Forward Method Research Articles

Metasurfaces enabled dual-channel complex-amplitude hologram designed with neural network

In this work we present a polarization-multiplexing metasurface hologram designed with neural network, which can modulate the amplitude and phase of incident light with four nanopillars comprised unit structures with high efficiency. Then, to improve image quality by minimizing the difference between the complex amplitude field modulated by the structure and that obtained through the network, we developed an end-to-end framework-assisted multifunctional metasurface design method based on the aforementioned neural network, which can directly map geometric parameters of the metasurface to holographic images. The comparative results indicate that for the end-to-end framework, although the optimized distributions of complex amplitudes are limited by the selected finite number of structures, in the simulation results, higher-quality images can still be obtained compared to the forward design method. Full-wave simulation results show our proposed method can obtain higher quality holographic images compared to the GS algorithm. This work may open new possibilities in holographic displays and multifunctional optical devices.

Association between vertebral endplate defects and patient-reported symptoms: an immunohistochemical study investigating the COX-2/PGE-2/EP-4 axis

Background ContextVertebral endplate defects are often implicated in degenerative disc disorders, yet their connection to patient-reported symptoms remains unclear. COX-2 and PGE-2 are known for their roles in inflammation and pain, with EP-4 receptor involvement in pain signaling. Examining their expression in vertebral endplate tissues may provide insights into pathomechanism of low back pain. PurposeTo investigate the association between endplate defects and patient-reported symptoms and to further clarify the role of the COX-2/PGE-2/EP-4 axis in the pathogenesis of chronic low back pain. Study Design/SettingRetrospective study. Patient SampleA total of 71 patients who had undergone single-level L4/5 or L5/S1 modified laminectomy decompression preserving proximal upper laminae and transforaminal lumbar interbody fusion surgery were included in this study, including 18 patients diagnosed with lumbar disc herniation, 19 with lumbar disc herniation accompanied by degenerative lumbar spinal stenosis, and 34 with degenerative spondylolisthesis. Outcome MeasuresDemographic data, Pfirrmann grade, Modic changes, endplate defect score, visual analog scale (VAS) for back and leg pain, and Oswestry Disability Index (ODI) before surgery, 3-month and 6-month follow-up, and the percentage of immune-positive cells (COX-2, PGE-2, and EP-4) in endplate tissue sections. MethodsPatients were divided into defect and nondefect groups according to endplate morphology on lumbar MR. All intraoperative endplate specimens were immediately fixed in 10% formaldehyde, and then embedded in paraffin 3 days later for tissue sections. The outcome measures were compared between the defect group and nondefect group. Data were analyzed using independent t-tests and χ² tests. Pearson's rank correlation test was used to assess correlations between patient-reported symptoms and the percentage of immune-positive cells in the groups. Multivariable logistic regression models using the forward stepwise likelihood ratio method were used to identify the factors that were independently associated with endplate defects. ResultsThe age of Defect group was significantly higher than that of nondefect group (52.5±7.7 vs 57.2±9.1. p=.024). There were no significant differences in gender, diagnosis, BMI, comorbidities, or surgical level between the two groups. Modic changes (Type Ⅱ/Type Ⅲ) were more common in patients of Defect group than nondefect group (38.5% vs 11.1%, p<.001), and so was disc degeneration (Pfirrmann grade Ⅳ/Ⅴ) (69.2% vs 33.3%, p<.001). Defect group had significantly higher VAS-Back (6.5±2.0 vs 4.9±1.6, p<.001) and ODI scores (62.9±10.7 vs 45.2±14.8, p<.001) than nondefect group, while there was no significant differences between the two groups during the 3 and 6-month follow-up after surgery. Histologically, Defect group was characterized by upregulation of COX-2, PGE-2, and EP-4 in endplate tissue sections. Both in defect and nondefect groups, VAS-Back showed moderate positive correlations with the expressions of COX-2 (r=0.643; r=0.558, p both<.001), PGE-2 (r=0.611; r=0.640, p both<.001), and EP-4 (r=0.643; r=0.563, p both<.001). Multivariate regression analyses reveled that percentage of COX-2-positive cells was associated with endplate defects (OR=1.509, 95%CI [1.048–2.171], p=.027), as well as percentage of PGE-2-positive (OR=1.291, 95%CI [1.106–1.508], p=.001) and EP-4-positive cells (OR=1.284, 95%CI [1.048∼2.171], p=.003). ConclusionsPatients with endplate defects had worse quality of life, more severe disc degeneration and Modic changes, and up-regulated COX-2/PGE-2/EP-4 axis expression in cartilage endplates in patients with defected endplates. Inflammatory factors may significantly contribute to the onset and progression of chronic low back pain in patients with endplate defects, consequently impacting patient-reported symptoms.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Research on teaching quality evaluation of higher vocational architecture majors based on enterprise platform with spherical fuzzy MAGDM

Abstract Teaching quality evaluation is a process of evaluating the teaching quality of architectural majors. It can not only evaluate the teaching level of teachers, but also evaluate the learning effectiveness of students. Therefore, this study designs a teaching quality evaluation system for architecture majors based on fuzzy environment, in order to provide direction guidance for effectively evaluating the teaching quality of architecture majors by using this research. The teaching quality evaluation of higher vocational architecture majors based on enterprise platform is a multiple-attribute group decision-making (MAGDM). The spherical fuzzy sets (SFSs) provide more free space for decision makers to portray uncertain information during the teaching quality evaluation of higher vocational architecture majors based on enterprise platform. Therefore, this study expands the partitioned Maclaurin symmetric mean operator and induced ordered weighted average operator to SFSs based on the power average technique and construct induced spherical fuzzy power partitioned MSM (I-SFPPMSM) technique. Subsequently, a novel MAGDM method is put forward based on I-SFPPMSM technique and spherical fuzzy number weighted geometric technique under SFSs. Finally, a numerical example for teaching quality evaluation of higher vocational architecture majors based on enterprise platform is employed to verify the put forward method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFPPMSM technique.

Interannual variations in the seasonal cycle of extreme precipitation in Germany and the response to climate change

Abstract. Annual maxima of daily precipitation sums can be typically described well with a stationary generalized extreme value (GEV) distribution. In many regions of the world, such a description does also work well for monthly maxima for a given month of the year. However, the description of seasonal and interannual variations requires the use of non-stationary models. Therefore, in this paper we propose a non-stationary modeling strategy applied to long time series from rain gauges in Germany. Seasonal variations in the GEV parameters are modeled with a series of harmonic functions and interannual variations with higher-order orthogonal polynomials. By including interactions between the terms, we allow for the seasonal cycle to change with time. Frequently, the shape parameter ξ of the GEV is estimated as a constant value also in otherwise instationary models. Here, we allow for seasonal–interannual variations and find that this is beneficial. A suitable model for each time series is selected with a stepwise forward regression method using the Bayesian information criterion (BIC). A cross-validated verification with the quantile skill score (QSS) and its decomposition reveals a performance gain of seasonally–interannually varying return levels with respect to a model allowing for seasonal variations only. Some evidence can be found that the impact of climate change on extreme precipitation in Germany can be detected, whereas changes are regionally very different. In general, an increase in return levels is more prevalent than a decrease. The median of the extreme precipitation distribution (2-year return level) generally increases during spring and autumn and is shifted to later times in the year; heavy precipitation (100-year return level) rises mainly in summer and occurs earlier in the year.

Forward and inversion approach for direct current resistivity based on an unstructured mesh and its application to tunnel engineering

AbstractThe accurate identification of water‐bearing structures is urgently required for the safe construction of tunnel engineering. Currently, the direct current resistivity method is an effective method for detecting water‐bearing structures in tunnels. In the advanced detection of the direct current resistivity based on the finite element method, the traditional hexahedron mesh performs poorly for the discretization of models of complex tunnel structure sections such as horseshoe‐shaped and round sections. Therefore, this study adopts unstructured grid generation technology combining tetrahedra and hexahedra to achieve more accurate modelling of complex structures, such as round and horseshoe‐shaped sections, and establishes a forward modelling method of the direct current resistivity in tunnels based on an unstructured mesh. The maximum error between the numerical simulation and theoretical results for an infinite tabular body in full space is less than 0.8%. It is more complicated to calculate the sensitivity matrix and model constraint term for the inversion region containing two types of grid than for one. For this purpose, the sensitivity matrix of different types of grid areas is calculated, a model constraint term based on the dual constraints of volume and distance is constructed, and finally, a partitioned domain‐weighted least‐squares inversion method based on an unstructured mesh is proposed. Synthetic examples of typical water‐bearing structures are analysed, and the results show that the proposed forward and inverse methods of the direct current resistivity in tunnels based on an unstructured mesh can effectively capture the position and morphology of the water‐bearing structure. Finally, an on‐site application was conducted in the Yellow River Diversion Project in central Shanxi. The proposed method could effectively identify the water body in front of the tunnel face and guide the on‐site construction of the project. These results can improve the interpretation of the direct current resistivity data in tunnels and play a positive role in promoting the use of the direct current resistivity method to prevent and control water‐inrush disasters in tunnels with complex structures.

Integrated management of abatement technology investment and resource extraction

As the need for increased renewable energy generation to meet climate goals grows, so does the demand for critical minerals in the industrial sector. This raises concerns about the availability of supply and the reliability of investment in critical minerals. Based on this, the study proposes an analytical framework for the integrated management of mineral extraction and renewable energy investments. Different from the previous strategy of separate management, this study has established an integrated model that utilizes new energy technology as the downstream application of emission reduction technology. It also utilizes copper, zinc, aluminum, rare earth elements, and other materials as the upstream raw material supply. In terms of minimizing costs, the entire supply chain process is optimized to achieve an empirical case. In addition, considering the significant uncertainty in the process of reducing emissions from new energy technologies, this study also incorporated a forward control stochastic programming method to model and solve it. We obtained confidence intervals for optimal technological investment paths and metal mining paths under various scenarios. Our findings indicate that: (1) Biomass, as a renewable energy technology with the lowest resource demand, should be prioritized for development. However, there is significant uncertainty surrounding the installed capacity of photovoltaics. (2) Copper ore reserves have little impact on the development of renewable energy technologies, while zinc ore and aluminum ore have a greater impact on wind energy and photovoltaics, respectively. (3) By optimizing the cut-off ratio of raw ore under a specific amount of ore storage, we can ensure the availability of materials needed for downstream energy technology.

Partial derivative-based dynamic sensitivity analysis expression for non-linear auto regressive with exogenous (NARX) model[sbnd]case studies on distillation columns and model's interpretation investigation

Constructing the reliable dynamic sensitivity profile for the output variable using the machine learning model is a challenging task; however, the dynamic sensitivity trends are helpful to understand the impact of the input variables on the system's performance. In this paper, we have derived the partial-derivative approach-based sensitivity analysis expression for the non-linear auto regressive with exogenous (NARX) model for the first time. The engineering systems-based case studies, i.e., two distillation columns with five and ten stages, respectively are taken which are commonly found in the chemical processing plants. Two output variables, i.e., liquid composition in tray 2 and tray 4 (Y2 and Y4) of a five-stage distillation column, and liquid composition in tray 7 (Y7) of a ten-stage (higher) distillation column are modelled by NARX with respect to time, feed concentration (Xf) and feed flow rate (Lf). The dynamic sensitivity profiles of the output variables with respect to Xf and Lf for the two distillation columns are plotted by the derived partial derivative-based sensitivity expression on the NARX model. Furthermore, the forward difference method of sensitivity analysis (first principle method) is also applied on the ordinary differential equations of the distillation columns to compute the sensitivity values of the output variables. A good agreement in the dynamic sensitivity values of the output variables with respect to the input variables is found for the two sensitivity analysis techniques thereby demonstrating the effectiveness of the partial-derivative approach for the improved NARX's interpretability performance. This research presents the explicit partial-derivative based sensitivity analysis expression for the NARX model which can be utilised for time-series applications and can provide the insights about the model's interpretation performance.

SABRE-hyperpolarization dynamics of [1-13C]pyruvate monitored by in situ zero- to ultra-low field NMR

Hyperpolarized [1–13C]pyruvate is the leading metabolite used in the emerging field of hyperpolarization-enhanced MRI. Signal amplification by reversible exchange (SABRE) is a straight forward hyperpolarization method that has recently been shown to hyperpolarize [1–13C]pyruvate at low (microtesla and below) magnetic fields. Here, we show that commercial optical magnetometers with Rb-vapor media can be used to readily monitor the build-up and decay of the hyperpolarized MR signal. In addition, we measure ZULF-NMR spectra in various conditions, ranging from a J-coupling-dominated regime transitioning into a Zeeman-dominated regime when going from a sub-nT field to a µT field. The experimentally acquired spectra are matched well by numerical simulations.

Non-invasive calibration of a Stewart platform by photogrammetry

AbstractAccurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg (DH) convention and used the Stewart platform “Tiger 66.1” developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per least-square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform’s center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The target poses and actual poses are then compared, and the error compensations are estimated using the least-square methods to calculate the predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements. Given that “Tiger 66.1” is based on the general Stewart platform structure, the proposed calibration method holds promise for extension to machines operating on similar principles where non-invasive calibration is desirable. This study contributes to advancing the field of Stewart platform calibration, paving the way for more precise and efficient applications in various domains.

Improving innovation and entrepreneurship education in higher institutions: the impact of “Mass Entrepreneurship and Innovation” education on undergraduates’ entrepreneurial intentions

The exploration of the way "mass entrepreneurship and innovation" (MEI) education influences students' aspirations to become entrepreneurs has grown into an important area of analysis in studies related to higher education. This research intends to examine the consequences of MEI education on students' tendency towards entrepreneurship, and to put forward methods for augmenting the teaching of innovation and entrepreneurship in private higher educational establishments.To achieve this objective, questionnaires and semi-structured interviews were employed in the study, which involved a total of 197 students and five education experts. The statistical analysis of the questionnaire data revealed that MEI education was positively related to students' entrepreneurial intentions, and that both entrepreneurial experience and family entrepreneurial background played moderating roles in this relationship. The interview findings indicated that private universities could enhance educational reforms by designing talent training programs, developing diversified curricula, and developing more professional entrepreneurial platforms to encourage students' entrepreneurial intentions. This study offers fresh insights for improving and perfecting the mechanism of innovation and entrepreneurship education in private universities.

Forward Modeling of 3‐D Ion Properties in Jupiter’s Magnetosphere Using Juno/JADE‐I Data

AbstractThe Jovian Auroral Distributions Experiment Ion sensor (JADE‐I) on NASA’s Juno mission provides in‐situ measurements of ions from 0.1 to 46.2 keV/q inside Jupiter’s magnetosphere. JADE‐I is used to study the plasma with two types of datasets from the same measurement: Time‐of‐flight (TOF) and SPECIES. The TOF dataset provides mass‐per‐charge measurements with a range of 1–64 amu/q but oversamples particles over 6π steradian viewing per spacecraft spin and has little directional information. On the other hand, the SPECIES dataset can provide a good measurement of the flow direction but does not provide mass‐per‐charge information due to the telemetry limit. In this study, we developed a 2‐step forward modeling method that combines the advantages and avoids the disadvantages of TOF and SPECIES data to derive the 3‐D properties of heavy ions. Assuming that the ion velocity distribution can be described with the kappa distribution, we first perform the forward model fit of the TOF data to calculate the relative abundance of heavy ion species. Then we fix the relative abundance and perform the second forward model fit on the SPECIES data. Using this method, we obtain the densities of different heavy ions, the shared temperature and kappa value, and the 3‐D flow velocity vector. Some data examples of the equatorial plasma disk before Perijove 24 are included to demonstrate the method. Plasma properties can then be mapped to explore spatial and temporal variabilities in Jupiter’s magnetosphere.

PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial Networks

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6 G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transport Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PEPesc</i> which has two distinctive features. First, it features <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retransmission-free</i> loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iperf</i> and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

Forward modeling of spectral gamma-ray (SGR) logging in sedimentary formations

We propose a new approach to improve spectral gamma-ray (SGR) logging forward modeling by considering the radioactive minerals present in the rock as gamma-ray sources. This is based on the radioactive attenuation theory. The assumptions are: 1) minerals with K40, U238, and Th232 content are considered radioactive sources uniformly distributed in the rock; 2) the measured radioactivity is proportional to the concentration of radioactive minerals and inversely proportional to the rock bulk density; 3) the radioactivity is only attenuated by absorption of gamma-rays. The forward modeling was tested using a synthetic case of sandstone with clay minerals and brine-saturated pores to analyze the sensitivity of SGR to changes in illite/smectite-illite/mica ratios and sandstone porosities. Finally, it was further validated with 44 core samples, of which 22 are from two shale gas and 22 from two clastic formations. The Pearson correlation coefficient applied to measure the misfit between the simulated and observed K, U, Th, and SGR data attained values of 0.82, 0.83, 0.61, and 0.57, respectively. A further improvement to 0.87, 0.85, 0.65, and 0.69 was achieved when applying joint inversion for data where illite/smectite and illite/mica ratios were not specified. The correlation between the simulated and observed data supports the viability of the proposed SGR forward modeling approach method.

MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.

Advancing Die Design and Optimization through the Combination Approach: A Focus on Spring-back Compensation

This study explores the geometrical deviations that occur in cold stamping, which are caused by the spring-back effects in elastic metals after forming. The primary aim is to introduce and assess the Combination Approach. This approach seamlessly integrates the Displacement Adjustment and Spring Forward methods for die compensation. This multi-stage process meticulously corrects dimensional errors and effectively reduces spring back through iterative processes. Using the NUMISHEET model, numerical simulations have shown a significant reduction in spring back errors, with up to a 55% reduction achieved with optimized die surfaces after five iterations. The proposed Combination Approach improves the accuracy of die design and contributes significantly to the stamping industry by incorporating established techniques within finite element software. This research broadens the scope of die compensation strategies, creating new opportunities for achieving precise die design in stamping.

FGeo-SSS: A Search-Based Symbolic Solver for Human-like Automated Geometric Reasoning

Geometric problem solving (GPS) has always been a long-standing challenge in the fields of automated reasoning. Its problem representation and solution process embody rich symmetry. This paper is the second in a series of our works. Based on the Geometry Formalization Theory and the FormalGeo geometric formal system, we have developed the Formal Geometric Problem Solver (FGPS) in Python 3.10, which can serve as an interactive assistant or as an automated problem solver. FGPS is capable of executing geometric predicate logic and performing relational reasoning and algebraic computation, ultimately achieving readable, traceable, and verifiable automated solutions for geometric problems. We observed that symmetry phenomena exist at various levels within FGPS and utilized these symmetries to further refine the system’s design. FGPS employs symbols to represent geometric shapes and transforms various geometric patterns into a set of symbolic operation rules. This maintains symmetry in basic transformations, shape constructions, and the application of theorems. Moreover, we also have annotated the formalgeo7k dataset, which contains 6981 geometry problems with detailed formal language descriptions and solutions. Experiments on formalgeo7k validate the correctness and utility of the FGPS. The forward search method with random strategy achieved a 39.71% problem-solving success rate.

A Risk-Structured Model for the Transmission Dynamics of Anthrax Disease

Anthrax, a zoonotic disease with serious public health consequences, has been the subject of rigorous mathematical and statistical modeling to better understand its dynamics and to devise effective control techniques. In this study, we propose a novel mathematical risk-structured model for anthrax disease spread that includes both qualitative and quantitative evaluations. Our research focuses on the complex interplay between host–anthrax interactions and zoonotic transmission. Our mathematical approach incorporates bifurcation analysis and stability considerations. We investigate the dynamic behavior of the proposed model under various settings, shedding light on the important parameters that determine anthrax transmission and persistence. The normalized forward sensitivity analysis method is used to determine the parameters that are relevant to reducing Rc and, by extension, disease spread. Through scenario simulation of our model, we identify intervention techniques, such as enlightenment of the populace, that will effectively minimize disease transmission. Our findings provide insights into anthrax epidemiology and emphasize the importance of effective disease management. Bifurcation investigations reveal the existence and stability of numerous equilibria, allowing for a better understanding of the behavior of the system under various scenarios. This study adds to the field of anthrax modeling by providing a foundation for informed decision-making regarding public health measures. The use of a mathematical modeling approach improves our ability to anticipate and control anthrax epidemics, ultimately helping to protect both human and animal populations.

Validation study of the Amharic version Safety Attitudes Questionnaire (SAQ) in public hospitals of Addis Ababa, Ethiopia: a cross-sectional study

BackgroundIn Ethiopia, there is a growing concern about improving patients’ safety in healthcare facilities. However, the lack of a valid and reliable instrument sensitive to the Ethiopian culture for measuring health professional practice environment leads to difficulty in constructing evaluations of safety climate and further linking organizational research to outcomes research. This research study examined the psychometric properties of the Safety Attitude Questionnaire (SAQ) in the Amharic language within an Ethiopian healthcare context.MethodA hospital-based cross-sectional study design was conducted. The SAQ was meticulously translated into Amharic using forward and backward translation methods. Content validity was evaluated with input from seven patient safety and healthcare quality experts. Face validity was established through feedback from healthcare professionals. Then, the Amharic SAQ (SAQ-A) was distributed to 648 participants working in 11 public hospitals, and a total of 611 valid questionnaires were completed and returned (95.2% response rate). Cronbach’s alpha, McDonald’s omega, composite reliability, correlation analysis, and average variance estimation were calculated, and confirmatory factor analysis was performed. Descriptive analyses were performed to describe socio-demographic characteristics. A P-value of ≤0.05 was considered statistically significant. Tables, figures, charts, and texts are used for data presentation.ResultThe overall internal consistency (Cronbach’s alpha) for the 31-item SAQ-A was 0.903, indicating excellent reliability. Confirmatory factor analyses demonstrated a good model fit for each dimension and the entire construct (χ2=1086.675, df=412, p<0.001, comparative fit index (CFI)=0.923, Tucker Lewis index (TLI)=0.913, and root mean square error of approximation (RMSEA)=0.052). The positive response rate of healthcare workers in hospitals was 32.1%. The positive response rates of the six dimensions were teamwork climate (59.7%), safety climate (41.9%), job satisfaction (57.1%), working conditions (37.5%), perception of management (37.6%), and stress recognition (46.2%).ConclusionThe Amharic translation of the SAQ showed good psychometric properties, making it a valuable tool for assessing safety attitudes among Amharic-speaking Ethiopian healthcare practitioners.

Performance of Distress Thermometer: A Study among Cancer Patients.

The Distress Thermometer accompanied with Problems List is a commonly used screening tool for psychosocial distress. However, it's cut-off score, performance and risk factors for psychosocial distress varies among studies. This is the first study conducted in Nepal to investigate the Distress Thermometer's screening properties, its optimal cut-off score and evaluating the prevalence of psychosocial distress and its risk factors. This cross-sectional study enrolled 162 heterogeneous cancer patients. The English form of the Distress Thermometer was translated to Nepali using a forward and backward translation method. Questionnaires including socio-demographic, clinical characteristics, the Hospital Anxiety and Depression Scale and Distress Thermometer accompanied with Problems List were filled. Receiver Operating Characteristic analysis of distress thermometer scores was evaluated against Hospital Anxiety and Depression Scale-Total (≥15). An Area Under the Curve, sensitivity, specificity, positive predictive value and negative predictive value were calculated at each Distress Thermometer cut-off score. Receiver Operating Characteristic analysis showed an excellent discriminating performance (Area Under the Curve =87.4%). A cut-off score of 4 on Distress Thermometer was established and it yielded sensitivity (88.9%), specificity (71.1%), positive predictive value (75.4%) and negative predictive value (86.5%) respectively. Furthermore, 55.6% of participants were distressed and emotional problems (odd ratio = 28.00), practical problems (odd ratio = 12.152) and physical problems (odd ratio = 2.397) were found to be significant risk factors for PD. PD is a global burden in cancer patients. The DT with a cut-off score of 4 accompanied with PL is valid instrument for screening PD in Nepali cancer patients. PL identified the problems that causes of PD.