Response Data Research Articles

IoT Power Optimization: A Comprehensive Survey

Abstract: The Receiving station fault detection correction has been a goal of power system engineers to achieve maximum required Receiving side Power from the generating station , since the creation of distribution and transmission systems. By enabling the disconnecting of faulty lines prior to any serious equipment damage. Rapid fault detection can help to protect the machinery. An accurate fault type can be used to determine the maximum power of the generation station. Various algorithms are being developed to perform this task with greater accuracy and efficiency. The cloud IOT server receives these fault condition and solution data for immediate response. It is necessary to identify issues with power transmission lines for the security and maintenance of a power system. Most defect detection methods use the electrical quantities that current and voltage transformers provide for measurement. These transformers can be expensive and must come into direct contact with the highvoltageequipment being observed.

의과대학생이 평가한 자신과 동료 학생의 학습윤리의식 분석

Objectives The purpose of this study is to identify and characterize the prevailing state of medical students' per-ceptions of their own and their classmates' academic ethics. Methods For the purpose of this study, we conducted in an online survey method for all six academic years at a single medical school and a total of 217 response data were analyzed. The data collected were analyzed using de-scriptive statistics, t-test, and ANOVA. Results Medical students rated their own academic ethics at 4.46 and their peers at 3.96. Female students rated themselves higher than male students, but rated their peers lower. This gender difference resulted in a statistically significant gap in scores between self- and peer-ratings. Scores showed an upward trend with increasing grade levels and academic stages. In particular, peer scores showed statistically significant differences by phase, and the gap scores between self- and peer-ratings were also statistically significant across different academic phases. Students in the criterion-referenced rating system rated themselves and their peers higher than students in the norm-referenced rating system, although the difference was not statistically significant. Conclusions The results of this study serve as a basic framework for the development of educational strategies aimed at improving and increasing perceptions of academic ethics among medical students.

Resurgence of wheat stem rust infections in western Europe: causes and how to curtail them.

Ten years ago, (black) stem rust - the most damaging of wheat (Triticum aestivum) rusts - re-emerged in western Europe. Disease incidences have since increased in scale and frequency. Here, we investigated the likely underlying causes and used those to propose urgently needed mitigating actions. We report that the first large-scale UK outbreak of the wheat stem rust fungus, Puccinia graminis f. sp. tritici (Pgt), in 2022 may have been caused by timely arrival of airborne urediniospores from southwest Europe. The drive towards later-maturing wheat varieties in the UK may be exacerbating Pgt incidences, which could have disastrous consequences. Indeed, infection assays showed that two UK Pgt isolates from 2022 could infect over 96% of current UK wheat varieties. We determined that the temperature response data in current disease risk simulation models are outdated. Analysis of germination rates for three current UK Pgt isolates showed substantial variation in temperature response functions, suggesting that the accuracy of disease risk simulations would be substantially enhanced by incorporating data from prevailing Pgt isolates. As Pgt incidences continue to accelerate in western Europe, we advocate for urgent action to curtail Pgt losses and help safeguard future wheat production across the region.

Structural acceleration response reconstruction based on BiLSTM network and multi-head attention mechanism

The effectiveness of structural health monitoring often relies on the precise analysis of structural response data, with data completeness directly impacting the performance of monitoring systems. Addressing the common issue of data loss in structural health monitoring systems, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) network based on a multi-head attention mechanism for the reconstruction of missing structural acceleration responses. Firstly, employing a two-layer BiLSTM network to establish an encoder-decoder framework for extracting spatio-temporal correlation features among sensor data. Subsequently, innovatively embedding a multi-head attention mechanism into the network framework enhances the modeling capability of long-distance input elements and establishes dependencies among different sensors, thereby enabling the network to better capture the global features of input information. The proposed method undergoes validation through a numerical example of a simply supported beam, practical monitoring data on the Hardanger Bridge from the Norwegian University of Science and Technology, and an experimental study of steel frame structure from our laboratory. These validations are compared with alternative response reconstruction models. The experimental results make obvious that the proposed method demonstrates superior accuracy in response reconstruction. Additionally, the suitability of this method for modal identification is validated. Through the utilization of reconstructed responses, the method effectively discerns the structural natural frequencies with accuracy.

Wireless Remote Controlled Multi-Purpose Robot using Raspberry PI

Over the past five decades, robotics and associated technologies have evolved into indispensable components across numerous industries, particularly in advanced manufacturing. Moreover, their application has extended beyond production-centric domains to encompass pivotal roles in military and law enforcement sectors, owing to advancements in sophistication, reliability, and compactness. This paper proposes the implementation of a real-time object detection system capable of precisely identifying and locating recognized objects, presenting a significant and practical advancement. Integrating various modules such as GSM and GPS with a Raspberry Pi, along with an array of sensors including metal and flame sensors, an L293 Motor driver, HC-SR04 Ultrasonic sensor, and HC-05 Bluetooth, this system facilitates seamless recognition and localization of objects in real-time. The Bluetooth connectivity feature enables remote control of the device, while a built-in camera captures images of unidentified individuals, thereby enhancing its surveillance capabilities. The incorporation of a motor driver ensures secure and controlled movement of the robot, while the ultrasonic sensor detects obstacles within its proximity. Additionally, the metal sensor swiftly identifies metallic objects, relaying their locations to the administrator, whereas the flame sensor promptly detects fires, enabling the system to dispatch location data for immediate response. This holistic approach renders the proposed system a versatile tool applicable across a spectrum of scenarios, ranging from security and surveillance operations to emergency response initiatives.

Evaluation of Cognition of Rural Public Space Based on Eye Tracking Analysis

Amid the current global trend toward urbanization, there is a growing need for an improved quality of life. While rural public spaces are essential components of community life, their design and planning have traditionally focused on material aspects, neglecting the spiritual well-being of rural residents. This study uses the public spaces of Huixingtou Village in Handan City, Hebei Province, China, as an example to investigate the impact of five key elements within rural public spaces (architecture, streets, greenery, nodes, and landmarks) on individual emotions and perceptions, exploring how these elements contribute to the satisfaction of individuals’ spiritual lives. Initially, we compared the disparities among these elements concerning perceptual dimensions (functionality, technology, and aesthetics) and emotional dimensions (sense of achievement, safety, and well-being). This analysis revealed significant variations in emotional and perceptual dimensions influenced by different rural public space elements. Subsequently, we examined the associations between these elements and objective physiological response data from participants, using eye-tracking technology to delve into the interplay between emotions and perceptions. The results indicate that distinct rural public space elements elicit specific emotional responses, with visual elements and features exerting a pronounced influence on emotional reactions. This in-depth analysis provides comprehensive recommendations for emotional design strategies. Ultimately, this study furnishes emotionally driven design strategies for rural public spaces based on objective data, offering practical guidance for sustainable development and the enhancement of rural public space quality. These findings have significant implications for creating more attractive, inclusive, and sustainable rural spatial environments, addressing the needs of individuals seeking a high and enhanced quality of life.

#1234 Thrombotic microangiopathy (TMA) response during index atypical haemolytic uremic syndrome (aHUS) hospitalisation

Abstract Background and Aims aHUS is associated with dysregulation of the alternative complement pathway. C5 inhibitors (C5i) improve TMA response and renal recovery, but significant morbidity may remain. Although C5i studies have reported on TMA response rates after treatment discontinuation, TMA response data during index hospitalisation are limited. This study described TMA response during index aHUS hospitalisation using real-world evidence from a large, diverse cohort of presumed incident aHUS in the United States (US). Methods This was a retrospective cohort study of hospitalised adult patients with presumed aHUS with electronic healthcare records in the US Premier Healthcare Database, which contains ∼25% of all US hospitalisations (January 1, 2011–June 30, 2021). aHUS was defined as the presence of an International Classification of Diseases (ICD)-9-CM/ICD-10-CM diagnostic code for TMA or haemolytic uremic syndrome (HUS) and a treatment code for a C5i, without a diagnostic code for secondary causes of TMA/HUS or other C5i indications. Complete TMA response was defined by >25% decline of serum creatinine (SCr) and normalisation of platelets and lactate dehydrogenase (LDH) from index presentation, without renal replacement therapy (RRT) at discharge. Included patients had longitudinal laboratory data; patients with in-hospital death were excluded. Patient demographics, clinical characteristics, treatment patterns, and clinical outcomes were assessed as risk factors. Variables were analysed by Wilcoxon signed-rank test, Kruskal–Wallis test, Fisher's exact test, or Chi-square test, as appropriate. Results Of 634 adult patients hospitalised with presumed aHUS, 556 survived to discharge. SCr, platelet, LDH, and all three TMA response criteria data were available for 21.0% (117/556), 18.9% (105/556), 18.2% (101/556), and 16.5% (92/556) of patients, respectively. Median duration between the first and last laboratory measurement before discharge was 21 days (interquartile range [IQR]: 12–33) for SCr, 21 days (IQR: 14–32) for platelets, and 17.5 days (IQR: 11–27) for LDH. Only 6.5% (6/92) of patients achieved a complete TMA response at discharge, preventing statistical analysis with other parameters. Platelet normalisation occurred in 53.3% (56/105) of patients and was associated with younger median age (39 vs 54 years; P = 0.0167) and median time from admission to C5i (8 vs 10 days; P = 0.0459). LDH normalisation occurred in 29.7% (30/101) and was associated with male sex (60.0% vs 22.5%; P = 0.0005), Hispanic ethnicity (20.0% vs 7.0%; P = 0.0330), and median duration of intensive care unit stay (3.5 vs 8 days; P = 0.0185). A drop of >25% in SCr from admission to discharge occurred in 20.5% (24/117) and was associated with Hispanic ethnicity (16.7% vs 8.6%; P = 0.0089) and no RRT (45.8% vs 14.0%; P = 0.0014). Among patients with mortality data, the association between platelet normalisation and 60-day post-discharge mortality was significant (P = 0.0499). LDH normalisation and a >25% decline in SCr were not associated with 60-day (P = 0.6368 and P = 1.000, respectively) or 365-day (P = 0.4288 and P = 0.7023, respectively) post-discharge mortality. Conclusions In this US population of hospitalised adults with presumed incident aHUS, few patients achieved a complete TMA response at discharge, despite C5i. Small sample size limited assessment of clinical characteristics associated with complete TMA response; however, achievement of each TMA response criterion was associated with unique clinical and patient characteristics. Future efforts should focus on characterising patients with aHUS at high risk at discharge and on developing novel precision therapies to improve outcomes.

Development of an electrocoagulation method using alternating pulse current to treat wastewater generated by tanneries

ABSTRACT Direct current electrocoagulation has various drawbacks, including electrode passivation, heat creation from energy consumption, and high sludge formation. The restrictions limit its usage in tannery wastewater treatment. Therefore, alternating pulse current EC (APC-EC) was developed to solve these obstacles. The study was empirically examined, considering high-frequency (3,000–11,000 Hz), stirring speed (400–1,200 rpm), and reaction time (15–30 min). This research examined how factors affect chemical oxygen demand (COD) removal efficiency, turbidity from trivalent chromium (Cr+3), and energy consumption for perforated aluminum electrodes. The central composite design in the surface response design approach has improved various operational parameters in the APC-EC process for tannery wastewater treatment. The employment of mathematical and statistical methods resulted in optimal removal of COD, Cr+3, and turbidity while reducing energy usage. Using mathematical and statistical methods, we achieved maximum COD, Cr+3 ion, and turbidity reduction while reducing energy use. The investigation found that COD (70.3%), Cr+3 (89.56%), and turbidity (96%) were the most rapidly removed components at 11,000 Hz, 576 rpm, and 30 min. Surface response data explain high-frequency APC-EC dynamics.

Temperature control research for laser-induction hybrid strengthening process

ABSTRACT A comprehensive control methodology has been developed to address challenges such as temperature fluctuations, inconsistent depth penetration, and compositional integrity variations during laser-induction hybrid strengthening performed with consistent power parameters. This approach synergistically integrates a fuzzy controller and a decoupling compensator. In the initial part of this study, a temperature field model was utilized to investigate the effects of different process factors on temperature profiles. The analysis of step response data facilitated the development of mathematical models that delineate the correlation between the input factors and output responses. Subsequently, a decoupling compensation technique was used to reduce the coupling effects between temperature and its rate of change. A fuzzy controller was then developed to reduce the system's reliance on mathematical models, hence improving the accuracy of the control strategy. The results indicate that the application of fuzzy PI control and the implementation of decoupling compensation, the control strategy successfully improves the performance of the controller by minimizing parameter interactions. This strategy also incorporates real-time adjustment of PI parameters. Compared to conventional PI control, this method offers improved response speed and reduced overshoot. The laser-induction hybrid strengthening technique ensures superior temperature control, maintaining steady-state temperature precision within a 1% tolerance.

Minimal Conditioned Stiffness Matrices with Frequency-Dependent Path Following for Arbitrary Elastic Layers over Half-Spaces

This paper introduces an efficient computational procedure for analyzing the propagation of harmonic waves in layered elastic media. This offers several advantages, including the ability to handle arbitrary frequencies, depths, and the number of layers above an elastic half-space, and efforts to follow dispersion curves and flag up possible singularities are investigated. While there are inherent limitations in terms of computational accuracy and capacity, this methodology is straightforward to implement for studying free or forced vibrations and obtaining relevant response data. We present computations of wavenumber dispersion diagrams, phase velocity plots, and response data in both the frequency and time domains. These computational results are provided for two example cases: plane strain and axisymmetry. Our methodology is grounded in a well-conditioned dynamic stiffness approach specifically tailored for deep-layered strata analysis. We introduce an innovative method for efficiently computing wavenumber dispersion curves. By tracking the slope of these curves, users can effectively manage continuation parameters. We illustrate this technique through numerical evidence of a layer resonance in a real-life case study characterized by a fold in the dispersion curves. Furthermore, this framework is particularly advantageous for engineers addressing problems related to ground-borne vibrations. It enables the analysis of phenomena such as zero group velocity (ZGV), where a singularity occurs, both in the frequency and time domains, shedding light on the unique characteristics of such cases. Given the reduced dimension of the problem, this formulation can considerably aid geophysicists and engineers in areas such as MASW or SASW techniques.

Inverting social innovation to transform health system responses to climate change adaptation and mitigation in the global south.

Systems thinking is aimed at understanding and solving complex problems that cut across sectors, an approach that requires accurate, timely, and multisectoral data. Citizen-driven big data can advance systems thinking, considering the widespread use of digital devices. Using digital platforms, data from these devices can transform health systems to predict and prevent global health crises and respond rapidly to emerging crises by providing citizens with real-time support. For example, citizens can obtain real-time support to help with public health risks via a digital app, which can predict evolving risks. These big data can be aggregated and visualized on digital dashboards, which can provide decision-makers with advanced data analytics to facilitate jurisdiction-level rapid responses to evolving climate change impacts (e.g., direct public health crisis communication). In the context of climate change, digital platforms can strengthen rapid responses by integrating information across systems (e.g., food, health, and social services) via citizen big data. More importantly, these big data can be used for rapid decision-making,a paradigm-changing approach that can invert social innovation, which we define as co-conceptualizing societal solutions with vulnerable communities to improve economic development with a focus on community wellbeing. However, to foster equitable and inclusive digital partnerships that invert social innovation, it is critical to avoid top-down approaches that sometimes result when researchers in the Global North and South collaborate. Equitable Global South-North partnerships can be built by combining digital citizen science and community-based participatory research to ethically leverage citizen-driven big data for rapid responses across international jurisdictions.

Outcome of Hairy Cell Leukemia: A Single-center Study

Abstract: BACKGROUND: Hairy cell leukemia (HCL) is a rare chronic B-cell lymphoproliferative disorder. Modern therapy with purine analogs and immunotherapy can provide long-term remission, but the risk of recurrence remains about 40%–50%. The aim of this study was to evaluate the outcome of patients with HCL who received treatment in Nanakali Hospital. PATIENTS AND METHODS: A retrospective cross-sectional study was carried out on 50 patients of HCL diagnosed from 2004 to 2022 in Nanakali Hospital in Erbil City, Kurdistan Region, Iraq. Demographics, clinical presentation, treatment data, complications, response, recurrence, and survival data were collected from medical records. The results were presented with descriptive statistics. Variables were compared by Chi-square analysis. RESULTS: The mean age was 52.64 ± 12.37 years, and 84% were male. The most common presenting symptoms were splenomegaly (18%) and fatigue (14%). The majority (69.6%) received cladribine; the response rate was 73.9%, with a complete remission (67.4%). 47.8% had recurrent disease. The most common adverse effects were febrile neutropenia (58.7%) and Grade III and IV hematologic toxicity (41.3%). The results were significantly associated with ANC pretreatment (P = 0.019), comorbidity (P = 0.001), and treatment response (P = 0.004). Cladribine–rituximab combination resulted in complete remission (100%). Ten-year overall survival was 70%. CONCLUSIONS: The results were broadly consistent with literature reports, demonstrating the efficacy and safety of cladribine with/without rituximab as first-line therapy for HCL but with a 30% mortality of concern. Further studies should identify modifiable factors that affect poor prognosis in subgroups to guide improvements in risk management of HCL.

Data driven damping separation method for the Mega-offshore wind turbine tower

This study proposes a novel damping separation method based on operating state-measured data collected from a 6.7 MW offshore wind turbine tower to provide various damping ratios caused by different loads. First, the operating frequencies and total damping ratios for various operating states are identified using the dynamic responses under normal operating conditions. Subsequently, the natural and harmonic frequencies caused by the rotation of the blades are distinguished using rotor speed data and the Campbell diagram. Finally, to determine the various damping ratios of different modal orders of offshore wind turbines under multiple load conditions using only actual response data, a new damping equation based on different damping sources is proposed. Filtering strategies for total damping ratios corresponding to different damping sources with varying vibration amplitudes and wind speed thresholds were proposed. The outlier elimination method was introduced to remove error-scattered points, and the average value calculation was used to identify various damping ratios. The actual measured data of a 6.7 MW offshore wind turbine tower was studied, and the results show that the proposed method can effectively separate various damping ratios.

Culturally responsive data analysis for evaluations in contexts of fragility, conflict, and violence

This article aims to provide practical guidance for conducting culturally responsive data analysis for evaluations in contexts of fragility, conflict and violence. To this end, we propose three core principles – or ‘golden rules’ -that must be taken into consideration when analyzing such data. These include the engagement of wise stakeholders in data analysis, the consideration of diversity in detecting and treating outliers and the documentation of all data analysis processes, including the assumptions made. We are aware that applying these principles may pose practical challenges given the complexity of fragile and conflict-based environments. However, doing so can enhance the validity of evaluation findings and their acceptability. Further research is encouraged to empirically test the proposed principles in conflict regions, notably in Sub-Saharan Africa.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Effective re-induction regimen for children with recurrent medulloblastoma

Abstract Background There is no standard treatment for recurrence of medulloblastoma, the most common malignant childhood brain tumor, and prognosis remains dismal. In this study, we introduce a regimen that is well-tolerated and effective at inducing remission. Methods The primary objectives of this study were to assess tolerability of the regimen and overall response rate. A retrospective chart review of patients with recurrent medulloblastoma, treated at two institutions with a re-induction regimen of intravenous irinotecan and cyclophosphamide with oral temozolomide and etoposide, was performed. Demographic, clinicopathologic, toxicity and response data were collected and analyzed. Results Nine patients were identified. Median age was 5.75 years. Therapy was well-tolerated with no therapy-limiting toxicities and no toxic deaths. Successful stem cell collection was achieved in all five patients in whom it was attempted. Overall response rate after two cycles was 78%. Three patients had a complete response, four patients had a partial response, one patient had stable disease, and one patient had progressive disease. Four patients are alive with no evidence of disease (NED), two patients are alive with disease, two patients have died of disease, and one patient died of toxicity related to additional therapy (NED at time of death). Conclusion This regimen is well-tolerated and effective. Tumor response was noted in the majority of cases, allowing patients to proceed to additional treatment with no or minimal disease. Further study of this regimen in a clinical trial setting is an important next step.

On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges

Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

Dabrafenib-trametinib in BRAF V600-mutated non-small-cell lung cancer: a single center real world experience.

Aim: We retrospectively evaluated the effect of dabrafenib/trametinib combination in patients with BRAF-mutated non-small-cell lung cancer (NSCLC) treated in a single center from 2017 to 2022. Patients: The response and safety data of 42 patients (27 treated in first-line and 15 as second/subsequent lines) were analyzed. Results: The objective response was 73.8%, with no differences between patients undergoing first- or second-line. A longer, statistically significant median progression-free survival (PFS) was observed in patients receiving the combination in first-line vs those in the second/subsequent lines (19.9 months [95% CI: 19.7-20]vs 13.1 months [95% CI: 8.6-17.6], respectively;p=0.012). The median overall survival (OS) was 29.9 months (95% CI: 14.1-45.7) for patients treated with the combination in first-line and 22.4months (95% CI: 14.6-30.2) for those treated in subsequent lines. The combination was well tolerated. Conclusion: We confirm the efficacy of dabrafenib/trametinib in BRAF-V600-mutated NSCLC.

Penerapan Media Video Tutorial pada Mata Pelajaran Teknologi Menjahit Kompetensi Penyelesaian Kampuh untuk Meningkatkan Hasil Belajar Siswa Kelas X Tata Busana SMK Negeri 3 Kediri

This research is motivated by the fact that student learning outcomes of seam allowens competencies are still low. Students still not being able to absorb the lessons well because of limited learning media used. The aims of this research is : (1) to describe the implementation of video tutorial media of seam allowens competencies, (2) to describe the students responses to video tutorial media of seam allowens competencies, and (3) to describing student learning outcomes in seam allowens competencies. This type of research is Classroom Action Research (CAR). this research used quantitative data in the form of observations of learning implementation, student response data and student learning outcomes in the cognitive and psychomotor domains. The data collection methods used were observation sheets on learning implementation, student response questionnaires and student learning outcome tests. The results of this research are : (1) The results of the implementation of learning using the video tutorial media of seam allowens competencies obtained very good results with teacher observation results 90% and student observation results 93,1%, (2) The results of student responses regarding the application of video tutorial media of seam allowens competencies was very good with results 98,2% and (3) Student learning outcomes in the cognitive and psychomotor domains increased from cycle I to cycle III.