Analysis System Research Articles

A human factor analysis of remote-control failure for maritime autonomous surface ships

Human factor management is crucial to the safety of maritime shipping. Whilst there has been extensive research on human factors in conventional shipping, the development of autonomous shipping necessitates the investigation of emerging human factors under the evolving landscape. Given that the research on human factor analysis for remote operation failure is limited, this study aims to enrich the literature by establishing a comprehensive human factor framework and empirically assessing the factors. Based on the literature review and expert interview, a total of ten categories and thirty-one human factors are organized under the human factor analysis and classification system. Adopting the purposive sampling, questionnaire surveys are then distributed to experts specializing in ship operations and automation to collect their degree of belief on potential human factors. A hybrid method composed of evidential reasoning and the rule-based Bayesian network is applied to analyze the data. The empirical results indicate that adverse mental states and adverse cognitive states are perceived to be the most threatening risk categories in remote operations. This study expands the academic research on human factor analysis and highlights the significance of managing psychological and cognitive factors. The findings provide managerial implications for the future safety management of autonomous ships.

A Computational Model for Water Quality Analysis and Assessment in Tanzania

Research on water quality has received much attention in both developing and developed countries. This is because of the fact that, the effects of poor quality of water are detrimental to human beings, animals and the environment. This study is about a computational model for water quality analysis and assessment in Tanzania. Water quality can be understood as the measure of suitability of water based on physical, chemical and biological attributes. Water quality analysis and assessment face several challenges due to population growth, urban land use, agricultural activities, and industrialization. Besides, attempts have been made by the scholars to address the challenges. However, the tools used like titrimetric, electrometric, pH-meter, thermometer and turbidity meter are yet to come up with effective solutions. Because of these, the researcher was compelled to adopt computational model which uses Statistical Analysis System (SAS) software in order to come up with effective solutions concerning water quality analysis and assessment. In this study therefore, the secondary data were collected from Lake Victoria littoral stations under the auspices of the Ministry of Water in Tanzania with the objective to get sufficient information concerning water quality analysis and assessment. Additionally, the collected data were coded in SAS software to analyse independent and dependent variables. SAS software therefore, was employed to obtain central tendency and dispersion as benchmarks in determining quality of water. Also, the Multivariate Linear Regression Model was run to obtain coefficients of estimation, 95% confident limits and p-value. Statistical findings from central tendency and dispersion indicate that, the mean for potential of Hydrogen (pH) was 8.165; for total suspended solids was 3.065 mg/l; chloride displayed a mean of 6.494 mg/l; calcium displayed a mean of 6.421 mg/l; iron had a mean of 0.188 mg/l; magnesium displayed a mean of 3.331 mg/l and sulphate had mean of 2.326 mg/l. Looking closely at all of the above-mentioned water quality parameters, they all align with a Tanzania Bureau of Standards (TBS) and World Health Organization (WHO) as shown on table 1. Findings from the Multivariate linear regression model shows that: First, iron had a p-value of 0.0153, magnesium 0.0347 and total hardness had a p-value of 0.001. All of these were statistically significant in the analysis and assessment of water quality as shown on table 2. The study concludes that, the water quality in Lake Victoria complies with both TBS and WHO standards as explained above.

The Kinetic and Kinematic Efficacy of Posterior Leaf Spring Ankle-foot Orthosis in Patients with Drop foot

Background: Dorsi flexor weakness is one of the common consequences of stroke with a negative impact on normal walking. Posterior leaf spring (PLS) Ankle Foot Orthosis (AFO) is the most frequent prescription to assist foot clearance and leg propulsion in these patients. However, the real efficacy of this AFO needs more attention. This study aimed to investigate the efficacy of PLS AFO on gait outcomes as the most common AFO in drop foot post-stroke patients. Methods: The study design was a pre–post-test experiments in which twenty drop foot patients with a mean age of 65 years with a mean age of 58.45 (±5.23) years who were 4/5 years post-stroke. They walked at self-selected walking speed on a 10 meters walk away in two conditions of with their own shoes and PLS AFO. The 3D motion capture gait analysis system including 6 vicon cameras and a Kistler force plate were used in order to measure gait kinetic and kinematic variables. For each trial, each joints (ankle, knee and hip) angle, moment and power were averaged over one-minute walking. Results: Walking with PLS showed a statistically significant improve kinetic and kinematic variables in hip, knee and ankle joints included power, moments and angles at entire gait cycle. P<0.05. Conclusions: In our study of stroke patients who were fitted with a PLS AFO, immediate significant improvements occurred in most kinetic and kinematic than shoes only in sacrifice some others.

An observational study of lower limb muscle imbalance assessment and gait analysis of badminton players.

The imbalance of muscle strength indicators has a negative impact on players. Lower limb muscle imbalance can cause gait abnormalities and increase the risk of muscle injury or decreased performance in significantly asymmetrical situations. This study aims to assess the lower limb muscle imbalance and gait feature between the dominant and non-dominant sides of badminton players and the associations between the two variables. The study included 15 badminton players with years of training experience. Muscle strength and gait parameters were obtained from isokinetic muscle strength testing and plantar pressure analysis systems. The symmetry index was calculated based on formulas such as plantar pressure distribution and percentage of plantar contact area. In the isokinetic muscle strength test, significant differences were found in bilateral knee flexors' average power and total work at 60°/s angular speed. The hamstring to quadriceps ratio (H/Q) range of knee joints of the dominant and non-dominant sides is 0.63-0.74at low speed, while the H/Q range is 0.81-0.88at fast speed. The H/Q of bilateral knees increases with increasing angular velocity. As the angular velocity increases, the peak torque to body weight ratio (PT/BW) of the participants' bilateral knee flexors and extensors shows a decreasing trend. The asymmetry score of H/Q at 180°/s angular speed is positively related with step time and stance time. There are varying degrees of differences in gait staging parameters, plantar pressure in each area, plantar contact area, and symmetry index between the dominant and non-dominant sides of badminton players when walking. Badminton players have weaker flexors of the knee joint, imbalanced muscle strength in flexors and extensors, decreased lower limb stability, and a risk of knee joint injury on the non-dominant side. The bending and stretching strength of the knee joint on the dominant side of the players is greater than that on the non-dominant side. The pressure in the first metatarsal region of the dominant side is higher, while that in the midfoot and heel regions is higher on the non-dominant side. badminton players have better forward foot force and heel cushioning ability. Long term badminton sports result in specialized changes in plantar pressure distribution and reduced symmetry.

Research and Design of Digitally Empowered Teaching Model Transformation

Traditional offline teaching models, particularly in courses like Data Structures and Algorithm Design, struggle to meet the demands of modern education due to limited classroom time, lack of personalized instruction, and insufficient real-time feedback. To address these issues, this paper proposes a digitally empowered teaching model that integrates big data, cloud computing, and artificial intelligence. By extending learning beyond the classroom with online platforms like MOOC, PTA, and Rain Classroom, and using a Student Concentration Analysis System, the deep learning model collects and analyzes data on students' learning behaviors. This enables personalized feedback and dynamic adjustments to teaching content, improving both engagement and learning outcomes. Additionally, a Blockchain-based Student Record System ensures secure data management, making the approach more adaptive, data-driven, and student-centered.

Evaluation of Hurricane Analysis and Forecast System (HAFS) Error Statistics Stratified by Internal Structure and Environmental Metrics

Abstract This study quantifies tropical cyclone (TC) error statistics from the Hurricane Analysis and Forecast System (HAFS) across different environmental conditions (e.g., vertical wind shear) and inner core structural metrics. A particular focus is the evolution of poorly understood aspects of internal TC structure, including vortex tilt, and their impact on forecast errors. Although previous studies have demonstrated that vortex tilt, vertical wind shear, and precipitation processes impact TC intensity and track, this is the first known study to stratify these cooperative interactions to gain insights on their relationships with forecast errors. A three-year retrospective sample of forecasts in the North Atlantic basin from two HAFS configurations (-A and -B) demonstrates that TCs with larger tilt magnitudes have larger forecast track errors on average than smaller tilt TCs. Smaller tilt magnitudes have larger absolute intensity errors in short range forecasts, whereas larger tilt magnitudes tend to have larger negative intensity biases at medium range. TCs with a tilted vortex are shown to have both left of shear (maximizing DSL) and left of tilt oriented positional track biases. Furthermore, those cases with greater downshear biases tend to have more convection and larger positive intensity biases, highlighting the importance of the interplay between inner core characteristics and forecast errors.

Will new tram lines enhance the connectivity and walkability? A case study of Sakarya

<p>This study investigates the impact of planned tram lines on walkability and accessibility in Sakarya, utilizing Geographic Information Systems (GIS) for spatial analysis. The research evaluates the catchment areas of proposed tram stops, assessing their suitability and coverage, and examines how these areas integrate with existing public transport routes. By calculating time-oriented catchments for 5, 8, and 10-minute walking distances, the study identifies service gaps and redundancies in the public transport network. The findings reveal that the new tram lines will enhance accessibility by extending coverage to high-density areas and potentially reducing the demand on existing bus services. The analysis also highlights the overlap between bus transit lines and tram stops, suggesting optimizations to improve network efficiency. The results offer valuable insights for urban planners, aiming to optimize Sakarya’s public transport system to be more inclusive and sustainable. This study contributes to urban mobility planning by providing a detailed understanding of how new tram lines can improve connectivity and support sustainable urban development.</p>

Validation of an artificial intelligence system for holter ECG analysis

Abstract Introduction Holter ECG is a commonly used tool to identify heart rhythm disorders. As physician-based analysis of such Holter ECG is time consuming, various approaches using computerized interpretation have been developed. However, accuracy of these tools remains inferior to physician-based analysis. Thus, this study assesses the diagnostic performance of an artificial intelligence (AI)-powered Holter ECG service in comparison to results from reference databases as well as with physician-based evaluation. Methods 231 ECG recordings, covering 2,261 hours of ECG with annotations for 10,024,315 heartbeats from the following reference databases were used: MIT-BIH Arrhythmia (MITDB), American Heart Association ECG (AHDB), MIT-BIH Atrial Fibrillation (AFDB), Long Term Atrial Fibrillation (LTAFDB). Those recordings were originally collected from a mixed population of inpatients and outpatients between 1975 and 2008. Analysis of these recordings regarding the detection of QRS complexes, QRS classification and detection of Atrial Fibrillation (AF) was performed using a novel service for AI-based ECG analysis. Results were compared to the diagnosis stated within the database. QRS complex detection accuracy was performed on recordings from MITDB and AHADB by checking if time positions are within 150 ms to reference annotations. The QRS classification in three classes (normal, supraventricular, ventricular) was then compared for each detected heartbeat. Mismatches were adjudicated manually by three experts. Episodes of Atrial Fibrillation were detected in recordings from MITDB, AFDB and LTAFDB and compared to the reference episodes from the databases. The accuracy was evaluated based on the overlap with the reference annotations in the time-domain. Results Sensitivity and Precision of heartbeat detection compared to the reference databases exceeded 99% Figure 1 displays the results of heartbeat classification. Normal heartbeats were classified with a sensitivity of 99.6%, precision of 99.9% and specificity of 99.6%. For ventricular heartbeats, a sensitivity of 99.58 %, precision of 99.88 and specificity of 99.99% were achieved. Premature supraventricular contractions were recognized with a sensitivity of 99.58 %, precision of 100% and specificity of 100%. When comparing the 248,187 QRS complex classifications of MITDB and AHADB to the AI-powered analysis, 128 discrepancies were revealed. 43 were due to a discrepancy of convention as these beats were in sections that were also marked as AF episodes by the databases and the AI-powered analysis marks all beats in AF episodes as normal. Of the 85 remaining discrepancies, more than 87% were adjudicated in favor of the AI-powered analysis by the experts. Figure 2 shows the comparison of AF detection results showing Sensitivity and Precision above 99%. Conclusion AI-powered Holter ECG analysis compared to reference ECG stripes exceed 99% of sensitivity, specificity and precision.QRS complex detectionAF/ AFib detection

A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.

Manual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, we recently designed an artificial intelligence based digital pathology system (AI-DP) for digital image capture and analysis of KK thick smears. Preliminary results of its diagnostic performance are encouraging, and a comprehensive evaluation of this technology as a cost-efficient end-to-end diagnostic to inform STH control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation. Here, we describe the study protocol for a comprehensive evaluation of the AI-DP based on its (i) diagnostic performance, (ii) repeatability/reproducibility, (iii) time-to-result, (iv) cost-efficiency to inform large-scale deworming programs, and (v) usability in both laboratory and field settings. For each of these five attributes, we designed separate experiments with sufficient power to verify the non-inferiority of the AI-DP (KK2.0) over the manual screening of the KK stool thick smears (KK1.0). These experiments will be conducted in two STH endemic countries with national deworming programs (Ethiopia and Uganda), focussing on school-age children only. This comprehensive study will provide the necessary data to make an evidence-based decision on whether the technology is indeed performant and a cost-efficient end-to-end diagnostic to inform large-scale deworming programs against STHs. Following the protocolized collection of high-quality data we will seek approval by WHO. Through the dissemination of our methodology and statistics, we hope to support additional developments in AI-DP technologies for other neglected tropical diseases in resource-limited settings. The trial was registered on September 29, 2023 Clinicaltrials.gov (ID: NCT06055530).

Air quality modeling intercomparison and multiscale ensemble chain for Latin America

Abstract. A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Linking antibody against apolipoprotein A-1 to metabolic dysfunction-associated steatohepatitis in mice

Abstract Background Metabolic dysfunction-associated fatty liver disease (MASLD) is a common liver condition associated with increased cardiovascular disease (CVD) risk. Cytokeratin 18 (CK-18) is indicative of liver injury and used as a surrogate biomarker covering MASLD to cirrhosis phenotypes. Autoantibodies against apolipoprotein A-1 (AAA-1) are known CVD risk factors inducing MASLD in vitro, but their role in modulating steatohepatitis and fibrosis in vivo is still elusive. We investigated AAA-1's contribution to systemic low-grade inflammation, liver steatosis, and fibrosis using a MASLD mouse model and a validated passive immunization protocol (PIP). Methods 10 weeks-old male C57BL/6J mice were fed a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) and immunized with AAA-1 (200ug) or control antibodies for ten days. At sacrifice, plasma samples were collected, and CK-18 levels, and proinflammatory cytokines were measured using the Mesoscale Discovery platform. Hematoxylin and Eosin and Sirius Red Fast Green staining were used to reveal liver steatosis and fibrosis, respectively. RNA were extracted from mouse liver samples and mRNA were then analyzed using a commercial fibrosis-specific genes panel (nCounter Human Fibrosis V2 Panel) and the nCounter Analysis System. Results Compared to control IgG recipients, CDAHFD mice injected with AAA-1 exhibited significantly elevated plasma levels of CK-18 (5.3 vs 2.1, p=0.031), IL-6 (13 vs 6.9, p=0.035), IL-10 (27.3 vs 9.8, p=0.007) and TNF-α (32.1 vs 24.2, p=0.032), and liver steatosis (93.4% vs 73.8%, p=0.007). Nanostring technology experiments revealed upregulation of several pro-fibrotic m-RNAs in liver tissue from AAA-1 immunized mice compared to the IgG immunized control group. These included Serum Amyloid A (Fold Change (FC) AAA-1 vs IgG : 2.5, p=0.02) , G-protein coupled receptors 65 (FC: 2.7, p=0.01), Periostin (FC: 2.6, p=0.001), Phospholipid transfer protein (FC: 2.2, p=0.0004), Vascular Cell Adhesion Molecule 1 (FC: 2.5, p=0.02) and Angiopoietin-like 4 (FC: 2.8, p=0.01). Histologically, the amount of liver fibrosis remained unaffected, probably due to the short time of the study. Conclusions Short AAA-1 PIP induced enhanced liver steatosis and inflammation accompanied by an elevation of the expression of several pro-fibrotic genes in CDAHFD mice, suggesting that AAA-1 may contribute to the transition from MASLD to MASH. Knowing whether increased exposure time to AAA-1 could lead to end stage disease (including cirrhosis and HCC), and if this could apply to humans as well warrant further investigations.

A comprehensive study of floodplain analysis utilising HEC-HMS, HEC-RAS, and GIS on the Kosasthalaiyar River sub-basin

ABSTRACT The present study focused on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, India. We analysed the 2015 peak flood and forecasted the feature data in the representative concentration pathways (RCP) 4.5 scenario for various years. We used the scientific data management system (SDSM) software to downscale the Geophysical Fluid Dynamics Laboratory (GFDL) 2.0 general circulation models (GCMs). According to hydrograph analysis, 142.7, 75.31, 461.73, and 248.22 mm runoff can occur in 2030, 2050, 2080, and 2100, respectively. The current study estimates probable peak flows by performing floodplain analysis on the Kosasthalaiyar River sub-basin using the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS), the Hydrologic Engineering Centre's River Analysis System (HEC-RAS), and geographic information system (GIS) tools. It is possible to observe that the two major peak floods, measuring 581.6 and 110.7 m3/s, respectively, will occur on 28 November 2030 at 10:20 a.m. and 12 December 2050 at 9:20 a.m. Additionally, high floods of 997 and 1,438.4 m3/s can be recorded on 20 December 2080 at 9:50 a.m. and 29 November 2100 at 9:40 a.m., respectively.

OS IMPACTOS DO TAPING NO PÓS-OPERATORIO EM ABDOMINOPLASTIA E LIPOASPIRAÇÃO: REVISÃO INTEGRATIVA

With a progressive increase in the number of plastic surgeries, the role of physiotherapy has been evolving exponentially. With this, it highlights the importance of using specific procedures in the postoperative period in order to reduce possible complications and reactions from surgery such as edema, seroma, fibrosis, hematoma, pain, scarring and tissue adhesions. The main objective of physiotherapeutic treatment in the preoperative period of plastic surgery is to avoid or reduce complications such as the incidence of scars, adhesions, edema, hematomas and pulmonary complications. The present study is an integrative literature review, carried out through an electronic search of the Medical Literature Analysis System (PudMed) Scientific Electronic Library Online (Scielo) database. This study can conclude with this study that the use of lymphatic taping in the postoperative period of abdominoplasty and liposuction is effective in reducing edema, preventing bruising and reducing the patient's pain, due to its action on the lymphatic system, thus having fewer complications. and complications.

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ENGLISH LANGUAGE TEACHING IN THE CONTEXT OF FORMAL AND INFORMAL HIGHER EDUCATION

This article considersthe impactof artificial intelligence on the process of teaching English in formal and informal higher education. The authors studythe use of various artificial intelligence technologies, such as machine learning, neural networks and automated speech analysis systems, to improve the effectiveness of teaching English to students, analyze the advantages and disadvantages of using artificial intelligence in teaching English, and offer recommendations for optimizing this process. The authors presentedresearchof the influence of artificial intelligence on the process of teaching English through a theoretical review of foreign experience and a questionnaire survey, the purpose of which was to study the opinions and views of students on the influence of artificial intelligence on the process of teaching English. The results of the researchmay be useful for teachers and students planning to introduceartificial intelligence into English language teaching.

The effect of knee braces on knee joint kinematics at different exercise speeds

ABSTRACT This study aims to investigate the influence of a new semi-rigid knee joint braces on kinematics using the Opti-knee knee joint kinematic analysis system at various exercise speeds. Twenty-four healthy young male adults were recruited for this study. Participants were randomly assessed while wearing and not wearing knee brace at four different speeds on a treadmill: normal walking (3.6 km/h), brisk walking (5.4 km/h), jogging (9 km/h), and moderate intensity running (10.8 km/h). Six degrees of freedom kinematic data from the knee joint were collected. Paired t-tests were conducted to calculate the kinematic differences. At speeds of 3.6 km/h and 5.4 km/h, wearing the knee brace led to reduced knee joint mobility in anterior-posterior translation, superior-inferior translation, internal-external rotation, and flexion-extension angles during the stance phase (P < 0.05). At 9 km/h, wearing the brace reduced knee joint internal-external rotation and flexion-extension mobility during the stance phase (P < 0.05). At 10.8 km/h, wearing the brace reduced knee joint flexion-extension mobility during the stance phase (P < 0.05). Wearing brace has significant influence on knee kinematics at four speeds, and the effect of brace varies with the speed of movement.

Comparing the Ground Reaction Forces, Toe Clearances, and Stride Lengths of Young and Older Adults Using a Novel Shoe Sensor System.

In this study, we developed a lightweight shoe sensor system equipped with four high-capacity, compact triaxial force sensors and an inertial measurement unit. Remarkably, this system enabled measurements of localized three-directional ground reaction forces (GRFs) at each sensor position (heel, first and fifth metatarsal heads, and toe) and estimations of stride length and toe clearance during walking. Compared to conventional optical motion analysis systems, the developed sensor system provided relatively accurate results for stride length and minimum toe clearance. To test the performance of the system, 15 older and 8 young adults were instructed to walk along a straight line while wearing the system. The results reveal that compared to the young adults, older adults exhibited lower localized GRF contributions from the heel and greater localized GRF contribution from the toe and fifth metatarsal locations. Furthermore, the older adults exhibited greater variability in their stride length and smaller toe clearance with greater variability compared to the young adults. These results underscore the effectiveness of the proposed gait analysis system in distinguishing the gait characteristics of young and older adults, potentially replacing traditional motion capture systems and force plates in gait analysis.

Out-of-distribution Detection in Dependent Data for Cyber-physical Systems with Conformal Guarantees

Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, Bonferroni Method, and so on, can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD .

Coupling solid phase microextraction to integrated optical sensors with microfluidic open interface

Increasing number of regulations today is very demanding, so many fields need to rely to screening techniques to separate samples whose content need to be confirmed with that field's gold standard approach. Therefore, the development of simple and fast approaches is very much needed. This manuscript presents the development of a microfluidic open interface integrated with ultraviolet-visible (UV–Vis) absorption detection for rapid screening applications. The system was designed as a rapid sensor readout for compounds enriched by solid-phase microextraction devices. Two azo dyes from a liquid water enhancer solution served as model analytes. These two dyes were extracted with the solid phase microextraction fiber and introduced to the system for analysis. Experimental results demonstrated that the system could effectively detect dyes with absorbance at 403 nm, indicating its potential for quantitating analytes with UV–Vis chromophores. Although this sensing method exhibits limited selectivity, it offers a cost-effective, straightforward approach to rapid screening that can be conveniently miniaturized for on-site applications. Further advances could focus on integrating low-cost sensors with specific responses in place of the UV–Vis unit, as well as simplification and miniaturization of the system to improve on-site analysis. Additionally, incorporating autosampler systems could enable high-throughput determinations, broadening the method's applicability. The developed solid phase microextraction method reliably samples and enriches small molecules from complex systems, delivering clean extracts to the selective sensor for readout without interference from the investigated sample matrix.

Effect of layered fertilizer strategies on rapeseed (Brassica napus L.) productivity and soil macropore characteristics under mechanical direct-sowing

Layered fertilization parameters affects crop root system configuration and growth distribution, which in turn affects soil pore properties and aggregate structure. Therefore, understanding the spatial distribution of the root system and soil pore space is helpful in choosing a reasonable fertilization ratio in production practice, which ensures high and stable crop yields and at the same time improves fertilizer utilization and soil health. Albeit the impact of layered fertilization ratio at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of layered fertilization ratio under on rapeseed root distribution and soil pore characteristics using the root analysis system and X-ray computed tomography (XCT). A new fertilization pattern that shallow-deep layer synchronized mechanical sowing was using, rotary tillage mixed fertilization in the shallow layer and 10 cm side-deep band fertilization in the deeper layer. It set five ratios of fertilizer amounts between shallow and deep layers as 0:4 (FD), 1:3 (FL), 2:2 (FM), 3:1 (FH), and 4:0 (F0), with non-fertilization (CK) as the control treatment. The results indicated that layered fertilization effectively improved the rape root conformation and spatial distribution, significantly increased total soil porosity and moisture content, and reduced soil bulk density and resistance (P < 0.05). Additionally, root configuration is closely related to soil pore structure and crop yield. Notably, compared with other treatments, the macroscopic porosity, equivalent pore diameter, hydraulic radius and roundness of FM treatment are significantly improved, and it effectively improves the yield and quality of rapeseed. Correlation analysis showed that the root system configuration parameters were negatively correlated with soil bulk density and resistance but positively correlated with soil moisture content, and macropore morphology parameters and rapeseed yield. Our study demonstrate that layered fertilization can improved rape growth and soil macropore porosity, but its effect depends on good tillage macropore characteristics and distribution created by reasonable fertilization ratio, which provided a theoretical basis for high-yield, efficient, green, and sustainable mechanized direct-sowing production of rapeseed.

Deep eutectic solvent as stationary phase for flow analysis: Automated trace metal determination in food products

BackgroundDeep eutectic solvents (DES) have emerged as effective solvents that address many challenges in analytical chemistry, particularly in microextraction. However, until now, their use has been primarily focused on extraction processes. This has significantly limited their application in analytical chemistry, especially in flow analysis, where the high viscosity of DES has made their use difficult. ResultsThis paper presents a novel DES-based liquid-liquid microextraction approach for the separation and determination of trace metals in foods using an automated flow analysis system. In this study, a DES composed of thymol and thionalide was first prepared and thoroughly characterized by spectroscopic (IR, NMR) and differential scanning calorimetry techniques. The COSMO-SAC model was employed to predict the solubility of metal salts (Cu, Cd, Pb, and Hg) in the new DES. The solvent was applied to glass fiber as a stationary phase in an extraction column in a flow analysis. After microwave digestion of food samples, metals were extracted by this DES in an automated mode and subsequently eluted with an aqueous thiourea solution. The procedure demonstrated limits of detection (LOD) of 6 μg kg−1 for mercury, 4 μg kg−1 for copper, 6 μg kg−1 for lead and 0.6 μg kg−1 for cadmium. SignificanceThis study represents the first application of a DES-based stationary phase in automated flow analysis, significantly enhancing extraction efficiency. The procedure enables precise and reliable determination of trace metals in food products, aligning with green chemistry principles by minimizing waste.