficacy and safety of dorzolamide/timolol fixed-combination in newly diagnosed primary open angle glaucoma patients. Methods: In this prospective, interventional case series, newly diagnosed primary open angle glaucoma (POAG) patients that had not been treated for glaucoma were included. Patients were started on Cosopt twice a day (BID) for 1 month and then switched to three times a day (TDS) for additional 1 month. Patients underwent comprehensive ophthalmic examination, diurnal intraocular pressure (IOP), blood pressure (BP) and 24-hours heart rate (HR) measurements at baseline, month 1( BID), and month 2( TDS). IOP, systolic and diastolic pressures were measured at 8:00 AM,12:00 AM, 4:00 PM, 8:00 PM and 12:00 PM. Throughout the study, all adverse events were recorded and monitored by the investigators. Results: In 31 POAG patients that completed the study ,mean baseline IOP was 23.1±3.15 mmHg . IOP was decreased significantly 16.5 ± 2.21 at 1 month (P < 0.0001) and 13.9 ± 2.23 mmHg at 1 and 2 month follow up. (P < 0.0001) IOP was significantly lower in month 2 compared to month 1 (P = 0.0004). While Cosopt BID significantly reduced the mean 24-hour systolic BP and mean 24-hour HR from baseline (P < 0.0001), the mean 24-hour systolic BP and HR remained unchanged 2ith Cosopt TDS compared to BID (P = 0.62). Conclusions: Cosopt TDS has a superior IOP-lowering effect than Cosopt BID in POAG patients with comparable safety profile.

Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scale up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom employing stakeholder interviews and user surveys, aiming at gaining insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities.

The commercial equipment that carries out the measurement of temperature has a high cost. Therefore, this article describes the development of a temperature measurement equipment, which uses a microcontrolled platform, responsible for managing the data of the collected temperature signals and making available the acquired information, so that they can be verified in real time at the measurement site, or remotely. The construction of the temperature measurement equipment was performed using open platform hardware / software, where performance tests were carried out with the objective of developing a temperature measurement equipment that has measurement quality and low cost.

To evaluate the 2-year efficacy and safety of ab interno trabeculectomy with the Trabectome in Chinese primary open-angle glaucoma (POAG) patients. This was a multicenter, retrospective study and included POAG patients with or without visually-significant cataracts. The primary outcome was intraocular pressure (IOP) reduction. Secondary outcomes included reduction of glaucoma medications, surgical complications, and success at 2-years. Success was defined as: 1) IOP < 21 mmHg and at least 20% IOP reduction from baseline after 3 months at any two consecutive visits, and 2) no additional glaucoma surgery required. A total of 42 patients were enrolled; thirteen had a history of failed glaucoma surgery. Twelve patients underwent Trabectome surgery combined with phacoemulsification while the remainder underwent Trabectome surgery alone. Overall, the mean preoperative IOP was 21.4±1.23 mmHg. The Trabectome lowered IOP to 18.5± 0.94 mmHg at 2 years (P<0.05). The number of glaucoma medications also decreased from a baseline of 2.0±0.9 to 1.2± 0.21 at 2 years (P=0.04). The overall success was 78%, with patients undergoing combined surgery having a higher success than those with Trabectome surgery alone (100% vs. 76%). No major complications were seen. The Trabectome appears to be an efficient and safe procedure in Chinese POAG patients in the long-term.

We discuss the fate of the correspondence principle beyond quantum mechanics, specifically in quantum field theory and quantum gravity, in connection with the intrinsic limitations of the human ability to observe the external world. We conclude that the best correspondence principle is made of unitarity, locality, proper renormalizability (a refinement of strict renormalizability), combined with fundamental local symmetries and the requirement of having a finite number of fields. Quantum gravity is identified in an essentially unique way. The gauge interactions are uniquely identified in form. Instead, the matter sector remains basically unrestricted. The major prediction is the violation of causality at small distances.

Mountain and pastoralist societies around the world have for centuries sustained their livelihoods and cultures by accumulating specialist knowledge about their local and regional socio-ecological environments. Developing traditional knowledge and customary practices takes time, sometimes spanning across generations. As macro-level changes to social and natural environment are now taking place, such as globalization and climate change, local communities could potentially also benefit from complementary, suitably adapted educational opportunities for sustainable development. However, access to education has often required moving to urban centres, which can weaken community structures and cohesion, and could also foster increased dependence on external specialists, providers or decision-makers. Careful introduction of emerging Educational Technologies could alleviate and possibly reverse such trends as mobile Internet access spreads to remote areas. This paper examines the role of education in sustainable development and specifically explores the potential for two educational innovations, open badges and blockchain, to provide a new construct for transformation in sustainable development amongst mountain and pastoralist societies. These technologies could not only facilitate education through online distance learning, but also allow geographically remote populations to highlight the value of their traditional knowledge and to engage more comprehensively in their changing worlds.

Ash trees have considerable economic, cultural and environmental value on the island of Ireland. However, European ash (Fraxinus excelsior L.) is currently under threat from the invasive ascomycete pathogen Hymenoscyphus fraxineus. This pathogen is the causal agent of ash dieback disease, which was initially reported in Poland in 1992. Hymenoscyphus fraxineus has since spread across Europe and the first recorded case of the disease on the island of Ireland was in 2012 at a forestry plantation in Co. Leitrim. The pathogen is now present in all 26 counties in Ireland and 6 counties in Northern Ireland, and it is considered unfeasible to eradicate. The spread of ash dieback disease is reflected in recent policy changes, which focus on management rather than eradication strategies. Since the first formal description of H. fraxineus in 2006, considerable research efforts have been made by the international scientific community to understand the biology of the pathogen and to develop management strategies against it. This review provides an update of current knowledge of H. fraxineus biology and infection. We then explore examples of mitigation techniques that have been trialled in Europe, in order to identify strategies that are feasible for disease management at a local level on the island of Ireland. Finally, we outline five key avenues of research that have the potential to provide breakthroughs in methods to protect valuable F. excelsior resources.

1) Introduction: Negative pressure wound therapy (NPWT) is a frequently applied open abdomen (OA) treatment. There are only a few experimental data supporting this method and describing the optimal settings and pressure distribution in the abdominal cavity during this procedure. The aim of our study was to evaluate pressure values at different points of the abdominal cavity during NPWT in experimental abdominal compartment syndrome (ACS) animal model. 2) Methods: In this study (permission Nr. 13/2014/UDCAR) 27 Hungahib pigs (15.4- 20.2 kg) were operated. ACS was generated by implanting a plastic bag in the abdomen through mini-laparotomy and filled with 2100- 3300 ml saline solution (37 C°) to an intraabdominal pressure (IAP) of 30 mmHg. After 3 hours, NPWT (Vivano Med ® Abdominal Kit, Paul Hartmann AG, Germany) or Bogota bag was applied. NPWT group was divided into -50, -100 and 150 mmHg suction group. Pressure distribution to the abdominal cavity was monitored at 6 different points of the abdomen via a multichannel pressure monitoring system. 3) Results: The absolute pressure levels were significantly higher above than below the layer. The values of the pressure were similar in the midline than laterally. Amongst the bowels, the pressure values changed periodically between 0 and -12 mmHg which might be caused by the peristaltic movements. 4) Conclusions: The porcine model of the present study seems to be well applicable for investigating ACS and NPWT. It was possible to provide valuable for clinicians. The pressure was well distributed by the protective layer to the lateral parts of the abdomen and this phenomenon did not change considerably during the therapy.

This pilot study explores the potential of using a citizen science approach for sourcing volunteered geographic information via social media to research wildlife tourism interactions with endangered Borneo Pygmy Elephants on the lower Kinabatangan River in Sabah, Malaysia. Such information is critical if the lower Kinabatangan region is to achieve the United Nations Sustainable Development Goals through a sustainable tourism industry based around viewing the pygmy elephants. Guests and guides from the Sukau Rainforest Lodge were encouraged to become close-range remote sensors by sharing geotagged photographs of pygmy elephant sightings on Flickr. A ten week on-ground trail generated 247 photographs shared by 17 individual contributors with approximately two-thirds (65%) of photographs being georeferenced for the time and location of the elephant sighting. Plotting those sighting to explore the vegetation matrix (i.e. remnant forest or oil palm plantation) showed almost three-quarter (73%) of the sightings occurred within 1 km of an oil palm plantation. Of greater concern is that one in two sightings (50%) along the river occurred within the 500 m of an oil palm planation, which is inside the riparian buffer that the Sabah Government recommended for conservation of the elephants in their Lower Kinabatangan range. This study therefore demonstrates proof of concept for this research method and its further application at the nexus of wildlife conservation and sustainable ecotourism research.

Primary varicella-zoster virus (VZV) infection causes varicella (chickenpox) and the establishment of a lifelong latent infection in ganglionic neurons. VZV reactivates in about one-third of infected individuals to cause herpes zoster, often accompanied by neurological complications. The restricted host range of VZV and, until recently, the lack of suitable in vitro models to study VZV latency have seriously hampered molecular studies of viral latency. Nevertheless, recent technological advances facilitated a series of exciting studies that resulted in the discovery of a VZV latency-associated transcript (VLT) and have redefined our understanding of VZV latency and factors that initiate reactivation. Together, these findings pave the way for a new era of research that may finally unravel the precise molecular mechanisms that govern latency. In this review, we will summarize the implications of recent discoveries in the VZV latency field from both a virus and host perspective and provide a roadmap for future studies.

To assist firefighters and other first responders to use their existing equipment for respiration during the COVID-19 pandemic without using single-use, low-supply, masks, this study outlines an open source kit to convert a 3M-manufactured Scott Safety self-contained breathing apparatus (SCBA) into a powered air-purifying particulate respirator (PAPR). The open source PAPR can be fabricated with a low-cost 3-D printer and widely available components for less than $150, replacing commercial conversion kits saving 85% or full-fledged proprietary PAPRs saving over 90%. The parametric designs allow for adaptation to other core components and can be custom fit specifically to fire-fighter equipment, including their suspenders. The open source PAPR has controllable air flow and its design enables breathing even if the fan is disconnected or if the battery dies. The open source PAPR was tested for air flow as a function of battery life and was found to meet NIOSH air flow requirements for 4 hours, which is 300% over expected regular use.

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Undoubtedly the Wageningen B-screw Series is the most widely used systematic propeller series. It is very popular to preselect propeller dimensions during the preliminary design stage before performing a more thorough optimization, but in the smaller end of the market it is often used to merely select the final propeller. Over time the originally measured data sets were faired and scaled to a uniform Reynolds number of 2·10⁶ to increase the reliability of the series. With the advent of the computer, polynomials for the thrust and torque values were calculated based on the available data sets. The measured data are typically presented in the well-known open-water curves of thrust and torque coefficients K_T and K_Q versus the advance coefficient J. Changing the presentation from these diagrams to efficiency maps reveals some unsuspected and surprising behaviours, such as multiple optima when optimizing for efficiency or even no optimum at all for certain conditions. These artefacts get more pronounced at higher pitch to diameter ratios and low blade numbers. The present work builds upon the paper presented by the author at the AMT’17 and smp’19 conferences and now includes the extended efficiency maps, as suggested by Danckwardt, for all propellers of the Wageningen B-screw Series.

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

This article explores the lack of unanimity regarding the nomenclature used to refer to the field of research that explores the intersection between art and science. A series of examples of nomenclatures and the context in which they are applied are listed and referenced. The diverse terminology reflects the heterogeneity, transdisciplinarity, and complexity of a research field in full expansion.

The world is currently facing an outbreak of the monkeypox virus and confirmed cases have been reported from 74 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of more than 255,000 Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Thermal sterilization is generally avoided for 3-D printed components because of the relatively low deformation temperatures for common thermoplastics used for material extrusion-based additive manufacturing. 3-D printing materials required for high-temperature heat sterilizable components for COVID-19 and other applications demands 3-D printers with heated beds, hot ends that can reach higher temperatures than polytetrafluoroethylene (PTFE) hot ends and heated chambers to avoid part warping and delamination. There are several high temperature printers on the market, but their high costs make them inaccessible for full home-based distributed manufacturing required during pandemic lockdowns. To allow for all these requirements to be met for under $1,000, the Cerberus – an open source three-headed self-replicating rapid prototyper (RepRap) was designed and tested with the following capabilities: i) 200^oC-capable heated bed, ii) 500^oC-capabel hot end, iii) isolated heated chamber with 1kW space heater core and iv) mains voltage chamber and bed heating for rapid start. The Cereberus successfully prints polyetherketoneketone (PEKK) and polyetherimide (PEI, ULTEM) with tensile strengths of 77.5 and 80.5 MPa, respectively. As a case study, open source face masks were 3-D printed in PEKK and shown not to warp upon widely home-accessible oven-based sterilization.

The separation of rare earth metals (REM) from a neodymium magnet has been widely studied in the last year. We have shown that the waste of computer hard disk contains 25.41 % neodymium, 64.09 % iron, and &lt;&lt;1 % boron. To further isolate rare-earth metals, the magnet was acidically dissolved in open and closed systems. In both methods of dissolution was used concentrated nitric acid. The difference between these methods are the conditions of dissolution of magnet. The magnet was dissolved in a microwave sample preparation system at different temperatures and pressures in a closed system. In the open system, the acid dissolution of the magnet conducted at room temperature. 0.2 g of the neodymium magnet sample was taken under two conditions, and the dissolution process in the closed system lasted 1 hour, and in the open system-30-40 minutes. The open system is a non-laborious, simple and cheap method of dissolving the magnet by comparing both systems. Therefore, an open sample preparation system is used for further work. To remove the iron in the magnet, oxalic acid was used and precipitated as oxalates under both conditions. According to the result of the ICP-MS method, it is shown that the neodymium and iron contents in the precipitate are 24.66 % and 0.06 %, respectively. This shows that the iron has almost completely passed to the filtrate. Thus, it is possible to remove the iron from the sample.

The latest developments in digital humanities have increasingly enabled the construction of large data sets which can easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with temporal information and are usually based on a strict hierarchy (country, city, street, house number, etc.) that is hard, if not impossible, to use with historical data. Indeed, historical data are full of uncertainties (temporal, textual, positional accuracy, confidence in historical sources) that can not be ignored or entirely resolved. We propose an open source, open data, extensible solution for geocoding that is based on gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address searched by the user. The matching criteria are customisable and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that geocoding results can be checked and collaboratively edited. The system has been tested on the city of Paris, France, for the 19th and the 20th centuries. It shows high response rates and is fast enough to be used interactively.

Based on the total scholarly article output of Norway, we investigated the coverage and degree of openness according to three bibliographic services 1) Google Scholar, 2) oaDOI by Impact Story and 3) 1findr by 1science. According to Google Scholar, we find that more than 70% of all Norwegian articles are openly available. However, degrees are profoundly lower according to oaDOI and 1findr, respectively 31% and 52%. Varying degrees are mainly caused by different interpretations of openness, with oaDOI being most restrictive. Furthermore, open shares vary considerably by discipline, with the Medcine and Health sciences at the upper and the Humanities at the lower end. We also determined the citation frequencies using Cited-by values as of Google Scholar, applying year and subject normalization. We find a significant citation advantage for open articles. However, this is not the case for all types of openness. In fact, the category Open Access journals was by far lowest cited, indicating that young journals with a declared open access policy still lack recognition.

Simulated crime scene investigation is an essential component of forensic science education, but its implementation poses challenges relating to cost, accessibility and breadth of experience. Virtual reality (VR) is an emerging technology which offers exciting prospects for teaching and learning, especially for imparting practical skills. We document here a multidisciplinary experimental study in which a bespoke VR crime scene app was designed and implemented, after which it was tested by both undergraduate student and staff/postgraduate student cohorts. Through both qualitative and quantitative analyses, we demonstrate that VR applications support learning of practical crime scene processing skills. VR-based practical sessions have the potential to add value to forensic science courses through offering cost-effective practical experience and the ability to work in isolation, in a variety of different scenarios. Both user groups reported high levels of satisfaction with the process and reports of adverse effects (motion sickness) were minimal. With reference to user feedback, we proceed to evaluate the scalability and development challenges associated with large-scale implementation of VR as an adjunct to forensic science education.

The Norwegian Museum of Science and Technology have developed a learning concept for school classes in science centres named ‘learning trails’. In this concept, groups of students perform a series of thematically related experiments with installations in the science centre. The learning trails are designed to support the generic learning outcomes for science centre visits. We argue for using the previously developed Engagement Profile in an indicator to determine both media forms and generic learning outcomes for such learning concepts. Further, we implemented the learning trails in two modes: one mode used paper-based content to guide the students, while the other mode supported the use of tablet PCs where engaging content is triggered when the students approach the location of an experiment in the learning trail. We studied the engagement factors of the learning trails and observed how school classes use these. In a study with 113 students from lower secondary school, they answered short questionnaires that were integrated into the implementation of the learning trails. While the concept of the learning trails was evaluated positively, we could not find significant differences in how engaging the two implemented modes were.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Revitalization of open water bodies into recreational areas are becoming widely popular in many parts of Sri Lanka. Understanding community perception and expectation on recreational development have important policy and cost-effectiveness implications. This paper explores the public perspectives on development, conservation and value of the open water recreational development at Diyatha Uyana and surrounding area. Contingent Valuation Method has been applied to estimate peoples’ Willingness To Pay for recreational spaces in the selected area. A total of 300 residents from different socioeconomic backgrounds were selected randomly for the study. Enjoying peace and relaxation, taking children to play and breath clean air were mentioned as most important aspects of open water body areas. More frequent visits occur to enjoy peace and relaxation, breath clean air and enjoy the natural landscape. Almost all the participants were agreed upon conserving open water body areas in urban settings. Willingness to pay was significantly associated with income and occupation status. It yielded a monthly average payment of Rs.446.93 per household for another five years’ time. The limited budget was the main reason for not willing to pay. This paper is significant as there is no contingent valuation method study has been carried on water body development in Sri Lanka.

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.

A controlled ammonia (NH₃) release experiment was performed at a grassland site to quantify the effect of dry deposition, at the field scale between the source and the receptors (NH₃ measurement locations), on the estimates of emission rates by means of inverse dispersion modelling. NH₃ was released for 3 hours at a constant rate of Q = 6.29 mg s⁻¹ from a grid of 36 orifices spread over an area of 250 m². The increase in line-integrated NH₃ concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH₃ (Q_bLS/Q) was calculated from the measurements of the individual instruments. Q_bLS/Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH₃ dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH₃ between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a ‘big‑leaf’ canopy resistance analogy. Canopy resistances (generally termed Rc) that provided Q_bLS/Q = 1 ranged from 75 to 290 s m⁻¹, showing that surface removal of NH₃ by dry deposition can plausibly explain the original underestimation of Q_bLS/Q. The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously distributed hot spots, such as e.g. urine patches on pastures in the case of NH₃.

The number of documents published on the Web other languages than English grows every year. As a consequence, it increases the necessity of extracting useful information from different languages, pointing out the importance of researching Open Information Extraction (OIE) techniques. Different OIE methods have been dealing with features from a unique language. On the other hand, few approaches tackle multilingual aspects. In such approaches, multilingual is only treated as an extraction method, which results in low precision due to the use of general rules. Multilingual methods have been applied to a vast amount of problems in Natural Language Processing achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We state that a multilingual approach can enhance OIE methods, being ideal to evaluate and compare OIE systems, and as a consequence, to applying it to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase the acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning the state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

The digital format opens up new possibilities for interaction with monographic publications. In particular, annotation tools make it possible to broaden the discussion on the content of a book, to suggest new ideas, to report errors or inaccuracies, and to conduct open peer reviews. However, this requires the support of the users who might not yet be familiar with the annotation of digital documents. This paper will give concrete examples and recommendations for exploiting the potential of annotation in academic research and teaching. After presenting the annotation tool of Hypothesis, the article focuses on its use in the context of HIRMEOS (High Integration of Research Monographs in the European Open Science Infrastructure), a project aimed to improve the Open Access digital monograph. The general line and the aims of a post-peer review experiment with the annotation tool, as well as its usage in didactic activities concerning monographic publications are presented and proposed as potential best practices for similar annotation activities.

The use of naturally available materials not conforming to traditional specifications or standards, in the base and sub-base layers of road pavement structures, stabilised with New-age (Nano) Modified Emulsions (NME), have been tested, implemented and successfully verified through Accelerated Pavement Testing (APT) in South Africa. This was made possible through the development and use of a design procedure addressing fundamental principles and based on scientific concepts, which are universally applicable. The understanding of and incorporation of the chemical interaction between the mineralogy of the materials and a NME stabilising agent (compatibility between the chemistry of the reactive agents and material mineralogy) into the design approach is key to achieving the required engineering properties. Stabilised materials evaluation is done using tests indicative of the basic engineering properties (physics) of compressive strengths, tensile strengths and durability. This article describes the basic materials design approach developed to ensure that organofunctional nano-silane modified emulsions can successfully be used for pavement layer construction utilising naturally available materials, at a low risk. The enablement of the use of naturally available materials in all pavement layers can have a considerable impact on the unit cost and life-cycle costs of road transportation infrastructure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

1) Background: A population-based cross-sectional study was conducted to understand how green cover and accessibility of common public open spaces in compact urban areas affect physical activeness of resident. 2) Methods: A total of 554 residents completed a structured questionnaire on quality-of-life, physical activity level, and healthy eating practice. 3) Results: The sampled population lived with green cover averaged 10.11 ± 7.95% (ranged 1.56–9.88), whereas majority (90%) of the residents performed physical activities at medium and high levels. Metabolic Equivalent of Task (MET)-minutes/week was associated with the green cover percentage (Pearson r = 0.092; p < 0.05). Irrespective of age and physical activity level, active residents commonly used public open spaces within district for performing exercise, especially parks and promenade were mostly used by older residents while sports facilities by the younger groups. 4) Conclusions: Current findings suggested promotion of exercise could be achieved by the design or redesign of built environment to include more parks accessible to the residents with the increase of vegetation.

Attitudes and behaviours towards the natural environment have been extensively studied in certain cultural settings during the last 40 years. In education, the teacher's ability to grasp the fundamentals of an academic subject may define his or her own attitudes towards that discipline; certainly the reverse is also valid. The correlations between affective and cognitive domains appear to play a significant role in teaching-learning dynamics. In this study we seek to assess whether the affective posture towards school sciences of a cohort of teachers in rural communities shows an association with their cognitive competence in the disciplines. The results of this study provide evidence that there is a statistically significant correlation between the cognitive and affective domains for in-service teachers. The affective domain, therefore, could serve as a predictor for cognitive competency and self-efficacy expectancies with respect to both content and career fulfilment.

In Data Science we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, described are the origins and many evolutions of the Data Science theme. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues.

Plant breeding is central to agriculture, and shifts in plant breeding practices (e.g., hybrid development) and selection goals (e.g., response to synthetic fertilizer) have catalyzed monumental and persistent changes in agricultural production systems of all scales with social, political, economic, and environmental repercussions. While plant breeders are largely trained in the sciences of biology, genetics, and statistics, we posit an ethical imperative to examine the degree of equity with which the benefits of new research and plant varieties are distributed. In the United States, the history of plant breeding parallels the colonial history of agriculture, which compels reflection by current plant breeders about their role in shaping our agricultural system. In this perspective essay, we examine longstanding ideas about equitable food systems through the lens of public plant breeding in the United States. We propose a framework for equitable public plant breeding with respect to both its process and outcomes, and we intend for the ideas presented herein to catalyze reflection, discussions, and actions as the plant breeding community seeks greater equity in the food and seed systems our work supports.

Wearable and mobile technology has advanced in leaps and bounds in the last decade with technological advances creating a role from enhancing healthy living to monitoring and treating disease. However, the discussion about the ethical use of such commercial technology in the community, especially in minors, is lacking behind. In this paper, we first summarize the major ethical concerns that arise from the usage of commercially available wearable technology in children, with a focus on smart watches, highlighting issues around the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used without medical advice. Then through a relevant thought experiment we move on to outline some further ethical concerns that are connected to the use of wearables by minors, to wit the issue of informed consent in the case of minors, forcing them to live in the spotlight, and compromising their right to an open future. We conclude with the view that mitigating potential pitfalls and enhancing the benefits of wearable technology especially for minors requires brave and comprehensive moral debates.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

Preprints are regularly cited in peer reviewed journal articles, books and conference paper. Are preprint citations somehow less important than citations to peer reviewed research papers? This study investigates citation patterns between 2017 and 2020 for preprints published in three preprint servers, one specializing in biology (bioRxiv), one in chemistry (ChemRxiv), and another hosting preprints in all disciplines (Research Square). As evaluation of scholarship continues to largely rely on citation-based metrics, this analysis and its outcomes will be useful to inform new research-based education in today’s scholarly communication.

Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measures such as task accuracy and latency. To create a better understanding of human behavior and brain functionality, we should introduce other measures and analyze behavior from various aspects. However, it is technically complex and costly to design and implement the experiments that record multiple measures. To address this issue, a platform that allows synchronizing multiple measures from human behavior is needed. Method: This paper introduces an opensource platform named OpenSync, which can be used to synchronize multiple measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain the structure and details of OpenSync, provide two case studies in PsychoPy and Unity. Comparison with existing tools: Unlike proprietary systems (e.g., iMotions), OpenSync is free and it can be used inside any opensource experiment design software (e.g., PsychoPy, OpenSesame, Unity, etc., https://pypi.org/project/OpenSync/ and https://github.com/moeinrazavi/OpenSync_Unity). Results: Our experimental results show that the OpenSync platform is able to synchronize multiple measures with microsecond resolution.

In common model testing practise, the measured values of the self propulsion test are split into the characteristics of the hull, the propeller and into the interaction factors. These coefficients are scaled separately to the respective full scale values and subsequently reassembled to give the power prediction. The accuracy of this power prediction depends {\em inter alia} on the accuracy of the measured values and the scaling procedure. An inherent problem of this approach is, that it is virtually impossible to verify each single step, because of the complex nature of the underlying problem. In recent years the scaling of the open-water characteristics of propeller model tests attracted a renewed interest, fuelled by competitive tests, which became the norm due to requests of the customer. This paper will show the influence of different scaling procedures on the predicted power. The prediction is compared to the measured trials data and the quality of the prediction will be judged. The procedures examined are the standard ITTC~1978 procedure plus derivatives of it: the Meyne, the strip method and the βᵢ-method.

The popular motor drive systems with a single two-level voltage source inverter (VSI) have one main problem that is the occurrence of the common-mode voltage (CMV), which is an effect of the electromagnetic interference, shaft voltage, bearing currents, leakage current. These cause the high stress, increasung temperature and early mechanical failure in machine. To overcome this problem, the technology of the dual two-level VSI fed open-end three-phase ac loads is now available to eliminate the CMV at the ac/induction motor load with the 120-degree modulation technique for controlling each inverter. In this paper, the discontinuous space vector modulation (DSVM) schemes are proposed and applied for the dual two-level VSI fed open-end load. It is based on the 120-degree modulation technique by using only 12 active voltage vectors and the 10 zero voltage vectors from the total 64 voltage vectors along with the different five-segment swicthing sequence designs with centralizing pulse width modulation technqiue in order to not only cancel the CMV in the ac load, but also reduce the switching number/switching loss of the conversion system. Among the various DSVM schemes, their performances are compared in this paper, such as the number of the switching, the step and peak value of the CMV in each inverter, and the quality of the output waveform, etc. The details of the verfication and comparison are carried out by simulation using Matlab/Simulink software.

Gender and identity issues permeate society as a whole. Therefore, the matters involving transgender individuals should be analised in order to understand the difficulties experienced by this population and the social practices implemented. In this sense, the objective of this study was to investigate the strategic themes and their evolution in relation to the theme. For this, a bibliometric performance and network analysis (BPNA) was carried out with the existing data in the Web of Science database between 1954 and march 2021. Twenty-three thousand and four hundred and seventy-one (23,471) articles were identified, which were included in the SciMAT software to perform a bibliometric analysis, resulting in the graph of the thematic evolution structure and the strategic diagram, in which 8 motor themes and a cross-cutting theme of great magnitude are highlighted, which are discussed in depth. The results show the relation between the transgender theme and gender, identity, sexual orientation, hormone therapy and gender-affirming surgery. It is concluded that, despite the large number of associated researches, some areas of study are still incipient, such as the inclusion of transgender people in the formal labor market and in the prison context, thus opening field for further studies.

Open-plan office (OPO) layouts emerged to allow organizations to adapt to changing workplace demands. We explore the potential for OPOs to provide such adaptive capacity to respond to two contemporary issues for organizations: the chronic challenge of environmental sustainability, and the acute challenges emerging from the great COVID-19 homeworking experiment. We apply a socio-technical systems perspective and green ergonomics principles to investigate the relationship between an OPO environment and the occupants working within it. In doing so, we consider the relevant technical and human factors such as green technology and employee green behavior. We also consider how a green OPO might provide non-carbon benefits such as improving occupant wellbeing and supporting the emergence of a green organizational culture. Our investigation highlights several avenues through which an OPO designed with green ergonomic principles could benefit occupants, the organizations they work for, and the natural environment of which they are a part and on which they depend. We find reason to suspect that green OPOs could play an important role in sustainable development; and offer a research agenda to help determine whether in fact OPOs are another example of how going green is good business.

A study of Bianchi Type I cosmological model is undertaken in the framework of creation of particles. To accommodate the creation of new particles, the universe is regarded as an Open thermodynamical system. The energy conservation equation is modified with the incorporation of a creation pressure in the energy momentum tensor. Exact solutions of the field equations are obtained (i) for a particular choice of the particle creation function and (ii) by considering the deceleration parameter to be constant. In the first model the behavior of the solution at late times is investigated. The physical aspects of the model have also been discussed. In the case of the second model we have restricted our analysis to the power law behaviour for the average scale factor. This leads to a particular form for the particle creation function. The behavior of the solution is investigated and the physical aspects of the model have also been discussed for the matter dominated era.

The present study was conducted with the aim of analyzing the impact of social networks on political socialization and political participation of political science students of the Islamic Azad University of Tehran, Tehran south branch during 2007-2017. This article is a descriptive-survey research based on the theory of planned behavior and has been done based on random sampling with a population of 280 samples. The findings indicate that 93% of students use social media and spend a significant part of their study hours on social networks, which mainly include Telegram, Instagram, Facebook, Twitter and WhatsApp. The variables related to social networks affect the socialization and political participation of students, and the extent of the impact of social networks on encouraging individuals to participate in the election as a component of political socialization is positive and significant. This finding and other findings are a positive and significant impact of social networks on the attitudes, values ​​and norms, attitudes and behaviors of political science students as a sample population, and thus the hypothesis of this research has been confirmed.

Scientists observe, discover, justify and eventually share their findings with the scientific community. Dissemination is an integral aspect of scientific discovery since discoveries which go unnoticed have no or little impact on science. Today, peer-review is part of this process of scientific dissemination as it contributes proactively to the quality of a scientific article. As the numbers of scientific journals and scientific articles published therein are increasing steady, processes such as the single-blind or double-blind peer review are facing a near collapse situation. In fact, these traditional forms of reviewing have reached their limits and, because of this, are also increasingly considered as unfair, sloppy, superficial and even biased. In this manuscript we propose forms of Post Publication Public Peer Review (P4R) as valuable alternatives to the traditional blind peer review system. We describe how the journal Sci has explored such an approach and provide first empirical evidence of the benefits and also challenges such a P4R approach is facing.

Trends such as Industrial Internet of Things (IIoT) and Industry 4.0 have increased the need to use powerfull network technologies in industrial automation. The growing communication in industrial automation is harnessing the productivity and efficiency of manufacturing and process automation with minimum human intervention. Due to the ongoing evolution of industrial networks from Fieldbus technologies to Ethernet, the new opportunity has emerged to integrate the Software Defined Networking (SDN) technique. In this paper, we provide a brief overview of SDN in the domain of industrial automation. We propose a network architecture called Software Defined Industrial Automation Network (SDIAN), with the objective of improving network scalability and efficiency. To match the specific considerations and requirements of having a deterministic system in an industrial network, we propose two solutions for flow creation: Pro-active Flow Installation Scheme (PFIS) and Hybrid Flow-Installation Scheme (HFIS). We analytically quantify the proposed solutions in alleviating the overhead incurred from the flow setup cost. The analytical model is verified through monte carlo simulations. We also evaluate the SDIAN architecture and analyze the network performance of the modified topology using an emulator called Mininet. We further list and motivate SDIAN features and in particular report on an experimental food processing plant demonstration featuring Raspberry PIs (RPIs) instead of traditional Programmable Logic Controllers (PLCs). Our demonstration exemplifies the characteristics of SDIAN.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

A considerable body of research exists on women in leadership and likewise on women in STEM (science, technology, engineering, mathematics) fields. However, the intersection of the two is terra incognita: women in leadership in STEM. At the most fundamental level, we don’t even have a solid idea of how many women hold leadership positions in STEM. This study determined the proportion of women in leadership positions in several academic STEM areas via a sampling of institutions across the United States and other countries. In every area studied, women held fewer leadership positions than the proportion of female PhDs in those fields. The proportion of women in non-STEM specific top academic leadership roles was also examined to see what proportion of those individuals leading academic institutions might have background in a STEM discipline and how that compares to men in the same positions. This study opens the door to exploring the experiences of women who lead in STEM, which is likely to promote women’s participation in these fields.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

Barriers to inventing electronic devices involve challenges of iterating electronic designs due to long lead times for professional circuit board milling or high-costs of commercial milling machines. To overcome these barriers this study provides open source (OS) designs for a low-cost circuit milling machine. First, design modifications for mechanical and electrical sub-systems of the OS D3D Robotics prototyping system are provided. Next, Copper Carve, an OS custom graphical user interface, is developed to enable circuit board milling by implementing backlash and substrate distortion compensation. The performance of the OS D3D circuit mill is then quantified and validated for: positional accuracy, cut quality, feature accuracy and distortion compensation. Finally, the return on investment is calculated for inventors using it. The results show by properly compensating for motion inaccuracies with Copper Carve, the machine achieves a motion resolution of 10 microns, which is more than adequate for most circuit designs. The mill is at least five times less expensive than all commercial alternatives and the material costs of the D3D mill are repaid from fabricating 20-43 boards. The results show that the OS circuit mill is of high-enough quality to enable rapid invention and distributed manufacturing of complex products containing custom electronics.

Ramsey’s theory (RAM) from combinatorics and network theory goes looking for regularities and repeated patterns inside structures equipped with nodes and edges. RAM represents the outcome of a dual methodological commitment: by one side a top-down approach evaluates the possible arrangement of specific subgraphs when the number of graph’s vertices is already known, by another side a bottom-up approach calculates the possible number of graph’s vertices when the arrangement of specific subgraphs is already known. Since natural neural networks are often represented in terms of graphs, we suggest to utilize RAM for the analytical and computational assessment of a peculiar structure supplied with neuronal vertices and axonal edges, i.e., the human brain connectome. We discuss how a RAM approach in neuroscientific issues might be able to locate and trace unexplored motifs shared between different cortical and subcortical subareas. Furthermore, we will describe how notable RAM outcomes, such as the Ramsey’s theorem and the Ramsey’s number, could contribute to uncover still unknown anatomical connexions endowed in neuronal networks and unexpected functional interactions among grey zones of the human brain.

There is an intense need to optimize agrivoltaic systems. This article describes the invention of a new testing system: the parametric open source cold-frame agrivoltaic system (POSCAS). POSCAS is an adapted gardening cold-frame used in cold climates as it acts as a small greenhouse for agricultural production. POSCAS is designed to test partially transparent solar photovoltaic (PV) modules targeting the agrivoltaic market. It can both function as a traditional cold frame, but it can also be automated to function as a full-service greenhouse. The integrated PV module roof can be used to power the controls or it can be attached to a microinverter to produce power. POSCAS can be placed in an experimental array for testing, agricultural and power production. It can be easily adapted for any type of partially transparent PV module. An array of POSCAS systems al-lows for testing agrivoltaic impacts from the percent transparency of the modules by varying the thickness of a thin film PV material or the density of silicon-based cells, and various forms of optical enhancement, anti-reflection coatings and solar light spectral shifting materials in the back sheet. All agrivoltaic variables can be customized to identify ideal PV designs for a given agricultural crop.

Centrifuges are commonly required devices in medical diagnostics facilities as well as scientific laboratories. Although there are commercial and open source centrifuges, costs of the former and required electricity to operate the latter, limit accessibility in resource-constrained settings. There is a need for low-cost, human-powered, verified and reliable lab-scale centrifuge. This study provides the designs for a low-cost 100% 3-D printed centrifuge, which can be fabricated on any low-cost RepRap-class fused filament fabrication (FFF) or fused particle fabrication (FPF)-based 3-D printer. In addition, validation procedures are provided using a web camera and free and open source software. This paper provides the complete open source plans including instructions for fabrication and operation for a hand-powered centrifuge. This study successfully tested and validated the instrument, which can be operated anywhere in the world with no electricity inputs obtaining a radial velocity of over 1750rpm and over 50N of relative centrifugal force. Using commercial filament the instrument costs about US$25, which is less than half of all commercially available systems; however, the costs can be dropped further using recycled plastics on open source systems for over 99% savings. The results are discussed in the contexts of resource-constrained medical and scientific facilities.

The ability to replicate scientific experiments is a cornerstone of the scientific method. Sharing ideas, workflows, data, and protocols facilitates testing the generalizability of results, increases the speed that science progresses, and enhances quality control of published work. Fields of science such as medicine, the social sciences, and the physical sciences have embraced practices designed to increase replicability. Granting agencies, for example, may require data management plans and journals may require data and code availability statements along with the deposition of data and code in publicly available repositories. While many tools commonly used in replicable workflows such as distributed version control systems (e.g. “git”) or scripted programming languages for data cleaning and analysis may have a steep learning curve, their adoption can increase individual efficiency and facilitate collaborations both within entomology and across disciplines. The open science movement is developing within the discipline of entomology, but practitioners of these concepts or those desiring to work more collaboratively across disciplines may be unsure where or how to embrace these initiatives. This article is meant to introduce some of the tools entomologists can incorporate into their workflows to increase the replicability and openness of their work. We describe these tools and others, recommend additional resources for learning more about these tools, and discuss the benefits to both individuals and the scientific community and potential drawbacks associated with implementing a replicable workflow.

Today in times of increasing inequality, climate change, and major social challenges, education is the best way to equip citizens, scholars and leaders to implement meaningful change and prevent future crises. Biologi education and science will solve these problems to supporting sustainable development goals especially in soil remediation, clean water, education quality and clean and affordable energy. This paper will describe how biological education could solve these problems. New biology can solve the problem about hunger use biotech, use synthetic biological material to find new advance material. New biology could driving intersectorial, interdisciplinary and international connectivity, and the leveraging of existing investments in synthetic biology, materials science, allied science and technology areas, are the major challenges in delivering the Materials from Biology vision.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

This study explores how to delineate Russian cities in order to make them comparable on the world scale. In doing so we introduce the concept of large urban regions (LUR) applicable to the Russian urban context. This research is motivated by a principal research question: how to construct a statistical urban delineation, which would allow first, to demonstrate integration of cities into globalization, and second, to make global urban comparative research. Previous studies on urban delineation in Russia have focused almost exclusively on functional urban areas, which have substantial limitations and are not suitable for global urban comparisons. Addressing this research gap, we propose a new definition of Large Urban Regions (LUR). In doing so, first, we introduce the context of Russian cities (2), then we discuss existing Russian urban concepts (3), and justify a need for a new urban delineation (4). Afterwards, we present a general method to delineate Large Urban Regions in Russian context (5.1), and illustrate it in the two case studies of St. Petersburg (polycentric region) and Samara (monocentric region) (5.2). In the last part (6), we discuss the 10 the largest urban regions in Russia and describe a constructed database including all Russian LURs.

In Australia, orthodox soil scientists and alternative practitioners who promote ‘regenerative agriculture’ have not been communicating and engaging effectively with each other. Over many years scientists in CSIRO, state departments and universities have made significant achievements in mapping soil distribution, describing soil behaviour and identifying key soil properties and processes that are fundamental to healthy soil function. However, many alternative practitioners are dismissive of these achievements and highly critical of orthodox soil science. Yet many of the tools of soil science are essential to conduct evidence-based research towards elucidating how and why the exceptional results claimed by some alternative practitioners are achieved. We stress the importance of effective engagement and communication among all parties to resolve this ‘clash of cultures’.

In this paper, we introduce a new class of open sets that is called is*-open set . Also, we present the notion of is*-continuous, is*-open, is*-irresolute, is*-totally continuous, and is-contra-continuous mappings, and we investigate some properties of these mappings. Furthermore, we introduce some is*-separation axioms, and is*-mappings are related with is*-separation axioms. . .

Religious and spiritual struggles are typically assessed by self-report scales using closed-ended items, yet nascent research suggests that using open-ended items may complement and advance assessment. In the current study, undergraduate participants (N = 976) completed open-ended descriptions of their religious and spiritual struggles, the Religious and Spiritual Struggles Scale (RSS), and a standardized measures of religious belief salience. Qualitative coding showed that the themes emerging from open-ended descriptions generally fell within the broad domains of the RSS though some descriptions reflected more contextualized struggles. Scores derived from the open-ended responses to assess RSS domains achieved evidence of reliability as well as convergent and discriminant validity with the RSS . Correlations revealed a mix of similar and divergent associations between methods of assessing religious and spiritual struggles and religious belief salience. Open-ended descriptions of religious and spiritual struggles may yield reliable and valid information that is related to but distinct from assessments relying on closed-ended items.

Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. Central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this paper, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with Random Forest classifier using Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

We present the mathematical model of cooperative functioning of unconscious and consciousness. The model is based on the theory of open quantum systems. Unconscious and consciousness are treated as bio-information systems. The latter plays the role a measurement apparatus for the former. States of both systems are represented in Hilbert spaces. Consciousness performs measurements on the states which are generated in unconscious. This process of unconscious-conscious interaction is described by the scheme of indirect measurements. This scheme is widely used in quantum information theory and it leads to the theory of quantum instruments (Davis-Lewis-Ozawa). Our approach is known as quantum-like modeling. It should be sharply distinguished from modeling of genuine quantum physical processes in biosystems, in particular, in the brain. In the quantum-like framework, the brain is a black box processing information in the accordance with the laws of quantum theory. During the last 10-15 years this framework has been actively used in cognition, psychology, decision making, social and political sciences. The quantum-like scheme of unconscious-consciousness functioning has already been explored for sensation-perception modeling.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

Background: Rapid advancements in Distributed Ledger Technology (DLT), including blockchain, are foundational to a new era of digital innovation. This innovation has catalyzed the emergence of ‘Decentralized Science (DeSci),’ a new concept and movement that aims to address the challenges of modern science. Objective: Given the novelty of the field of DeSci, this study aims to provide a comprehensive definition of the term as well as explore and conceptualize shared values and guiding principles inherent to DeSci. Methods: In line with the objectives of this study, an exploratory literature review was conducted to identify and synthesize the scholarly and secondary literature. The search and selection process included six databases (PubMed, Google Scholar, Web of Science, IEEE Xplore, arXiv, and Social Science Research Network), and the search period was limited to the last 15 years, from 2008 to 2023. To identify relevant secondary literature, such as articles, reports, blog posts, and website content, a keyword search was conducted in three search engines (Google.com, Bing.com, and Yahoo.com). Owing to the novelty of the concept and movement of DeSci, the exploratory literature review was supplemented by an anonymous online-based expert survey using a combination of single-choice and open-ended questions. The experts were selected based on predefined inclusion criteria, in association with their activities in the field of DeSci. The responses to the single-choice questions were subject to statistical analysis, whereas the open-ended questions were analyzed using qualitative content analysis. Results: Seven studies were selected for evaluation as part of the search and selection process to identify relevant scholarly literature. Following the review of secondary literature, additional 24 publications were included in the analysis. In the expert survey, 39 valid datasets were collected and analyzed. Following the synthesis of the results of the exploratory literature review and expert survey, a comprehensive definition of the term ‘Decentralized Science’ (DeSci) was formulated to reflect recurring themes. As no publications that explicitly discussed or addressed the values or principles of DeSci in the exploratory literature review could be identified, a set of shared values and guiding principles for DeSci were defined based on the results of the expert survey. Conclusion: The results of this study underscore the emerging nature of DeSci, as evidenced by the limited availability of relevant information and scarcity of academic publications. While this study proposes a comprehensive definition of DeSci as well as a set of shared values and guiding principles, the results of this study highlight the importance of ongoing evaluation and validation. Furthermore, the results of this study indicate a clear need for future research in the field of DeSci, emphasizing its dynamic and developing nature.

The importance of measuring sustainability in tourism has been significantly advancing in recent years, following the need to manage the impact of tourism on territories and hosting communities. It was further boosted by the pandemic, where sustainability has been defined as one of the central elements to restart global tourism. The ETIS model, developed by the European Commission, is a point of reference based on self-assessment, data collection and analysis by the destinations themselves. The application of ETIS toolkit has faced many challenges, especially at sub-national level, mostly related to the lack of available and updated data to feed the model. The hypothesis explored by the authors is to solve the implementation issues, developing an indicator based on the use of the Sentiment Analysis to frame e-reputation and tourism satisfaction, and further combining it with other open data sources. The Tourism Sustainability Index (TSI) can provide a scalable and geo-referenced evaluation of tourism sustainability, measuring the four pillars and sub-components referenced to ETIS criteria, applicable to any tourism destination. Results show that the TSI can be seen as a consistent and valid tool for destinations to analyze sustainability, monitor its evolution through time periods and sub-areas, and compare it to other benchmark or competitive areas.

A revitalized practice of natural philosophy can help people to live a better life and promote a flourishing ecosystem. Such a philosophy is natural in two senses. First, it is natural by seeking to understand the whole of nature, including mental phenomena, In particular, a comprehensive natural philosophy should address the phenomena of sentience by embracing first- and second-person methods of investigation. Moreover, to expand our understanding of the world, natural philosophy should embrace a full panoply of explanations, similar to Aristotle’s four causes. Second, such a philosophy is natural by being grounded in human nature, taking full account of human capacities and limitations. Future natural philosophers should also make use of all human capacities, including emotion and intuition as well as reason and perception, to investigate nature. Finally, since the majority of our brain's activities are unconscious, natural philosophy should explore the unconscious mind with the aim of deepening our relation to the rest of nature and enhancing well-being.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

Scientists who hope to obtain a faculty position at a primarily undergraduate institution (PUI) need a distinct skill set and outlook on their future teaching and research career. To obtain a position at a PUI, candidates should 1) design a strategy for obtaining a faculty position that suits each individual’s career goals and aspirations, 2) prepare for the application process, on-campus interview, and contract negotiations, and 3) plan a strategy for the probationary period leading up to tenure and promotion. Given the different types of PUIs, candidates need to consider whether they seek a position that consists of all or mostly all teaching, or both teaching and research. Candidates should educate themselves on the expectations at PUI’s, including current thought, practice, and aspirations for science pedagogy, and gain teaching experience prior to seeking a suitable position. If the candidate’s goal is a position with both teaching and research, it is important to discuss with the current research mentor what projects the candidate can take with them to their new position. The candidate should also consider what types of projects will be successful with undergraduate student researchers in a PUI research environment Importantly, candidates should clearly demonstrate a commitment to diversity and inclusion in their teaching, research, and outreach, and application materials should demonstrate this. On interviews, candidates should be knowledgeable about the mission, values, and resources of the institution and how the candidate will contribute to that mission. Once hired, new faculty should discuss a formal or informal mentoring plan during the probationary period that includes peer evaluations on a regular basis, and maintain communication with the department chair or designated mentor regarding teaching, research, and service activities.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

Objective: To highlight opportunities for future nutrition intervention research within early childhood and education care (ECEC) settings, with a focus on generating evidence that has applicability to real-world policy and practice. Methods: An overview of opportunities to progress the field was developed by authors using a collaborative writing approach and informed by recent research in the field. The group developed a list of recommendations aligned with the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework. Pairs of authors drafted individual sections of the manuscript, which was then reviewed by a separate pair. The first and senior author consolidated all sections of the manuscript and sought critical input on draft iterations of the manuscript. Results: Interventions that employ digital platforms (Reach) in ECEC settings, as well as research in the family day care setting (Effectiveness) were identified as areas of opportunities. Research understanding the determinants of and effective strategies for dissemination (Adoption), implementation of nutrition programs, in addition to de-implementation (Implementation) of inappropriate nutrition practices, is warranted. For maintenance, there is a need to better understand sustainability and sustainment of interventions, in addition to undertaking policy-relevant research. Conclusions: The ECEC setting is prime for innovative and practical nutrition intervention research.

(1) Teeth, represent in humans the most resilient tissues. However, exposure to concentrated acids might lead to their obliteration, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper introduces novel method of 3D reconstruction of dental patterns as a crucial step for digital identification with dental records.; (2) With combination of modern methods of Micro-Computer Tomography, Cone Beam Computer Tomography, Attenuated Total Reflection in conjunction with Fourier-Transform Infrared Spectroscopy and Artificial Intelligence Convolutional Neural Network algorithms, the paper presents the way of 3D dental pattern reconstruction and human remains identification. Research studies morphology of teeth, bone, and dental materials (Amalgam, Composite, Glass-ionomer cement) under different periods of exposure to 75% sulfuric acid; (3) Results reveal significant volume loss in bone, enamel, dentine, and as well glass-ionomer cement. Results also reveal significant resistance of composite and amalgam dental materials to sulfuric acid impact, thus serving as strong parts in the dental pattern mosaic. Paper also introduces probably first successful artificial intelligence application in automated forensic CBCT segmentation.; (4) Interdisciplinary cooperation utilizing mentioned technologies can solve problem of human remains identification with 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.

Modern experimental astroparticle physics features large-scale setups measuring different messengers, namely high-energy particles generated by cosmic accelerators (e.g. supernova remnants, active galactic nuclei, etc): cosmic and gamma rays, neutrinos and recently discovered gravitational waves. Ongoing and future experiments are distributed over the Earth including ground, underground/underwater setups as well as balloon payloads and spacecrafts. The data acquired by these experiments have different formats, storage concepts and publication policies. Such differences are a crucial issue in the era of big data and of multi-messenger analysis strategies in astroparticle physics. We propose a service ASTROPARTICLE.ONLINE in the frame of which we develop an open science system which enables to publish, store, search, select and analyse astroparticle physics data. The cosmic-ray experiments KASCADE-Grande and TAIGA were chosen as pilot experiments to be included in this framework. In the first step of our initiative we will develop and test the following components of the full data life cycle concept: (i) describing, storing and reusing of astroparticle data; (ii) software for performing multi-experiment and multi-messenger analyses like deep-learning methods; (iii) outreach including example applications and tutorial for students and scientists outside the specific research field. In the present paper we describe the concepts of our initiative, and in particular the plans toward a common, federated astroparticle data storage.

Horticulture crops (fruit trees) had been grown and cultivated from ancient times in Central Asia. Few researchers have addressed the problem of this profitable sector in the former Great Silk Road, which was at the crossroads of trading avenues. Horticulture has received much attention in the last twenty years. To investigate the current state of research activity of horticulture in Central Asia, we downloaded 4205 English papers from the Scopus database between 2000-2020. We identified a total of 50 papers, and the last four years have witnessed significant growth in publication number, an average of 5 articles per year. Acta Horticulturea was one of the most productive journal. Institute of Plant Biology and Biotechnology (Almaty) and the Academy of Sciences of the Republic of Uzbekistan have worked productively to study high issues. United States Department of Agriculture and Swiss National Science Foundation with Karl Popper Foundation have supported scientific activity in the region. Top 15 highly cited articles were published within the framework of funded projects with international researchers. Researchers of Central Asia focused on walnut, grape and apple, studied on molecular level and cryopreservation of wild relatives for future use. Cherry, apricot, almond and pomegranate crops were less studied by researchers.

The digital transformation has initiated a paradigm shift in research and scholarly communication practices towards a more open scholarly culture. Although this transformation is slowly happening in the Digital Humanities field, open is not yet default. The article introduces the OpenMethods metablog, a community platform that highlights open research methods, tools, and practices within the context of the Digital Humanities by republishing open access content around methods and tools in various formats and languages. It also describes the platform’s technical infrastructure based on its requirements and main functionalities, and especially the collaborative content sourcing and editorial workflows. The article concludes with a discussion of the potentials of the OpenMethods metablog to overcome barriers towards open practices by focusing on inclusive, community sourced information based around opening up research processes and the challenges that need to be overcome to achieve its goals.

The science‐policy interface in climate change adaptation became better managed over the past decades. However, the scientists and other knowledge producers, as well as policy makers still need to take bolder steps to more effectively engage with others to apply science and shape up policies. This paper aims to provide practical recommendations, intended to promote conversations between science and policy sectors to address climate change issues. Here, I used two different approaches to synthesize experiences and identify recommendations: a literature review and a case study. The paper stress main findings: (1) The linear communication model is still commonly involved in the science - policy dialogue and proved to be useful to increase the relevance of science and data products to decision makers. (2) When a gap between knowledge producer and knowledge user or decision maker exists, the need for a third party to specialize in bridging the gap become essential. (3) Indigenous people and knowledge must be involved in adaptation policy making based on legitimation local and traditional knowledge, designing the consultation process to broadly engage local and indigenous people, facilitating meaningful dialogues between traditional knowledge and science, and developing initiatives to strengthen skills and capacity of indigenous communities.

In this work the general problem of the characterization of the topological phase of an open quantum system is addressed. In particular, we study the topological properties of Kitaev chains and ladders under the perturbing effect of a current flux injected into the system using an external normal lead and derived from it via a superconducting electrode. After discussing the topological phase diagram of the isolated systems, using a scattering technique within the Bogoliubov-de Gennes formulation, we analyze the differential conductance properties of these topological devices as a function of all relevant model parameters. The relevant problem of implementing local spectroscopic measurements to characterize topological systems is also addressed by studying the system electrical response as a function of the position and the distance of the normal electrode (tip). The results show how the signatures of topological order affect the electrical response of the analyzed systems, a subset of such signatures being robust also against the effects of a moderate amount of disorder. The analysis of the internal modes of the nanodevices demonstrates that topological protection can be lost when quantum states of an initially isolated topological system are hybridized with those of the external reservoirs. The conclusions of this work could be useful in understanding the topological phases of nanowire-based mesoscopic devices.

This paper presents the results of a recent study that was conducted with a number of German municipalities/cities. Based on the obtained and briefly presented recommendations emerging from the study, the authors propose the concept of an Urban Data Space (UDS), which facilitates an eco-system for data exchange and added value creation thereby utilizing the various types of data within a smart city/municipality. Looking at an Urban Data Space from within a German context and considering the current situation and developments in German municipalities, this paper proposes a reasonable classification of urban data that allows to relate the various data types to legal aspects and to conduct solid considerations regarding technical implementation designs and decisions. Furthermore, the Urban Data Space is described/analyzed in detail, and relevant stakeholders are identified, as well as corresponding technical artifacts are introduced. The authors propose to setup Urban Data Spaces based on emerging standards from the area of ICT reference architectures for Smart Cities, such as DIN SPEC 91357 “Open Urban Platform” and EIP SCC. Thereby, the paper walks the reader through the construction of an UDS based on the above mentioned architectures and outlines all the goals, recommendations and potentials, which an Urban Data Space can reveal to a municipality/city.

In most world’s countries, scholarship evaluation for tenure and promotion continues to rely on conventional criteria of publications in journals of high impact factor and grant funding. Continuing to hire and promote scholars for their achievements in research and in securing research funds exposes universities at risk because students, directly and indirectly through government funds, are the main source of revenues for academic institutions, whereas talented young researchers are those who actually carry out most of the published research. Purposeful scholarship evaluation needs to include all three areas of scholarly activity: research, teaching and mentoring, and service to society. Young scholars seeking tenure and promotion benefit from the practice of open science because it provides better and more impactful results with respect to each of the three areas of scholarship.

This article addresses the question why initiatives in the field of green business and sustainable development often fail. Therefore, it dismantles some typical patterns of failure and shows – as a case study – how these patterns can be challenged through an innovative educational concept: the green business and sustainable development school. The applied methodology is a real-life project that is designed through blended, interdisciplinary elements from business model canvas, Theory U, participation and design thinking. The results of the school initiative are discussed and evaluated by four distinctive stakeholder groups and outline the school’s supporting potential to overcome typical patterns of failure by the younger generation in the future. This article concludes with ideas to enhance the school concept reaching out to even more stakeholder-groups to increase its reliability and viability.

We explore the class of positive integers n that admit idempotent factorizations n=pq such that lambda(n) divides (p-1)(q-1), where lambda(n) is the Carmichael lambda function. Idempotent factorizations with p and q prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p and/or q. Idempotent factorizations are exactly those p and q that generate correctly functioning keys in the RSA 2-prime protocol with n as the modulus. While the resulting p and q have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p &lt; 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.