dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of data set To evaluate the range of expected uncertainties with co-heating tests and the key components, the approaches to uncertainty estimates detailed in section 5. 1 are applied to 14 co-heating tests previously conducted by the authors. Some details of specific cases or larger datasets can be found in previous publications  [4, 20, 55, 69, 74, 75] , although anonymity is preserved in some cases here. Relevant summary details can be found in Table  4. The case studies do not constitute a representative sample but, as it can be seen in the table, they provide an overview of a range of different buildings and tests carried out by the authors. Siviour analysis is used, with 24-hour aggregation periods. Previous work has suggested little difference between Siviour and MLR approaches and little benefit in adopting longer aggregation intervals  [55].
Acknowledgements This research was made possible by support from the  EPSRC   UCL Doctoral Prize Fellowship  [grant number  EP/N509577/1 ] and the  SEREINE Project  from  PROFEEL French National Funding Programme. The authors gratefully acknowledge the funding provided by the  Technology Strategy Board (now Innovate UK)  as part of its  Building Performance Evaluation Programme. This funding was used to undertake the in-depth performance tests on a number of the case study dwellings identified within this paper.
An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802. 11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home.
Processor Selection The BeagleBone Black-Wireless (BBB-W)  [58]  was selected as the BCI processor for its low cost, availability, compatibility, and WiFi capabilities. Moreover, the availability of an open-source LabVIEW toolkit (LINX LabVIEW  [59] ) significantly reduced the software redesign. The BBB-W has a 1 GHz ARM processor, 512 MB of DDR3 RAM, and 4 GB of onboard storage, providing the computational power and storage space necessary for the BCI headset.
Software For the development of the device, LabVIEW (National Instruments Inc. , Austin, TX, USA) was selected as the primary coding language due to its extensive libraries and access to National Instruments' hardware and software in the early phases of the design. We note, however, that any coding language could instead be used with the selected hardware, and, in fact, a C++ version of the BCI firmware module has also been developed. This section details the main considerations, modular design, and resulting open-and closed-loop characteristics for the system software. The primary focus throughout the software development was on maintaining real-time capability, modularity, and flexibility to implement different BCI applications, thereby increasing the interoperability of the system.
Communication The BeagleBone Black-Wireless (BeagleBoard. org Foundation, Oakland Charter Township, MI, USA) processing board has both WiFi and Bluetooth capabilities (802. 11 b/g/n WiFi and Bluetooth 4. 1 plus BLE), which are important for the goal of creating a completely portable system. This gives the BCI device the capability of communicating with any device that can be controlled remotely. In addition to communicating with WiFi-enabled devices, to remain completely portable, the device includes a user interface that communicates with the system through the available LabVIEW web service. For the design of this interface, HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and Javascript (JS) were selected as the base languages for the interface, since they can be used for the creation of a cross-platform interface that can be accessed from any browser and display that can handle the computational demands of the system.
Open-Loop Capabilities The BCI device can be used to collect and save raw data from a user according to an easily modifiable protocol. These data include five EEG channels, three EOG channels, and accelerometer data from the IMU. Due to the design considerations, the maximum sampling rate that can be achieved for raw data collection and saving is 80 Hz. To achieve this sampling rate, the system utilizes LabVIEW's point-by-point virtual instruments and channel mechanisms. Sampling up to 80 Hz means future applications can be developed that require a spectral analysis of the Delta, Theta, Alpha, Beta, and lower Gamma frequency bands.
Closed-Loop Capabilities To test the closed-loop capabilities of the system, an example experimental protocol was implemented. This experimental protocol includes a real-time signal processing pipeline, training data collection, training of a machine learning model, testing of the trained model in real time, a graphical user interface (GUI), and constant communication with a third-party device. Due to design considerations, the system processes EEG and EOG data at 40 Hz and can save data at 20 Hz while simultaneously processing the signal, controlling a third-party WiFi device/object, and controlling a user interface over the web server. Sampling at up to 40 Hz supports applications that require a spectral analysis of the Delta, Theta, Alpha, and lower Beta bands. The authors note that further coding optimization effort could be made on the firmware design, which would likely allow for higher sampling frequencies.
Model Calibration-For applications that rely on machine learning model predictions for the acquisition of a control signal, training data must first be collected to train the machine learning model. The system allows for customization of the protocol for different BCI paradigms. Functionality has been built to allow for the acquisition of multiple days of data, which can then be used to train a machine learning model or monitor task performance and progress. As an initial machine learning model selection, the system includes a support vector machine (SVM) library (including hyperparameter optimization and n-fold cross validation), which the user can initiate from the user interface. Once the SVM model is trained, the user is then able to proceed with the model-testing stage. In addition to collecting EEG data for each testing trial, this module also collects protocol-specific characteristics, which can be analyzed later by a clinician or researcher to verify the progress of a user through a specific protocol.
The authors note that, while only an SVM library has been developed, many types of machine learning model can be implemented in the device within the limits of the available onboard memory. Survey Collection-The proposed system includes a survey functionality that gives the user a way to provide feedback, which can be completed at any time. These results are stored onboard the processing unit for further analysis. This pop-up interface is presented in Section 3. 3. 1 , which can be modified depending on the type of feedback desired for a particular application. Debugging Interface-For ease of use, significant effort was made in developing a debugging user interface. The device's debugging interface, presented in Section 3. 3. 1, includes mechanisms to check whether the internal LabVIEW script is running, whether the web server is correctly activated, a signal-impedance check with a channel-selection mechanism, and a device-communication check.
All components of the system have been extensively bench tested and also validated with healthy adults, including an individual with chronic stroke. In the development of the proposed system, the importance of understanding the cascading nature of single design decisions cannot be overstated. Early design decisions can significantly impact the available options for hardware and software functionality and overall system operation. For the current system, the most influential design choice was in the selection of LabVIEW as the back-end coding language. While LabVIEW has a large number of well-tested libraries available, many of these libraries require a processing board developed by National Instruments. Due to the cost of those boards, the selection of the processing board was limited by whether the board was capable of using an open-source user-built LabVIEW library, which is not as well-tested as the libraries developed by National Instruments.
Many of the challenges faced in the development of the proposed system were due to incompatibilities between LabVIEW and the low-cost processing board. Careful selection of the high-level system components (such as the backend language, port selections, wireless protocol, etc. ) are critical for maximizing the performance and flexibility. In this regard, and to show the flexibility of our proposed system, we have recently programmed the board in C++ and achieved an open-loop sampling rate of 250 Hz. In conclusion, the proposed system should provide an open test bed for developing low-cost and portable yet effective custom EEG-based closed-loop BCI systems with wireless capabilities, which will help expand the potential user base and application domains and increase the feasibility for academic research and workforce development.
Depending on the sample, between 56,000 and 3. 4 million particles were analyzed. The PAQXOS software (Version 4. 2, SympaTec GmbH, Clausthal-Zellerfeld, Germany) was used for evaluation. Due to the non-elongated particle shape, we decided to use the Feret diameter parameters for estimating L, D, and L/D. Feret maximal and minimal diameters were calculated for each particle after consideration of all possible orientations (0 • to 180 • ). Therefore, the maximum and minimum can be significantly larger and smaller than the diameter of the equivalent circle, respectively. Finally, the ratio of L/D was determined for each tomato fraction. For SEM analysis, tomato particles were taken from each sample and initially sputtered with a thin layer of gold (Edwards Sputter Coater, Atlas Copco Group, Stockholm Sweden).
Background: Fibromyalgia syndrome (FMS) is defined as chronic widespread pain associated with sleep disorders, cognitive dysfunction, and somatic symptoms present for at least three months and cannot be better explained by another diagnosis. Objectives: To examine efficacy and safety of non-pharmacological interventions for FMS in adults reported in Cochrane Reviews, and reporting quality of reviews. Methods: Systematic reviews of randomised controlled trials (RCTs) of non-pharmacological interventions for FMS were identified from the Cochrane Database of Systematic Reviews (CDSR 2022, Issue 3 and CDSR 2023 Issue 6). Methodological quality was assessed using the AMSTAR-2 tool and a set of methodological criteria critical for analgesic effects. The primary efficacy outcomes of interest were clinically relevant pain relief, improvement in health-related quality of life (HRQoL), acceptability, safety, and reduction of mobility difficulties as reported by study participants.
Determining the incidence of FMS is problematic. A US large insurance claims database study found that the US age-adjusted rate for 'new' FMS was 6. 88 cases per 1000 person-years for men and 11. 28 cases per 1000 person-years for women between 1997 and 2002  [8]. The clinical presentation of people with FMS is heterogenous. FMS can be associated with mental disorders (mainly anxiety and depressive disorders) and other chronic secondary pain syndromes such as inflammatory rheumatic diseases and osteoarthritis  [9]. Most people with FMS in secondary and tertiary medical care report high disability levels and poor health-related quality of life (HRQoL), along with extensive use of medical care  [10]. FMS symptoms are difficult to treat effectively, and only a minority of individuals experience a clinically relevant benefit from any one intervention  [10].
Eligibility criteria We included any Cochrane Review (with or without meta-analysis) of randomised controlled trials (RCTs) and quasi-RCTs of nonpharmacological interventions for adults (aged 18 years and older) with a diagnosis of FMS using one of the established criteria  [22] [23] [24] [25] [26]. We only included reviews with a range of medical conditions if data for FMS had been reported separately. We excluded reviews of non-pharmacological interventions for FMS in children and adolescents, and reviews with mixed populations if outcomes were not reported separately for adults. In the absence of an internationally accepted definition and classification of non-pharmacological interventions, we use a definition from  [27] , which defined non-pharmacological interventions as therapies that do not involve taking medicine or any other active substance.
Search, selection process, quality assessments We searched the most recent issue of the Cochrane Database of Systematic Reviews (via the Cochrane Library) on 30 September 2022 and 15 June 2023, with no date limits (see Appendix B). Two overview authors independently screened reviews against the eligibility criteria (RAM, JB, WH). If a review had been updated, we used the most recent version. Two authors independently performed assessments of methodological quality (EF, WH), extracted data (RB, JB, WH), analysed data if required (JB, WH), assessed how the review authors had used the GRADE criteria (EF, WH), and made their own GRADE assessments based on the information provided (EF, WH). Some of the overview authors were also authors of eligible Cochrane Reviews; final assessment decisions were made by overview authors who did not have a conflict of interest with that review.
Assessment of methodological quality of included reviews We assessed included reviews against the AMSTAR-2 criteria  [33]  to provide context about whether and to what extent the reviews' methods may have affected the overview's comprehensiveness and results. We conducted additional checks of potentially critical factors in the evaluation of efficacy  [34]  (see Table  2  and Appendix C). To assess the risk of bias due to missing results in a review, we collected data on any imputation methods used for missing data.
Summary of findings and assessment of the certainty of the evidence We used GRADE to assess the certainty of the evidence related to the key outcomes (Appendix A)  [35]. Two overview authors independently rated the certainty of the evidence for each outcome, blinded to any GRADE evaluation in the original reviews. The two assessments for each review were compared. We took account of how the Cochrane Review authors had used the GRADE criteria and noted where the overall rating for a particular outcome needed to be adjusted as recommended by GRADE  [35]. In circumstances where no data were reported for an outcome, we reported the certainty of evidence as very low  [36]. We used the following descriptors for levels of evidence  [37]  and we note that "substantially different" in this context implies a large enough difference that it might affect a decision: • High: this research provides a very good indication of the likely effect.
Limitations of this overview There are several limitations in our overview. As with any review, an outcome could have been reported in the original paper that was not picked up by the Cochrane Review. We did not check the data extraction of the original studies. The overview included only Cochrane Reviews, although we have compared the findings of our overview with other reviews of the same interventions, this overview does not include all the available non-pharmacological interventions. Although overview authors were also authors of some of the reviews, we ensured, the data extraction and quality assessment of those reviews were performed by overview authors not involved in the original reviews, but with all authors able to comment. Procedures to minimise bias were discussed with Cochrane editorial teams at the protocol stage.
Implications for randomized controlled trials The GRADE rating of the certainty of evidence for pain was low to very low in nine reviews and moderate in one review. Some Cochrane authors have argued there should be a halt on trial registration, until the quality and focus of studies with psychological therapies in chronic pain radically improve, because of a significant threat of research waste  [70]. Future randomized clinical trials of non-pharmacological interventions delivered by health care professionals (e. g. physiotherapy, psychological therapies) should improve the implementation of the intervention and provide clearer reporting of blinding status, particularly of trial personnel who can be easily blinded such data managers, the data safety and monitoring committee, statisticians and conclusion makers  [71].
For Gongolaria barbata, attention should be paid not to confuse immature hermaphroditic conceptacles with antheridial conceptacles (Figure  5G ). If male thalli occur in a population of Gongolaria barbata selected for monitoring, mature antheridial should be added as a fourth category to the three defined for the hermaphroditic conceptacles (Figure  5G ). The level of maturity is recorded for all receptacles collected. If different numbers of receptacles are collected in different monitoring dates (or from different sites), the maturity data will be best presented as percentages of receptacles belonging to the three categories defined above.
We strongly recommend that for each monitoring date some important environmental data should be measured and reported, in particular seawater temperature and photoperiod. This information will allow to infer possible correlations between key abiotic factors and the onset of reproduction in the population monitored. Many experimental studies and field observations have shown that light, temperature or their combination play a key role in triggering the onset of fertility in temperate seaweeds  (Liu et al. , 2017 and references therein). A series of papers, predominantly addressing fertility in red and brown algae under laboratory conditions, highlighted the importance of photoperiod (daylength) and temperature as the most important environmental factors controlling reproduction in most seaweeds  (Liu et al.
l. could potentially impact the functioning of marine Mediterranean shallow rocky bottoms. MHW will also have disruptive effects if they take place shortly after a reproductive period, when numerous juvenile individuals are present. Germlings and juvenile thalli are typically the most sensitive stages in the life history of these seaweeds and are impacted by environmental disturbances much more heavily than adult specimens  (de Caralt et al. , 2020; Verdura et al. , 2021). Temperature can be measured using a portable temperature sensor or, if possible, a logger fixed in situ providing measurements at short time intervals (such as HOBO data loggers). Although not strictly necessary, data for additional variables such as light irradiance, salinity, nutrients and sediment load are also desirable.
These will better define the environmental conditions in which gametangia reach maturation. We stress, however, that data of environmental variables collected in previous years should not be used as an absolute reference to guide restoration actions. Year-toyear variation in reproductive potential attributed to thermal anomalies have been documented: as an example,  Bevilacqua et al. (2019)  reported that in 2019 a population of Gongolaria barbata of the Gulf of Trieste became reproductive three months earlier than expected; the same population did not reproduce at all in the two subsequent years. A successful restoration action requires accurate knowledge of the reproductive phenology of the donor population shortly before the action is planned. We recommend that the protocol presented here should become a tool of widespread use for this purpose.
Funding Financial support has been provided by the  EASME-EMFF  ( Sustainable Blue Economy ) Project  AFRIMED  (http://afrimedproject. eu/, grant agreement  N. 789059 ), by  Biodiversa+ , the  European Biodiversity Partnership  under the  2021-2022 BiodivProtect joint  call for research proposals, co-funded by the  European Commission  ( GA N°101052342 ) and with the funding organisations,  Agencia Estatal de Investigación   AEI / 10. 13039/501100011033/ NextGenerationEU/PRTR  ( PCI2022-135070-2 ,  PCI2022-135052-2 ) and the  Spanish Ministry of Science and Innovation  ( FoRestA Grant/Award  No. PID2020-112985GB-I00 ). SB, RD, and FR acknowledge support from the  National Biodiversity Future Center of Italy , funded by the  European Community  under the  National Recovery and Resilience Plan (NRRP) for Italy.
Access to data is essential for farmers to make use of it and steer their activities. Only one method (TAPE) referred to a storage system, a 'global database', for future use of the data  (FAO, 2018; Mottet et al. , 2020). The issues of sovereignty and access to data in a usable form to support the agroecological transition steering in the medium and long term need to be addressed in the future development of assessment methods. Complementarity between decision-support methods originally designed to be used alone may exist  (Cerf & Meynard, 2006). A smart combination of existing methods can make sustainability assessment more profound and can broaden the insights of different end-user groups  (Van Passel & Meul, 2012). Considering the existing trade-offs between the evaluation purpose, the time requirement and the level of participation, we have highlighted above some suggestions on the approaches that could be built upon to consider each of the five evaluation criteria.
These species of agronomical interest have been bred to improve specific traits, and therefore constitute good model systems to understand the genetic processes of domestication  [7]. Petunia is a famous bedding plant with high agronomic value, and hundreds of floral varieties selected over the years provide a large repertoire for variation in flower morphology. In particular, petunia has been instrumental to decipher the anthocyanin biosynthesis pathway involved in petal pigmentation  [8]  and evolution of flower colour in relation to changes in pollinators  [9]. Moreover, a large transposon-insertion database allows for reverse genetics to be carried out easily, which distinguishes it from the other model Solanaceae species  [10, 11]. In tobacco, the famous Maryland Mammoth short-days flowering mutant plant has revealed the role of photoperiodism on the floral transition  [12, 13] , but afterwards tobacco has been little used for research on flower development  [6].
Advice from multiple stakeholders is required to design the optimal pediatric clinical trial. We present recommendations for acquiring advice from trial experts and patients/caregivers, derived from advice meetings that were performed through a collaboration of the Collaborative Network for European Clinical Trials for Children (c4c) and the European Patient-CEntric ClinicAl TRial PLatforms (EU-PEARL). Three advice meetings were performed: (1) an advice meeting for clinical and methodology experts, (2) an advice meeting for patients/ caregivers, and (3) a combined meeting with both experts and patients/caregivers. Trial experts were recruited from c4c database. Patients/caregivers were recruited through a patient organization. Participants were asked to provide input on a trial protocol, including endpoints, outcomes, and the assessment schedule.