Consistent Data Research Articles

SISTEM LAPORAN PERILAKU SANTRI PONDOK PESANTREN AL-IHSAN BOARDING SCHOOL BERBASIS WEBSITE

Education is a conscious and planned effort to form a learning atmosphere and learning process that is able to activate the potential of students in developing spiritual dimensions, religion, self-control, personality, intelligence, noble character, and skills needed for the benefit of self, society, nation and state. Although formal education such as elementary school to tertiary education has become a common form of education, non-formal education such as the Al-Ihsan Boarding School Islamic Boarding School also has an important role in shaping the character and values of the students. Currently, reporting of violations of students' behavior is done manually by recording them in a report book which results in several problems, such as inconsistencies in violation data, and non transparency in providing information to parents of students. To overcome this problem, a website-based student behavior reporting system is needed with the aim of building a website-based student behavior reporting system that is capable of managing student behavior data accurately, as well as providing notifications via the whatsapp application to parents of students so that parents can easily receive and monitor behavior and violations committed by their children. The research flow method used is as a reference for conducting research in order to have structured and systematic results by covering several stages by identifying problems, studying literature, designing systems, developing systems, testing systems, implementing systems and evaluating systems. Thus, the results of this study are that a web-based student behavior reporting system can contribute and has great potential to increase efficiency, accuracy, and accessibility in managing student behavior data and this will help the Al-Ihsan Boarding School Islamic Boarding School in managing and monitoring behavior. students effectively.

Discussion on “Speed optimization for maximizing the ship's economic benefits considering the Carbon Intensity Indicator (CII) ” by R Hua, J Yin, S Wang, Y Han, and X Wang (https://doi.org/10.1016/j.oceaneng.2024.116712)

The inconsistencies in the optimization data presented in the original paper by Hua et al. (2024) (hereafter identified as “the original paper”, or, “the original authors”) will be thoroughly examined in this study. The necessary parameters in the optimization model will be identified from a data consistency standpoint, and any insufficiencies in the original conclusions will be highlighted. An optimization model with consistently reliable data will be developed, based on the available information in the original paper. An illustrated example is solved by a rigorous meta-heuristic method. The original paper could potentially enhance its clarity by providing a more detailed explanation of the optimization model's parameters to further reinforce its findings and conclusions. This not only adds credibility to the study but also enables readers to replicate the analysis and validate the outcomes.

Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level

Evapotranspiration (ET), net ecosystem productivity (NEP), and ecosystem water use efficiency (EWUE) of forests are changing due to climate change. Traditional models using coarse-scale climate reanalysis data fail to capture local meteorological and hydrological conditions accurately. This study combines in situ meteorological observations, remote sensing, and advanced datasets (forest age, rooting depth, soil moisture) to estimate ET, NEP, and EWUE at forest meteorological stations via machine learning. About 60.6 % of stations showed a decrease in NEP from 2003 to 2010 to 2011–2019, while 63.9 % showed an increase in ET, and 58.9 % showed a decrease in EWUE. NEP and EWUE significantly declined in forests older than 60 years, with younger forests exhibiting higher NEP. EWUE in different forest types is driven by varying mechanisms, with DBF sites influenced by VPD and ENF sites by RSDN. EWUE of regions with inconsistent VPD data between site and reanalysis, such as northwestern North America, showed divergences from previous reanalysis-based studies but aligned more with atmospheric inversion findings. Slight summer VPD increases boosted NEP, especially in high-latitude areas, while early spring phenology and increased spring VPD reduced summer water availability. Incorporating more site-specific observations, such as plant traits, could enhance understanding of climate-plant-ecosystem relationships. This study underscores the potential of meteorological station-level data to improve forest carbon and water flux dynamics understanding, aiding forest management for carbon neutrality and climate adaptation.

Supplementing systematic review findings with healthcare system data: pilot projects from the Agency for Healthcare Research and Quality Evidence-based Practice Center program

ObjectivesThe US Agency for Healthcare Research and Quality, through the Evidence-based Practice Center (EPC) Program, aims to provide health system decision makers with the highest-quality evidence to inform clinical decisions. However, limitations in the literature may lead to inconclusive findings in EPC systematic reviews (SRs). The EPC Program conducted pilot projects to understand the feasibility, benefits, and challenges of utilizing health system data to augment SR findings to support confidence in healthcare decision-making based on real-world experiences. Study Design and SettingThree contractors (each an EPC located at a different health system) selected a recently completed SR conducted by their center and identified an evidence gap that electronic health record (EHR) data might address. All pilot project topics addressed clinical questions as opposed to care delivery, care organization, or care disparities topics that are common in EPC reports. Topic areas addressed by each EPC included infantile epilepsy, migraine, and hip fracture. EPCs also tracked additional resources needed to conduct supplemental analyses. The workgroup met monthly in 2022-2023 to discuss challenges and lessons learned from the pilot projects. ResultsTwo supplemental data analyses filled an evidence gap identified in the SRs (raised certainty of evidence, improved applicability) and the third filled a health system knowledge gap. Project challenges fell under three themes: regulatory and logistical issues, data collection and analysis, and interpretation and presentation of findings. Limited ability to capture key clinical variables given inconsistent or missing data within the EHR was a major limitation. The workgroup found that conducting supplemental data analysis alongside an SR was feasible but adds considerable time and resources to the review process (estimated total hours to complete pilot projects ranged from 283 to 595 across EPCs), and that the increased effort and resources added limited incremental value. ConclusionSupplementing existing SRs with analyses of EHR data is resource intensive and requires specialized skillsets throughout the process. While using EHR data for research has immense potential to generate real-world evidence and fill knowledge gaps, these data may not yet be ready for routine use alongside SRs.

Evaluating the effectiveness of nicotine replacement therapy in critically ill smokers: A meta-analysis of randomized controlled trials.

The effectiveness of nicotine replacement therapy (NRT) in critically ill patients remains uncertain, as conflicting research results have been reported. Despite potential side effects and inconsistent data on safety and efficacy, NRT is still prescribed in intensive care units (ICUs) to prevent withdrawal symptoms and manage agitation in patients who are smokers. This meta-analysis aimed to assess the effectiveness of nicotine replacement therapy in critically ill smoking patients. A systematic review and meta-analysis of randomized controlled trials investigated the outcomes of smokers admitted to ICUs and were randomized either to receive or not receive nicotine replacement therapy (NRT) during their ICU stay. The MEDLINE and Embase databases were searched from inception through 13 February 2023 using OVID. The primary outcome was ICU length of stay (LOS) for this systematic review and meta-analysis. Meta-analysis was conducted using both random-effects and fixed-effect models; the latter is recommended when meta-analysis is restricted to just a few studies. The study was registered in the Prospective International Register of Systematic Reviews (PROSPERO) under reference number CRD42023407804. Of 28 studies initially identified, three, with 67 patients on NRT and 72 controls, were deemed eligible for pooled analysis. Patients who received NRT experienced a shorter LOS (mean difference, MD= -3.06; 95% CI: -5.88 - -0.25, p=0.0, I2=0%). The mechanical ventilation (MV) duration was also shorter in the NRT group, but this difference was not statistically significant (MD= -1.24; 95% CI: -3.21-0.72, p=0.22, I2=12.69%). Delirium duration was reported by two studies, from which pooled analysis revealed an MD of -0.50 (95% CI: -1.63-0.62, I2=0%). The vasopressor duration was assessed in two studies, and the overall MD for vasopressor duration was not statistically different between NRT patients and controls in the fixed-effects model (MD=0.11; 95% CI: -0.75-0.96, I2=0%). Critically ill smoker patients who received NRT experienced a significantly shorter ICU LOS but no significant differences in the durations of MV, vasopressor use, or delirium.

Are farmers’ minds really too elusive to capture? A novel evaluation framework for the knowledge transfer initiatives in agriculture

ABSTRACT Knowledge transfer initiatives aim to improve farmers’ knowledge and skills for tackling the growing economic, environmental and social challenges in agriculture. However, their evaluation is often inadequate due to institutional barriers and a lack of comprehensive methodology for assessing normative and behavioural shifts within this social group. To address this gap, we developed an extended evaluation framework for the knowledge transfer activities within the European Union's Common Agricultural Policy (CAP), including three indicators levels: output, result and impact. The indicators and survey instruments were based on the Theory of Planned Behaviour and piloted in a selected EU Member State for evaluating knowledge transfer measures in the programming period 2014–2022. The results indicate a promising potential of the proposed framework for capturing the multifaceted outcomes of knowledge transfer activities in agriculture. The usefulness and feasibility of the evaluation framework were further assessed through interviews with AKIS actors. They highlighted that evaluation activities at the Member State level may be hindered by inconsistent long-term data collection, insufficient response and involvement of farmers, deviations from the agreed protocols and a lack of dedicated resources. To ensure the uptake of the proposed evaluation framework, evaluation culture within the managing authorities and among stakeholders should be systematically fostered at the Member State level. This process can be enhanced by adopting a more elaborated CAP monitoring framework and methodology guidelines and by providing targeted training on evidence-driven policy-making.

Aplikasi Absensi Siswa Berbasis Android

Students at SMA Negeri 1 Central Bengkulu are still dominated by the manual method, namely taking student absences by recording them on paper which will later be summarized from each student's attendance data. Because the manual recap of student attendance data also affects the information about attendance recaps which is still limited to the school, so parents/guardians cannot monitor student attendance levels. Inconsistency in attendance data can be detrimental to students, there is often a difference between the absences listed in the attendance register book/record into the system, which is one of the factors that makes the original data and data that have been verified into the system different. Utilizing the location of an Android smartphone to become an attendance system, which functions to provide location information based on the user's nearest location, assisting in searching for locations based on 6 existing parameters, namely education (high school/vocational school/university), hospital, hotel, restaurant, tourist attraction , and malls/traditional markets. It is hoped that this application will always be up to date so that this application follows developments in Android-based lecturer/teacher attendance information technology using the Flutter framework.

Using non-randomized trials to assess the clinical benefit of systemic anti-cancer treatments: Viable or not?

BackgroundThe Dutch Committee for the Evaluation of Oncological Agents (cieBOM) assesses the clinical benefit of systemic anti-cancer treatments (SACTs). For SACTs tested in non-randomized trials (NRTs), cieBOM primarily utilizes response-related thresholds as assessment criteria. As sufficiency of NRT-based evidence for benefit assessments is questionable, this study investigated whether and how NRTs can be used to assess the clinical benefit of new SACTs initially appraised by cieBOM based on randomized controlled trials (RCTs). MethodsUsing the RCTs underpinning cieBOM recommendations issued between 2015 and 2017, we searched for matching NRTs and applied the NRT-related assessment criteria by cieBOM to them. We then compared the assessment outcomes to the respective RCT-based cieBOM recommendations. Further, we investigated how the assessments would change when applying different response-related thresholds and adding a progression-free survival (PFS) threshold. ResultsFor 13 of the 37 eligible recommendations, a matching NRT was found. Two treatments were assessed positively and six negatively; five treatments were non-assessable. Two positive recommendations matched a positive NRT-based assessment; one matching negative assessment was found, and one treatment could not be assessed based on either trial results. Adding a > 6 months PFS threshold decreased the number of non-assessable NRTs (five to two). ConclusionsLimited publications and inconsistent data reporting hampered the viability of NRTs for clinical benefit assessments of SACTs beyond the scope of rare indications. Further, response-related assessment criteria alone might not fully grasp the clinical benefit of novel SACTs. NRT-based assessments should be considered with caution due to uncertainty of the trial results.

A framework for fully automated reconstruction of semantic building model at urban-scale using textured LoD2 data

The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-dimensional (3D) city modeling, has been accessible for an extended period. However, its comprehensive implementation remains limited due to challenges such as insufficient automation and inconsistent data quality. This research introduces an innovative and fully automated framework aimed at urban-scale semantic building model reconstruction. The proposed framework addresses three critical challenges: (1) proposing facade layout graph model to formalize the geometry and topological relationships of semantic entities on building facades, thereby promoting the deduction of structural completeness and the reconstruction of semantic facade models; (2) establishing a mapping relationship between texture images, semantic entities, and building shells guided by the facade layout graph to ensure consistent correlations among the geometry, semantics, and topology of building models; (3) developing an efficient representation methodology for semantic building models utilizing a parameter set derived from the facade layout graph. The proposed framework has been successfully validated by reconstructing 8,681 buildings from three different locations in Berlin. The results demonstrate an outstanding reconstruction accuracy of 91%, with a time efficiency of only 3.42 s per building. Visual analysis further confirms that the framework effectively fulfills the application prerequisites of 3D GIS. The code of the proposed framework is available in the repository: https://github.com/wangyuefeng2017/LoD3Framework-.

TDSQL: Tencent Distributed Database System

Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency access. However, developing a distributed database system that is well-suited for diverse application scenarios, particularly for large-scale applications, presents several challenges. These challenges include ensuring data consistency and achieving high levels of performance. This paper presents TDSQL, a distributed database system that prioritizes core design principles of distributed systems, including high availability, strong consistency, and scalability. In particular, TDSQL has achieved high performance through over a decade of practical experience and optimization in various modules, such as the kernel, synchronous replication, and transaction processing, in large-scale application scenarios. By conducting the TPC-C benchmark test, TDSQL demonstrated outstanding performance, achieving a throughput of 814 million tpmC across 1650 database nodes, with a jitter rate of less than 0.2%. This jitter rate is an order of magnitude lower than the standard required, showcasing the system's stability and reliability. During the 8-hour TPC-C standard stress test, TDSQL successfully completed over 860 billion transactions and processed 40 trillion order details, with zero forced rollbacks and zero data inconsistency.

EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME WITH LOCKING STRATEGY

In today's digital landscape, collaborative work in real-time is on the rise, allowing individuals to connect across different locations through applications facilitated by client-server architecture, enabling users to access and work on the same project simultaneously. However, clients' simultaneous access and modifications to the database can result in data inconsistencies, underscoring the importance of concurrency control. Managing concurrent transactions can introduce complexities and potentially adversely impact server performance. Object caching emerges as a viable solution as an alternative approach to handling transaction traffic. Extended Serial Graph-Validation Queue (Extended SG-VQ) is a control concurrency scheme that operates within the client-server architecture framework and incorporates object caching. The cache component implements a queue-based validation algorithm as part of its validation process. At the same time, the server-side employs a graph-based validation algorithm with locking strategies. Through a series of hypothetical transaction scenarios across three cases, this study validates the effectiveness of the Extended SG-VQ, demonstrating its ability to utilize serial graphs, resolve conflicts, and identify cyclic patterns.

Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast

BackgroundThis study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027.MethodsThis research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE.ResultsBased on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products.ConclusionsThe growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient’s blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.

Preconception Folic Acid and Multivitamin Supplementation for the Prevention of Neural Tube Defect: An Umbrella Review of Systematic Review and Meta-analysis.

Previous reviews explored the association between maternal use of folic acid and multivitamin supplements and risk of neural tube defect (NTD) in children, with no definitive conclusion. These reviews had produced contradictory results, and there had been no umbrella review. Therefore, the objective of this umbrella review is to combine the inconsistent data on the effect of prenatal folic acid and/or multivitamin supplementation for the prevention of NTD in offspring. Using the PRISMA guideline, PubMed, Embase, Scopus, Web of Sciences, Cochrane Database of Systematic Reviews, Scopus, and Google Scholar reported that the effects of folic acid and/or multivitamin supplementation for the prevention of NTD in offspring were searched. The quality of the included studies was assessed using Assessment of Multiple Systematic Reviews (AMSTAR). A weighted inverse variance random-effects model was applied to find the pooled estimates. The subgroup analysis, heterogeneity, publication bias, and sensitivity analysis were also assessed. Ten SRM with 296,816 study participants were included. The random-effects model analysis from 10 included systematic review and meta-analysis revealed that the pooled effect of either folic acid or multivitamin supplementation for the prevention of NTD globally is found to be 0.43 (95% CI: 0.29, 0.58) (I2 = 93.50%; p ≤0.001). In the subgroup analysis, the pooled effect was found to be 0.23 (0.09, 0.37) in folic acid group, while this estimate is 0.63 (0.53, 0.72) and 0.61 (0.46, 0.75) in groups who took multivitamin. The pooled effect of prevention of NTD was found to be 0.50 (0.34, 0.66) in SRMs aimed at occurrence prevention (primary prevention) group, while this estimate is 0.20 (-0.01, 0.41) among SRMs, which aimed at reoccurrence (secondary) prevention, and 0.61 (0.46, 0.75) among those SRMs aimed to assess the effect folic acid or multivitamin for the prevention of both occurrence and reoccurrence. The pooled effect of either folic acid or multivitamin supplementation for the prevention of NTD was found to be 0.45 (0.03, 0.87) in SRMs of observational studies, while this estimate is 0.43 (0.32, 0.54) among SRMs of randomized controlled trials. This umbrella review of systematic review and meta-analysis found that prenatal folic acid and/or multivitamin supplementation was associated with a 57% reduction in NTD. Participants who took folic acid supplementation were associated with a slightly higher (77%) percentage of reduction in NTD compared with those who took multivitamin (37%). Reductions of 80% and 50% were observed for reoccurrence and occurrence prevention of NTD. Reductions of 57% and 55% of NTD have been found in SRM of RCTs and observational studies. This umbrella review revealed that both folic acid and multivitamin were associated with significantly lower levels of NTD in children. Considering the incorporation of those supplements in NTD prevention strategies during the preconception period is recommended. More large-scale prospective cohort and RCTs are needed to understand the protective effect of multivitamins and/or folic acid on NTD risk addressing the molecular mechanisms and to determine the optimal dose, duration, and timing of maternal multivitamin and folic acid intake for best child NTD risk reduction.

Detection and Utilization of Reflections in LiDAR Scans through Plane Optimization and Plane SLAM.

In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.

An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory

AbstractThere is a growing interest in multidisciplinary research in multimodal synthesis technology to stimulate diversity of modal interpretation in different application contexts. The real requirement for modality diversity across multiple contextual representation fields is due to the conflicting nature of data in multitarget sensors, which introduces other obstacles including ambiguity, uncertainty, imbalance, and redundancy in multiobject classification. This paper proposes a new adaptive and late multimodal fusion framework using evidence-enhanced deep learning guided by Dempster–Shafer theory and concatenation strategy to interpret multiple modalities and contextual representations that achieves a bigger number of features for interpreting unstructured multimodality types based on late fusion. Furthermore, it is designed based on a multifusion learning solution to solve the modality and context-based fusion that leads to improving decisions. It creates a fully automated selective deep neural network and constructs an adaptive fusion model for all modalities based on the input type. The proposed framework is implemented based on five layers which are a software-defined fusion layer, a preprocessing layer, a dynamic classification layer, an adaptive fusion layer, and an evaluation layer. The framework is formalizing the modality/context-based problem into an adaptive multifusion framework based on a late fusion level. The particle swarm optimization was used in multiple smart context systems to improve the final classification layer with the best optimal parameters that tracing 30 changes in hyperparameters of deep learning training models. This paper applies multiple experimental with multimodalities inputs in multicontext to show the behaviors the proposed multifusion framework. Experimental results on four challenging datasets including military, agricultural, COIVD-19, and food health data provide impressive results compared to other state-of-the-art multiple fusion models. The main strengths of proposed adaptive fusion framework can classify multiobjects with reduced features automatically and solves the fused data ambiguity and inconsistent data. In addition, it can increase the certainty and reduce the redundancy data with improving the unbalancing data. The experimental results of multimodalities experiment in multicontext using the proposed multimodal fusion framework achieve 98.45% of accuracy.

Diabetes Mellitus in Non-Functioning Adrenal Incidentalomas: Analysis of the Mild Autonomous Cortisol Secretion (MACS) Impact on Glucose Profile.

Non-functioning adrenal incidentalomas (NFAIs) have been placed in relationship with a higher risk of glucose profile anomalies, while the full-blown typical picture of Cushing's syndrome (CS) and associated secondary (glucocorticoid-induced) diabetes mellitus is not explicitly confirmed in this instance. Our objective was to highlight the most recent data concerning the glucose profile, particularly, type 2 diabetes mellitus (T2DM) in NFAIs with/without mild autonomous cortisol secretion (MACS). This was a comprehensive review of the literature; the search was conducted according to various combinations of key terms. We included English-published, original studies across a 5-year window of publication time (from January 2020 until 1 April 2024) on PubMed. We excluded case reports, reviews, studies on T1DM or secondary diabetes, and experimental data. We identified 37 studies of various designs (14 retrospective studies as well 13 cross-sectional, 4 cohorts, 3 prospective, and 2 case-control studies) that analysed 17,391 individuals, with a female-to-male ratio of 1.47 (aged between 14 and 96 years). T2DM prevalence in MACS (affecting 10 to 30% of NFAIs) ranged from 12% to 44%. The highest T2DM prevalence in NFAI was 45.2% in one study. MACS versus (non-MACS) NFAIs (n = 16) showed an increased risk of T2DM and even of prediabetes or higher fasting plasma glucose or HbA1c (no unanimous results). T2DM prevalence was analysed in NFAI (N = 1243, female-to-male ratio of 1.11, mean age of 60.42) versus (non-tumour) controls (N = 1548, female-to-male ratio of 0.91, average age of 60.22) amid four studies, and two of them were confirmatory with respect to a higher rate in NFAIs. Four studies included a sub-group of CS compared to NFAI/MACS, and two of them did not confirm an increased rate of glucose profile anomalies in CS versus NFAIs/ACS. The longest period of follow-up with concern to the glycaemic profile was 10.5 years, and one cohort showed a significant increase in the T2DM rate at 17.9% compared to the baseline value of 0.03%. Additionally, inconsistent data from six studies enrolling 1039 individuals that underwent adrenalectomy (N = 674) and conservative management (N = 365) pinpointed the impact of the surgery in NFAIs. The regulation of the glucose metabolism after adrenalectomy versus baseline versus conservative management (n = 3) was improved. To our knowledge, this comprehensive review included one of the largest recent analyses in the field of glucose profile amid the confirmation of MACS/NFAI. In light of the rising incidence of NFAI/AIs due to easier access to imagery scans and endocrine evaluation across the spectrum of modern medicine, it is critical to assess if these patients have an increased frequency of cardio-metabolic disorders that worsen their overall comorbidity and mortality profile, including via the confirmation of T2DM.

Mobility on demand in the United States – Current state of integration and policy considerations for improved interoperability

The concept of Mobility on Demand (MOD) aims to create a safe and convenient ecosystem for transportation and goods delivery. Despite offering various services, the integration and seamless data exchange among different modes of transportation remain challenging. To address this, research was conducted to examine the current state of integration in the MOD ecosystem, identify gaps in data exchange, and propose policy considerations. The research highlighted the limited integration and inconsistent data structures in the United States (U.S.), attributed to the absence of policy directives and high costs of traditional standards development. Industry-led initiatives are driving progress in standards and specifications. Deep linking and deep integration were distinguished, emphasizing the importance of a truly user-centric MOD solution. Emerging mobility models leveraging blockchain technology show promise in facilitating efficient data sharing and collaboration among various operators. Transportation policy makers play a crucial role in supporting open-source standards and integration efforts. An initial framework utilizing open-source efforts such as Transport Operator Mobility-as-a-service Provider (TOMP), Mobility Data Specification (MDS), General On-demand Feed Specification (GOFS), Transactional Data Specification (TDS), Operational Data Standard (ODS), and City Data Standard for Mobility (CDS-M) was proposed to drive the advancement of scalable and user-centric MOD systems.

Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions

Abstract In this article, we evaluate adding a weak physics constraint, that is, a physics-based empirical relationship, to the loss function with a physics-informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two-branch model we previously developed, as well as with a pure data-driven model. Unexpectedly, the proposed model did not consistently outperform the pure data-driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. This result has important implications for how to best incorporate physical constraints in ML models.

Prevalence of fetal anomalies, stillbirth, neonatal morbidity, or mortality in pregnancies complicated by placenta accreta spectrum disorders.

Placenta accreta spectrum disorders (PAS) lead to major complications in pregnancy. While the maternal morbidity associated with PAS is well known, there is less information regarding neonatal morbidity in this setting. The aim of this study is to describe the neonatal outcomes (fetal malformations, neonatal morbidity, twin births, stillbirth, and neonatal death), using an international multicenter database of PAS cases. This was a prospective, multicenter cohort study based on prospectively collected cases, using the international multicenter database of the International Society for PAS, carried out between January 2020 and June 2022 by 23 centers with experience in PAS care. All PAS cases were included, regardless of whether singleton or multiple pregnancies and were managed in each center according to their own protocols. Data were collected via chart review. Local Ethical Committee approval and Data Use Agreements were obtained according to local policies. There were 315 pregnancies eligible for inclusion, with 12 twin pregnancies, comprising 329 fetuses/newborns; 2 cases were excluded due to inconsistency of data regarding fetal abnormalities. For the calculation of neonatal morbidity and mortality, all elective pregnancy terminations were excluded, hence 311 pregnancies with 323 newborns were analyzed. In our cohort, 3 neonates (0.93%) were stillborn; of the 320 newborns delivered, there were 10 cases (3.13%) of neonatal death. The prevalence of major congenital malformations was 4.64% (15/323 newborns), most commonly, cardiovascular, central nervous system, and gastrointestinal tract malformations. The overall prevalence of major neonatal morbidity in pregnancies complicated by PAS was 47/311 (15.1%). There were no stillbirths, neonatal deaths, or fetal malformations in reported twin gestations. Although some outcomes may be too rare to detect within our cohort and data should be interpreted with caution, our observational data supports reassuring neonatal outcomes for women with PAS.

Temporal self-supervised domain adaptation network for machinery fault diagnosis under multiple non-ideal conditions

Although deep learning techniques have been widely used in machinery fault diagnosis, the key issue of feature learning under multiple non-ideal conditions, i.e., strong noise interference, inconsistent data distributions and class imbalance, is not tackled effectively. Thus, a novel deep neural network (DNN) named temporal self-supervised domain adaptation network (TSSDAN) is developed for machinery fault diagnosis in this article. Firstly, a temporal self-supervised learning (TSSL) method is implemented to tackle the issue of strong noise interference and class imbalance. Secondly, a specific feature extractor, perception auto-encoder (PAE) is implemented for signal feature learning by mimicking the working mode of the biological visual system and spinal reflex system. Thirdly, a cascaded domain adaptation (CDA) method is constructed to tackle the domain shift issue by considering the feature distribution discrepancy of various domains for all perception levels. Two cases (i.e., rotor and gearbox) are used in this study to evaluate the performance of TSSDAN. The experiment results illustrate the outperformance of TSSDAN in solving the problem of deep feature learning and machinery fault diagnosis under multiple non-ideal conditions.