Valid Information Research Articles

The impact of blockchain on restricting the misuse of green loans in a capital-constrained supply chain

Nowadays, green loans have been widely promoted to support the capital-constrained supply chain under carbon regulation. However, the borrowers in the supply chain may use the preferential green loans for other purposes which are not related to green development. That phenomenon is mainly attributed to the lack of valid information investigation under traditional information technology. Blockchain, as an innovative technology, can provide valid financial information and identify liability in the use of green loans, which could be an effective tool to restrict the misuse behavior. But will the high operating cost weaken the advantage of blockchain? To address this question, we consider a supply chain system composed of a supplier and a capital-constrained manufacturer. The manufacturer may misuse part of the green loans to purchase primary components from the supplier. Two operational modes corresponding to no-blockchain adoption (N case) and blockchain adoption (B case) are designed, in which the supplier determines the wholesale price, and the manufacturer determines the order quantity under green loans. By comparing the optimal decisions of supply chain members, we put forward and differentiate three types of effects: the carbon regulation effect, the information verification effect, and the blockchain cost effect, which influence the adoption of blockchain technology in the supply chain. In addition, we show that blockchain can restrict the misused amount of green loans and improve the environmental performance, but decrease the consumer surplus.

An Uncertainty Model for Strain Gages Using Monte Carlo Methodology.

For the purpose of validation and identification of mechanical systems, measurements are indispensable. However, they require knowledge of the inherent uncertainty to provide valid information. This paper describes a method on how to evaluate uncertainties in strain measurement using electric strain gages for practical engineering applications. Therefore, a basic model of the measurement is deduced that comprises the main influence factors and their uncertainties. This is performed using the example of a project dealing with strain measurement on the concrete surface of a large-span road bridge under static loading. Special attention is given to the statistical modeling of the inputs, the underlying physical relationship, and the incorporation and the impact of nonlinearities for different environmental conditions and strain levels. In this regard, also experiments were conducted to quantify the influence of misalignment of the gages. The methodological approach used is Monte Carlo simulation. A subsequent variance-based sensitivity analysis reveals the degree of nonlinearity in the relationship and the importance of the different factors to the resulting probability distribution. The developed scheme requires a minimum of expert knowledge of the analytical derivation of measurement uncertainties and can easily be modified for differing requirements and purposes.

Three-dimensional electric-field vector resistivity imaging for deep subsurface fractures network in heterogeneous crystalline rocks

SUMMARY Crystalline rocks, exposed in different parts of the world and over about one-third of India, have complex aquifer systems, which pose a challenge to mapping groundwater dynamics. Electrical resistivity tomography in quasi-3-D and electrical logging of some borewells carried out in an experimental ‘hydrogeological park’ in southern India, which has numerous boreholes and other geophysical information for validation, were unable to map the existence of deeper bedrock fractures and their connectivity. We have attempted electric-field vector resistivity imaging (EVRI), a new tool, to resolve the possible fracture-induced deep interconnectivity in a hard rock aquifer system. In the experiment, multiple two-orthonormal-channels independent receiver nodes for potential measurements are deployed and illuminated with several current injections between ∼ 0.9–3.7 A in full 3-D fashion, which allowed for improved mapping of resistivity variation than earlier approaches. The EVRI-derived full 3-D model shows the presence of fractures for depths between 20 and 70 m with substantial resistivity variations, supported by some borewells hydraulic investigations. It has also enhanced lateral resolution for depths > 30 m and almost doubled the depth of investigation than earlier electrical models. EVRI results revealed unweathered/unfractured granitic rock with no significant signature of fractures beyond 70 m depth that corroborates with existing borehole logs and hydrogeological conceptual model. Therefore, this study demonstrates the potential of EVRI for 3-D mapping of heterogeneous crystalline rocks, which would greatly help in groundwater management.

40 Performance validity in a presurgical epilepsy population

Objective:To examine whether suboptimal performance as determined by formal validity testing would predict neurocognitive scores in a sample of 83 pre-surgical, non-litigating epilepsy patients.Participants and Methods:Participants were 83 patients who underwent comprehensive outpatient neuropsychological testing as part as their evaluation as epilepsy surgery candidates. The sample consisted of 41 females and 42 males, with 72 patients identifying as White, 5 as Black, 2 as Hispanic, 1 as Asian, and 2 as other. Mean age was 36 (SD=12.4) mean FSIQ was 87 (SD=12.7), mean years of education of 12.9 (SD=2.1). Each patient’s assessment included a stand-alone performance validity test (PVTs)— Word Memory Test (WMT), the Test of Memory Malingering (TOMM), or the Medical Symptom Validity Test (MSVT)—as well as two embedded measures of validity—the California Verbal Learning Test Forced Choice (CVLT FC) and WAIS-IV Reliable Digit Span (RDS). Pass/fail rates were analyzed, with valid performance being defined as pass score on at least two of the completed PVTs (N=73 Pass Effort group 86.9%; N=10 Failed Effort group 11.9%). Point-biserial Pearson correlations were conducted to determine the relationship between validity pass/fail status and WAIS-IV FSIQ, VCI, and PRI scores, CVLT-II Trials 1-5 Total T scores, CVLT-II Long Delay Free Recall z scores, WMS-III Logical Memory II T scores, BVMT Total Recall T scores, BVMT Delayed Recall T scores, and Trail Making Test (TMT) B T scores.Results:Significant relationships were found between Failed Effort group and all neurocognitive scores except BVMT Total Recall. On average, the Failed Effort group obtained significantly lower FSIQ (M=76.57, SD=10.94), VCI (M=80.89, SD=16.03), PRI (M=81.00, SD=14.91), CVLT-II Trials 1-5 Total (M =34, SD=6.89), CVLT-II Long Delay Free Recall (M =-2.44, SD=1.43), WMS-IV Logical Memory II (M =4.83, SD=2.79), BVMT Delayed Recall (M=26.38, SD=6.41), and TMT B (M=29.70, SD=11.46) standard scores compared to the Pass Effort group (FSIQ M=88.09, SD=12.52; VCI M=92.13, SD=13.61; PRI M=91.14, SD=12.06; CVLT-II Trials 1-5 Total M=47.86, SD=12.02; CVLT- II Long Delay Free Recall M=-.44, SD=1.11; WMS-III Logical Memory II M=8.41, SD=3.17; BVMT Delayed Recall M=39.19, SD=12.66; TMT B M=39.34, SD=13.18). Correlation coefficients were r=-.266* (FSIQ), r=-.255* (VCI), r=-.271* (PRI), r=.361**(CVLT-II Total), r=-.474** (CVLT-II LDFR), r=-.298** (WMS-IV LM II), r=-.308** (BVMT DR), and r=-.240* (TMTB). All coefficients were significant at the .05 (*) or .01 (**) level.Conclusions:Results suggest that pass/fail status on formal validity testing predicts depressed performance on a variety of neurocognitive measures. Therefore, predicting surgical outcome of resection/ablation (e.g., compensation of contralateral hemisphere) should not be based upon neuropsychological memory performance alone when there are failures on tests of engagement as memory scores have strong correlations to pass/fail status on formal validity testing. Overall, this emphasizes the importance of routinely integrating PVTs as part of pre-epilepsy surgery neuropsychological evaluations.

Online cognitive assessment in the era of COVID-19: Examining the validity of the MEZURE.

Developed more than 2 decades ago, the MEZURE (Assessment Technologies, 1995-2020; https://www.mezure.com/) has received increased attention as a result of the COVID-19 pandemic. It is the first individualized test of cognitive ability created to use an online (local or remote) assessment modality. The MEZURE claims to be aligned both with the extended Gf-Gc theory and the Cattell-Horn-Carroll model of abilities. Whereas the test publisher claims it used exploratory factor analysis to investigate the instrument's factor structure, only the subtest factor loadings on the Gf-Gc factors were furnished. No other structural validity information was provided, suggesting that users of the instrument should interpret the scores produced by the MEZURE with caution. Accordingly, the present study used exploratory and confirmatory factor analysis to more fully investigate the structural validity of the MEZURE. The results revealed that the MEZURE contains a combined perceptual reasoning (i.e., [Gf/Gv]/working memory [Gwm]) group factor, a verbal ability group factor, and a relatively weak general factor that is dominated by perceptual reasoning. The finding of a paltry general factor that is weakly loaded by verbal subtests is inconsistent with the broader research on traditional cognitive ability assessment and could be related to the online administration format of the test. Future research is required to better understand this finding. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Lessons learned from behaviors of the employees in Iranian banks during the Covid-19 pandemic crisis: A phenomenological study

The aim of this research is to analyze the experiences of employees during the Covid-19 pandemic cri-sis and conceptualize the lessons learned to deal with similar crises in the future. To achieve this, a qualitative approach with a phenomenological strategy was conducted through semi-structured, in-depth interviews with 16 front-line employees of Bank Melli, the largest state bank in Iran. The study found that employee behavior plays a critical role in crisis management, with functional behaviors such as preventive measures, stress management, and sharing valid information being essential. Dysfunctional behaviors such as resistance to change and inconsistent behaviors were also identified. The prevention of infection and stress management were identified as key behaviors to cope with the crisis, while media literacy and acceptance of change were crucial for correct information analysis and crisis management. Overall, the study highlights the challenges faced by employees during the Covid-19 crisis and provides valuable lessons from a behavioral perspective for managers in the banking industry to manage disruptions caused by current and future pandemics. The study emphasizes the importance of learning from the lessons of the Covid-19 crisis to reduce negative effects on physical and mental health.

Structured serialization semantic transfer network for unsupervised cross-domain recognition and retrieval

Unsupervised domain adaptation aims to apply a model trained from labeled datasets to unlabeled datasets, which still exist challenges to minimize the degree of inter-domain sample differences. In the paper, we design the structured serialization semantic transfer network (S3TN) to further minimize the domain gap by structured and serialization information at the semantic level. Our S3TN contains the spatially structured alignment module (SSA) and the category serialization alignment module (CSA). The spatially structured alignment module preserves the structured information between different category-level centers (spatial associations between different categories in the feature space of category-level centers), further aligning the structured correlations between different domains. The serialization alignment module enhances the reliability of the category-level centers by avoiding valid information loss and error information retention, which further mitigates the problem of knowledge forgetting. Various experiments have been conducted on three widely-used datasets, Office-31, MI3DOR-1, and MI3DOR-2, and the experimental results verify the effectiveness of our proposed method. Especially on Office-31 dataset, our method outperforms the existing methods with gains of 1.3% for the average of the classification accuracy.

Regional Tourism Development in Pringsewu Regency: Perspective on Community-Based Tourism and Sustainable Livelihood

Institutionally, the Tourism Awareness Group (Pokdarwis) has encouraged community participation in each village (pekon) to optimize the potential of natural, cultural, and human resources as tourist attractions. This study aims to describe in-depth the dynamics of the collaboration of the Tourism Awareness Group in Pringsewu Regency to increase public awareness in each village about the benefits of the tourism sector for the sustainability of household livelihoods, preservation of social-cultural values, and environmental sustainability. This study uses a community-based tourism perspective. Meanwhile, the qualitative research method with a case study approach is used for three tourist objects developed by Pokdarwis, namely Akasia valley, Talang Indah, and Teluk Kenyo. Retrieval of research data is conducted using in-depth interviews, observation, and document study while, data processing uses triangulation techniques to obtain valid and credible information. This study indicates that Pokdarwis in each village (pekon) are organized because the Pokdarwis Communication Forum is the central organization that connects 37 Tourism Awareness Groups throughout the Pringsewu Regency, Lampung Province. Proactively, Forkom Pokdarwis establishes communication with the government, local entrepreneurs, academics, and rural communities to optimize natural, cultural, and human resources by pioneering tourist destinations. Several tourist destinations have successfully pioneered and developed through reasonable assistance and coordination, opening up local communities' job opportunities.

Evaluation of nurses' knowledge and performance regarding preparation and injection of intravenous drugs in pediatric wards in Iran

BackgroundA error in intravenous injection in pediatric wards can cause irreparable injuries. This study aimed to determine the level of knowledge and performance of nurses in terms of preparation and injection of intravenous drugs in pediatric wards of hospitals affiliated to Isfahan University of Medical Sciences.MethodsThis cross-sectional study was conducted in 2022 on 156 nurses working in pediatric wards. The data was collected with demographic information questionnaire and the knowledge and performance of the participants were determined using a researcher-made questionnaire, including the five rights of medication administration (preparation and injection, medication error, drug side effects, family empowerment, and documentation) using self-reporting and observation methods. Formal and content validity was calculated using the opinions of 10 experts and Cronbach's alpha with 40 samples.ResultsThe mean and standard deviation of total nurses' knowledge and performance scores were 58.31 + 10.1 and 66.1 + 14.4, respectively. Moreover, the mean and standard deviation of nurses' knowledge scores were 63.55 + 14.3 for documentation, 46.1 + 7.9 for preparation and injection, 73.9 + 12.3 for drug side effects, 58.4 + 10.2 for medication error, and 69.4 + 9.4 for family empowerment. Besides, the mean performance was 69.1 + 17.6 for documentation, 61.3 ± 9.9 for preparation and injection, 78.21 + 12 for drug side effects, 58.6 + 15 for medication error, and 65.4 + 17.7 for family empowerment.ConclusionThe results showed that the mean knowledge and pharmacological performance of nurses working in pediatric wards in different areas of the principles of medicine were not at the desired level, and this can affect children adversely.

Confident learning-based Gaussian mixture model for leakage detection in water distribution networks

Leakage detection in the water distribution system not only helps to reduce water waste but also decreases the risk of drinking water pollution. To reduce reliance on hardware devices and enable real-time detection, the water utilities are transitioning towards the data-driven based approach that relies on the analysis of the flow and pressure data collected from the supervisory control and data acquisition (SCADA) system. Due to the lack of leakage data, most of these methods are unsupervised methods that rely heavily on assumptions about the distribution of anomalies; whereas, the water utility's repair records contain much valid information about the leakage and normal characteristics. To convert this information into available labels and to address the lack of leakage data, this paper proposed a new leakage detection framework to infer the pressure characteristics under normal conditions based on historical data by combining a label cleaning method—confident learning (CL)—with an unsupervised method—Gaussian mixture model (GMM)—for leak detection. The methodology is validated with synthetic and real measured data. Comparisons with four unsupervised methods demonstrate that the GMM method has superior identification of leak features from pressure data. For a real-world water distribution system in K city containing 91 pressure sensors, the average true positive rate is 0.78 and the average false positive rate is 0.11. This methodology is a promising tool to identify signs of leakage in large-scale water distribution networks (WDNs).

Self-reports by persons with multiple sclerosis are an adequate surrogate for healthcare provider data on disease-modifying therapy and multiple sclerosis type

IntroductionSelf-reports are a valuable and cost-effective method of data collection, though they can be influenced by bias. Limited evidence exists on the quality of self-reports by persons with multiple sclerosis (pwMS), particularly since more potent disease-modifying therapies (DMTs) have been introduced. This study aimed to assess the reliability and validity of self-reported DMT use and multiple sclerosis (MS) type in the Swiss Multiple Sclerosis Registry (SMSR) by comparing self-reports with reimbursement approval requests from the Swiss Association for Joint Tasks of Health Insurers. MethodsThe self-reported and reimbursement approval data were linked using privacy-preserving methods based on information available in both databases, i.e., date of birth, canton of residence, sex, and year of MS diagnosis. The SMSR baseline questionnaire data was utilized for the main analysis, while the SMSR follow-up survey data was utilized for the sensitivity analysis. For both analyses, we compared self-reported data with reimbursement approval data that corresponded to the respective periods of the SMSR data collection. Thus, the main analysis included the entirety of the data over the six-year period, while the sensitivity analysis captured a more recent snapshot of the data. To assess reliability, we estimated agreement using Cohen's kappa, and for validity, we estimated accuracy parameters using reimbursement approvals as the reference standard. Univariable and multivariable logistic regression models were employed to investigate factors associated with discordance between self-reports and reimbursement approvals in the main analysis. ResultsThe main analysis included 446 participants, and the sensitivity analysis included 193 participants. The agreement between self-reported and reimbursement approval data for medication use was near-perfect in both analyses (κ = 0.87, 95% confidence interval (CI) 0.85, 0.90 and κ = 0.82, 95% CI 0.76, 0.88). However, the agreement between self-reported and reimbursement approval-documented MS types ranged from fair to moderate (κ = 0.37, 95% CI 0.25, 0.48 to κ = 0.61, 95% CI 0.46, 0.77). The accuracy estimates for self-reported DMT use were generally high (≥ 0.80) with narrow CIs, except for less frequently reported drugs. While the sensitivity and specificity for RRMS were high, there was a notable possibility of false-negative self-reports for RRMS (NPV = 0.33, 95% CI 0.22, 0.45), and false-positive reports for SPMS (PPV = 0.36, 95% CI 0.21, 0.54). Multivariable logistic regression models showed that age (OR = 1.07, 95% CI 1.04, 1.10 per year) and education level (OR = 0.27, 95% CI 0.11, 0.65) were associated with discordance in reported and documented MS types, whereas possession of Swiss citizenship (OR = 0.32, 95% CI 0.14, 0.72) was associated with discordance in DMT use. ConclusionSelf-reported DMT use in pwMS is a reliable and valid information source, with near-perfect agreement and high accuracy. Self-reported MS types showed fair to moderate agreement and varying accuracy, likely reflecting the complexity of diagnosing progressive forms of MS and access to DMTs. In population-based MS research, self-reports of MS types, and particularly DMT use, can serve as a suitable surrogate for healthcare provider data.

DSW-YOLO: A detection method for ground-planted strawberry fruits under different occlusion levels

The problems of stem and leaf shading, fruit overlapping, and different ripening stages pose a great challenge to picking robots, which are usual for the ground-planted strawberries in field. To achieve accurate detection of ripe strawberries and their occlusion levels, a DSW-YOLO (DCNv3-SA-WIoU-YOLO) network model is proposed based on the YOLOv7 network model. The model is designed with a DCN-ELAN module by introducing DCNv3 (Deformable Convolution v3) in the ELAN module to improve the feature extraction capability of the network for non-regular targets. Meanwhile, the SA (Shuffle Attention) mechanism is introduced at the end of the backbone network to enhance the extraction of valid information and improve detection accuracy. Optimizing the bounding box loss function to WIoU v3 (Wise-IoU v3) accelerates the convergence speed of the network. The proposed network model was tested using the ground-planted strawberry dataset, and the experimental results show that the P, R, and mAP@.5 of the improved network model are 82.8 %, 82.1 %, and 86.7 %, respectively, which are improved by 5.0 %, 1.7 %, and 2.2 %, respectively, compared with the YOLOv7 model. By comparing with other classical target detection algorithms, it is verified that the proposed model has advantages in terms of accuracy and model size. Finally, the DSW-YOLO model was deployed on Jetson Xavier NX of a strawberry picking robot, and visual recognition tests were conducted in field. The detection time of a single image is 92 ms, which satisfies the speed requirement of robot picking, and the detection results verify that the DSW-YOLO network model can quickly and accurately detect ripe strawberry fruits under different occlusion levels in a complex field environment, which provides a theoretical basis for the picking robot to achieve accurate positioning and efficient picking.

Enhanced Physical Layer Security and PAPR Performance Based on Disturbance of Data Cluster Under Chaotic Sequence Color Seeking Mechanism in CO- OFDM System

This study proposes a physical layer encryption scheme based on disturbance of data cluster under color seeking mechanism of chaotic sequence which is generated by Brownian motion determined by keys aiming to improve security of coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. Chaotic sequences with good randomness and unpredictability are endowed with a unique color composed of two primary colors and go through order transformation by sorting and seeking, which forms the chaotic sequence color seeking mechanism. The disturbance of transmitted data is realized according to chaotic sequence color seeking mechanism and PAPR performance is optimized simultaneously. The security of system is enhanced due to a key space of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">90</sup> and PAPR can be reduced by 0.5 dB. An experiment conducted in a 40 GHz 16QAM CO-OFDM system over an 80 km standard single mode fiber (SSMF) shows that the authorized user can successfully decrypt the received signal, while the eavesdroppers cannot derive valid information with bit error rate (BER) at approximately 0.5. Analyses of peak to average power ratio (PAPR) performance and time complexity of the scheme are further carried out under different cluster forming methods to derive the optimized configuration which is critical in encryption.

MWDINet: A multilevel wavelet decomposition interaction network for stock price prediction

Stock price prediction is a classical interdisciplinary issue drawn from finance, computer science, econometrics, and mathematics. Most stock price data are nonlinear, nonstationary, and highly complex, making stock price prediction challenging. Recently, deep neural networks (DNNs) have demonstrated powerful learning capabilities and have yielded notable results in stock price prediction tasks. Most existing deep learning solutions, however, only consider time-domain information or lack effective modeling of frequency-domain information, thus failing to effectively utilize both time-domain and frequency-domain information of the data. Meanwhile, existing methods ignore autocorrelated errors in the stock price forecasting task due to missing valid information data, i.e., they do not consider the correlation between the error at the current time step and the error at the previous time step, which undermines the standard maximum likelihood estimation (MLE) assumption, thereby weakening the model’s performance. We propose a multilevel wavelet decomposition interaction network (MWDINet), an end-to-end framework for stock price prediction. MWDINet employs the multiscale wavelet decomposition interaction module (MWDI-Block) and the Hull Moving Average module (HMA-Block) to extract the data’s frequency-domain and time-domain information, respectively. In MWDI-Block, the traditional signal processing method of Maximum Overlapping Discrete Wavelet Transform (MODWT) is seamlessly embedded into a deep learning framework (named DMODWT). The DMODWT algorithm not only automatically extracts the frequency-domain information from the data, but also fine-tunes the wavelet filters. With HMA-Block, we improve the Hull Moving Average (HMA), commonly used in the industry, into a deep learning module, which learns how changes in different markets over time. Inspired by the research on correcting autocorrelated errors in linear models in econometrics, we further design a deep difference module (DIF-Block) to correct autocorrelated errors and thus improve the prediction performance of the model. Moreover, all components are integrated seamlessly in a unified end-to-end framework. Extensive experiments on real-world datasets demonstrate that MWDINet outperforms the state-of-the-art models and has remarkable potential in stock price prediction.

The role of visual crowding in eye movements during reading: Effects of text spacing.

Visual crowding, generally defined as the deleterious influence of clutter on visual discrimination, is a form of inhibitory interaction between nearby objects. While the role of crowding in reading has been established in psychophysics research using rapid serial visual presentation (RSVP) paradigms, how crowding affects additional processes involved in natural reading, including parafoveal processing and saccade targeting, remains unclear. The current study investigated crowding effects on reading via two eye-tracking experiments. Experiment 1 was a sentence-reading experiment incorporating an eye-contingent boundary change in which reader's parafoveal processing was quantified through comparing reading times after valid or invalid information was presented in the parafovea. Letter spacing was jointly manipulated to compare how crowding affects parafoveal processing. Experiment 2 was a passage-reading experiment with a line spacing manipulation. In addition to replicating previously observed letter spacing effects on global reading parameters (i.e., more but shorter fixations with wider spacing), Experiment 1 found an interaction between preview validity and letter spacing indicating that the efficiency of parafoveal processing was constrained by crowding and visual acuity. Experiment 2 found reliable but subtle influences of line spacing. Participants had shorter fixation durations, higher skipping probabilities, and less accurate return sweeps when line spacing was increased. In addition to extending the literature on the role of crowding to reading in ecologically valid scenarios, the current results inform future research on characterizing the influence of crowding in natural reading and comparing effects of crowding across reader populations.

Validating the Engineering Fields Questionnaire for Use With African American Science, Technology, Engineering, and Mathematics Students

Lent’s (2003) Engineering Fields Questionnaire assesses major components of Social Cognitive Career Theory (SCCT) and includes measures of self-efficacy, coping efficacy, outcome expectations, technical interests, and educational goals. These measures have demonstrated good internal consistency in previous research, but validation information is more limited for use with African American STEM students. This paper discusses a validation study of an adapted version of Lent’s Engineering Fields Questionnaire, entitled the STEM Fields Questionnaire, with a sample of African American undergraduate STEM students ( n = 526). Validating the STEM Fields Questionnaire for African Americans is particularly important given the role of cultural values and certain experiences in career development among this population. Seven factors resulted from an exploratory factor analysis conducted in the present study: engineering/technology interests, outcome expectations, self-efficacy, STEM coping, goals, bio-chemical sciences interest/self-efficacy, and mathematics interest/self-efficacy, with four of six original subscales represented. Implications for research and practice were discussed.

Medication Safety in Breastfeeding Mothers. The Current Situation in the Greek National Healthcare System

Mothers during lactation may encounter barriers including the need of medication intake due to a chronic or acute illness, which may affect mainly the duration of breastfeeding. In these cases, the issue of medication safety during lactation arises and maternal decisions usually depend on professional counseling. Providing scientifically valid information on the benefits of breastfeeding and the risk of exposure of the newborn/infant to medicines through breast milk is a responsibility of healthcare professionals. Reliable sources on the compatibility of medicines with breastfeeding vary and the information among them is conflicting or even insufficient, creating as a consequence difficulty for healthcare professionals to provide counseling. Given the fact that breastfeeding mothers are excluded from clinical trials as they are considered a vulnerable population, the scientific community has turned to alternative methods based on pharmacokinetic population modeling for medication safety during lactation. In addition, through pharmacovigilance science, databases related to drug compatibility with breastfeeding can be enriched with new data. Undoubtedly, the contribution of midwives and other healthcare professionals involved in breastfeeding plays a key role in the pharmacovigilance process through the observation and reporting of side effects. In the Greek national healthcare system, telephone helplines have been established to provide information on medication safety during lactation. Finally, information is provided on the guiding principles for midwives and other healthcare professionals regarding medication safety in breastfeeding mothers. The ultimate goal is to support and prolong breastfeeding with evidence-based knowledge.

A fast and distributed C4.5 algorithm for urban big data

The amount of information nowadays is rapidly growing. Aside from valuable information, information that is unrelated to a target or is meaningless is also growing. Big data and broader digital technologies are considered the primary components of smart city governance and planning. Big data analysis is considered to define a new era in urban planning, research, and policy. Effective data mining and pattern detection techniques are becoming very important these days. Processing such a large amount of data entails the use of data mining, a technique that clarifies the association between valid information and excludes irrelevant data to implement a practical decision tree. A large amount of data affects processing time and I/O costs during data mining. This study proposes to distribute data among multiple clients and distribute a large amount of data computation equally to improve the resource cost problem of exploration. Following that, the main server consolidates the computation results and generates the survey results. Experiment results show that the proposed algorithm is superior, thus allowing a larger amount of data to be processed while producing high-quality results.

Digitization of weather records ofSeungjeongwon Ilgi: A historical weather dynamics dataset of the Korean Peninsula in 1623–1910

AbstractHistorical weather records from Europe indicate that the Earth experienced substantial climate variability, which caused, for instance, the Little Ice Age and the global crisis in the period between the 14th and 19th centuries. However, it is still unclear how global this climate variability was because of the scarce meteorological data availability in other regions including East Asia, especially around the 17th century. In this context,Seungjeongwon Ilgi, a daily record of the Royal Secretariat of the Joseon Dynasty of Korea, is a precious source of historical meteorological records for the Korean Peninsula, as it covers 288 years of weather observations made during 1623–1910. We used the digital database ofSeungjeongwon Ilgito construct a machine‐readable weather condition dataset. To this end, we extracted valid weather information from the original weather description text and compiled them into pre‐defined weather categories. Additionally, we attempted to improve the usability of dataset by converting the reported dates in the traditional calendar system to those in the Gregorian calendar. Finally, we outlined promising implications of this dataset for meteorological and climatological studies, while describing the limitations of the dataset. Overall, future studies focusing on the climate and weather of the past could use this meteorological database for investigating long‐term climate variability. Our datasets are publicly available at10.5281/zenodo.8382243.

Multianalyte analysis of volatile compounds in virgin olive oils using SPME‐GC with FID or MS detection: results of an international interlaboratory validation

AbstractThe organoleptic assessment (Panel test) is the only procedure within the official methods for determining the quality of virgin olive oils that involves an expert panel. There is an urgent need for analytical methodology that can reliably measure volatile compounds in virgin olive oils that is capable of supporting and anticipating the official Panel test. For this reason, a new method based on solid‐phase microextraction–gas chromatography with the choice of two possible detectors (FID or MS) was subjected to a large international interlaboratory validation study. The study involved a two‐stage process: first, a pretrial phase in which 7 participants were exposed to the method for the first time to identify any initial problems with the methodology; then, a formal validation stage (trial proper), which involved 20 laboratories from Europe, USA, Japan and China. The performance of the different detectors was investigated. While both methods have advantages, the method using FID provided better results for 11 compounds, in terms of reproducibility, compared to MS. This information will allow to implement the method with accurate information of the method performance depending on the detector used.Practical applications: This study provides information from an interlaboratory validation of a method for measuring volatile compounds in virgin olive oils conducted with laboratories (from industry and academia) working in the olive oil sector. The information on the expected analytical errors in the determination of each volatile compound is necessary to apply this method for supporting the official Panel test (sensory analysis). The SPME‐GC‐MS/FID methods proposed in this work can be used for the internal quality control of a company/distributor/quality control laboratory and could also be used in cases of difficult/contradictory organoleptic assessment, or to confirm results from sensory panels in cases of disputes/disagreement (Reg. EU 2022/2105).