Deviation Index Research Articles

Numerical assessment of the performance of a new multi side-hole catheter design in liver radioembolization.

Numerical fluid-particle dynamics simulations, such as Computational Fluid Dynamics (CFD), are widely used to study blood flow and microsphere transport in medical treatments like radioembolization (RE), a therapy for unresectable liver tumors. RE involves injecting 90Y-labeled microspheres via a microcatheter into a hepatic artery to irradiate cancer cells. This study uses simulations on three patient-specific hepatic arteries to evaluate microsphere distribution in RE, focusing on two catheter designs: a standard end-hole catheter (EHC) and a novel multi side-hole catheter (SHC). Key parameters include cancer scenario and catheter tip position. Three performance indexes are introduced: the matching deviation index (MDI), targeting deviation index (TDI), and tumor-flow deviation index (TFDI). Results show the SHC outperforms the EHC with better MDI (3.67 vs. 8.36 percent points) and TDI (6.04 vs. 10.24 percent points) values, suggesting the SHC's cross-flow effect improves microsphere dispersion and alignment with flow split. The TDI values for SHC often match the TFDI, making the latter a potential performance predictor. The SHC demonstrates superior performance when tumors are located downstream from the catheter tip, while for localized tumors, superselective treatment is recommended, minimizing the catheter's impact on therapy.

Correlation of BP180, BP230, and type VII collagen antibody titers in serum, blister fluid, erosion, and saliva in pemphigoid diseases.

Autoantibodies, including anti-BP180 and anti-BP230 antibodies in bullous pemphigoid (BP) and mucous membrane pemphigoid (MMP), and anti-type VII collagen (COL7) antibodies in epidermolysis bullosa acquisita (EBA), are well characterized. Enzyme-linked immunosorbent assays (ELISAs) for the detection of these antibodies in the serum are valuable for diagnosis. Previous studies have indicated that anti-BP180 and anti-BP230 antibodies in blister fluid and saliva can be detected using ELISA. The aim of this study was to detect anti-BP180, anti-BP230, and anti-COL7 antibodies in several types of samples, including blister fluid, an erosive surface swab, and saliva, by ELISAs and to compare the titers of these antibodies between different types of samples. Thirty-three patients with BP/MMP, two patients with EBA, and 13 patients with non-pemphigoid diseases were included in this study. Samples were collected from each patient and analyzed using commercial ELISA kits. In the cases of BP/MMP, the mean ± standard deviation indices of BP180 ELISA were 411.9 ± 538.3, 253.5 ± 333.3, 2.360 ± 1.714, and 1.599 ± 1.262, and those of BP230 ELISA were 27.73 ± 37.65, 20.52 ± 30.04, 0.486 ± 0.7915, and 0.346 ± 0.4373, from sera, blister fluids, erosive surface swabs, and saliva, respectively. The indices of BP180 and BP230 ELISAs using serum were significantly correlated with those of BP180 and BP230 ELISAs using blister fluid (r = 0.9342 and r = 0.9882), an erosive surface swab (r = 0.7586 and r = 0.9332), and saliva (r = 0.5731 and r = 0.6147), respectively. This study revealed that autoantibodies were detectable in blister fluids, erosive surfaces, and saliva, and their correlation with serum titers in pemphigoid diseases was highest in blister fluid. In summary, blister fluid can be a minimally invasive sample alternative to serum for the detection of pemphigoid-related antibodies.

Explicit Analytical Form for the Average Run Length of Double-Modified Exponentially Weighted Moving Average Control Charts Through the MA(q) Process and Applications

The Statistical Process Control (SPC) approach using mathematical modeling proves effective for correlated data, with applications in healthcare, finance, and technology to enhance quality and efficiency. Here, we provide a novel SPC method using mathematical modeling and discuss its use in simulation tests to assess its applicability for tracking processes containing correlated data operating on sophisticated control charts. Particularly, an approach for detecting small shifts in the mean of a process running on the double-modified exponentially weighted moving average (DMEWMA) control chart, which is symmetric about the center line with upper and lower control limits, is of special interest. The computations showed exceptional accuracy, with ARL from the explicit formula closely matching that from the NIE method. Simulation tests assess its applicability in detecting small mean shifts and compare its performance with exponentially weighted moving average (EWMA) and modified exponentially weighted moving average (MEWMA) control charts across various scenarios. For several values of the design parameters, the performances of these three control charts are also compared in terms of the relative average index and relative standard deviation index. The results show that the DMEWMA chart outperforms others for several process mean shifts. The method’s practical use is demonstrated with stock data, highlighting its superior effectiveness in enhancing process monitoring.

Integrating hydro-geomorphological adjustments into flood mapping for enhanced risk assessment

Accurate flood risk assessment is essential for effective risk management, especially as extreme rainfall trends have increased flood frequency and inundation globally. However, rainfall data alone often becomes insufficient in dynamic floodplains, where geomorphological changes are critical. The Shilabati River basin in West Bengal, India, illustrates such a landscape, having experienced severe floods in 1959, 1967, 1968, 1973, 1978, 1999, 2000, 2007, 2013, and 2021. For this basin, mapping landform variability is key to understanding erosion and landscape denudation processes. The Digital Elevation Model (DEM)-based technique known as DEM of Difference (DoD) enables the detection of terrain changes over time by comparing DEMs from distinct periods, offering insight into floodplain erosion. However, DEM-based analysis is prone to spatially correlated errors, which can propagate false topographic features, hindering accurate flood mapping. Our study employs the Elevation Deviation Index (EDI), a DEM comparison method that normalizes and mitigates spatially correlated errors across terrain. Using EDI, we mapped geomorphological changes in the Shilabati floodplain, revealing substantial erosion and elevation loss. Our findings demonstrate a strong positive correlation between EDI values and increased flood extent. These results underscore the importance of incorporating geomorphological analysis in flood risk assessment, as hydrological data alone may overlook critical terrain changes. We recommend a Preliminary Flood Risk Assessment (PFRA) to strengthen flood resilience in the region, targeting vulnerable zones for enhanced, adaptive flood mitigation strategies.

A study on misfire control using crank angular velocity combustion feedback considering in-cylinder gas properties

Internal combustion engines have been strongly required to improve thermal efficiency and utilize a variety of fuels derived from renewable energy sources to achieve carbon neutrality. In terms of engine development, the stable control of ignition and combustion is the primary challenge in adapting to diverse fuels for the recently emphasized premixed compression ignition combustion and super lean burn. In this study, a misfire index was used as a detector for a premixed type of diesel combustion engine. Combustion feedback control was performed to control the target value of EGR based on the difference between the misfire index and its target value. To apply crank angle velocity to feedback control, this study implemented corrections and limitations to the misfire index based on in-cylinder gas properties. This was made possible because, during misfire events, the in-cylinder pressure at top dead center (TDC) is not influenced by combustion pressure. Additionally, this research enabled model validation of the phenomena for the first time. A novel structure was also established to learn the EGR correction amount from the deviation of the misfire index, marking the first empirical demonstration of its effectiveness. This research not only presents control examples for misfire countermeasures, which are crucial for the development of engines capable of utilizing diverse fuels but also contributed to mass production and practical application.

Are Electromyography Data a Fingerprint for Patients with Cerebral Palsy (CP)?

This study aimed to first investigate changes in electromyography (EMG) patterns after multilevel surgical treatment in patients with cerebral palsy (CP) and then to assess the connection between the measure of EMG and motor control indices and surgery outcomes. We analyzed retrospective EMG and gait data from 167 patients with CP before and after surgery and from 117 typically developed individuals as a reference group. The patients underwent at least one soft tissue surgery on their shank and foot muscles. Using Repeated Measures ANOVA, we examined the norm-distance (ND) of the kinematics, kinetics, and EMG patterns, in addition to the Kerpape-Rennes EMG-based Gait Index (EDI), EMG Profile Score (EPS), and Walking Dynamic Motor Control Index (DMC) before and after surgery. Participants were divided into different response groups (poor, mild, and good gait quality) according to their pre- and post-treatment Gait Deviation Index (GDI), using the K-means-PSO clustering algorithm. The gait and EMG indices were compared between the responders using the nonparametric Mann–Whitney test. The ND for all kinematics and kinetics parameters significantly improved (p-value < 0.05) after the surgery. Regarding EMG, a significant reduction was only observed in the ND of the rectus femoris (p-value < 0.001) and soleus (p-value = 0.006). Among the indices, DMC was not altered post-operatively (p-value = 0.88). Although EDI and EPS were consistent across responders with a similar pre-treatment gait, a higher DMC was significantly associated with a greater improvement, particularly in patients with poor gait (p-value < 0.05). These findings indicate systematic changes in the EMG of patients with CP following surgery, which can also be demonstrated through indices. DMC is a measure that can potentially serve as a partial predictor of outcomes, particularly in patients with poor pre-operative gait. Future research should investigate the effects of different surgical strategies on the improvement of these patients.

Reproducibility and repeatability of 18F-(2S, 4R)-4-fluoroglutamine PET imaging in preclinical oncology models.

Measurement of repeatability and reproducibility (R&R) is necessary to realize the full potential of positron emission tomography (PET). Several studies have evaluated the reproducibility of PET using 18F-FDG, the most common PET tracer used in oncology, but similar studies using other PET tracers are scarce. Even fewer assess agreement and R&R with statistical methods designed explicitly for the task. 18F-(2S, 4R)-4-fluoro-glutamine (18F-Gln) is a PET tracer designed for imaging glutamine uptake and metabolism. This study illustrates high reproducibility and repeatability with 18F-Gln for in vivo research. Twenty mice bearing colorectal cancer cell line xenografts were injected with ~9 MBq of 18F-Gln and imaged in an Inveon microPET. Three individuals analyzed the tumor uptake of 18F-Gln using the same set of images, the same image analysis software, and the same analysis method. Scans were randomly re-ordered for a second repeatability measurement 6 months later. Statistical analyses were performed using the methods of Bland and Altman (B&A), Gauge Reproducibility and Repeatability (Gauge R&R), and Lin's Concordance Correlation Coefficient. A comprehensive equivalency test, designed to reject a null hypothesis of non-equivalence, was also conducted. In a two-way random effects Gauge R&R model, variance among mice and their measurement variance were 0.5717 and 0.024. Reproducibility and repeatability accounted for 31% and 69% of the total measurement error, respectively. B&A repeatability coefficients for analysts 1, 2, and 3 were 0.16, 0.35, and 0.49. One-half B&A agreement limits between analysts 1 and 2, 1 and 3, and 2 and 3 were 0.27, 0.47, and 0.47, respectively. The mean square deviation and total deviation index were lowest for analysts 1 and 2, while coverage probabilities and coefficients of the individual agreement were highest. Finally, the definitive agreement inference hypothesis test for equivalency demonstrated that all three confidence intervals for the average difference of means from repeated measures lie within our a priori limits of equivalence (i.e. ± 0.5%ID/g). Our data indicate high individual analyst and laboratory-level reproducibility and repeatability. The assessment of R&R using the appropriate methods is critical and should be adopted by the broader imaging community.

Three-Dimensional Gait Analysis of School-Age Children With Angelman Syndrome: A Case-Control Study.

Gait disturbance is a common motor symptom in Angelman syndrome (AS), but its characteristics have been poorly studied quantitatively. This study aimed to analyze gait characteristics in school-age children with AS using three-dimensional gait analysis (3DGA). Patients with clinically and genetically confirmed AS and healthy children aged 6-15 years were included. For gait assessments, 3DGA was performed using an eight-camera motion analysis system and eight force plates. Gait metrics, including gait speed, step length, step width, gait variability, gait deviation index, and kinematic and kinetic data of lower extremity joints were compared between the groups. Eight children with AS and 24 healthy controls were evaluated. Seven children with AS had flat feet. While step length and gait speed were similar between groups, children with AS showed greater variability in these parameters and larger step widths. Their average gait deviation index was 74.5, indicating significant gait disturbance, and characteristic features included anterior pelvic tilt, insufficient hip extension, excessive knee flexion during early stance, and reduced ankle joint power. School-age children with AS exhibit unstable, prancing gait characterized by knee flexion in the early stance phase, quantifiable using 3DGA. These findings provide foundation for evaluating therapeutic interventions.

Adaptive quality indicator for subband multi-baseline TomoSAR filter

ABSTRACT The development of Synthetic Aperture Radar (SAR) techniques has been ongoing for a few decades due to the continuous expansion of SAR technologies. However, the source of decorrelations among other noise artefacts constitutes one of the main limitations to all interferometric-based processes including Tomography (TomoSAR). As a consequence, the interpretation of generated height maps and 3D point clouds is challenging, particularly with the difficulty of obtaining accurate in-situ measurements for validation. To address this issue in urban areas, a few works proposed the use of a filtering approach in order to facilitate the application of TomoSAR inversion or detection methods. At our end, we propose an improved version of multi-baseline Goldstein-based filter through its application in the wavelet domain, on the one hand, and the adaptive estimation of the alpha parameter, on the other hand. The generated height maps and point clouds using a limited number of images acquired by TerraSAR-X sensor are evaluated qualitatively and quantitatively. Edge preservation index and standard deviation showed the effectiveness of the approach with respect to the filtering criteria, while the R-squared and detection rate illustrated its efficiency in terms of height estimation. Both assessments demonstrated the performance of the proposed denoising methodology.

Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss

In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work.

Glaucoma detection and staging from visual field images using machine learning techniques.

In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters. A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model's result. The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%. The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening.

Quality assessment of clinical guidelines in the care of laryngitis and pharyngitis according to AGREE II

ABSTRACT Purpose The study aimed to identify and assess the methodological quality of essential clinical guidelines for the management of laryngitis and pharyngitis. Methods A systematic search of clinical guidelines for the management of laryngitis and pharyngitis was performed in three databases. Methodological quality was assessed according to AGREE II, in which each item in its domains was scored by four independent evaluators. To determine the agreement, a weighted Kappa square statistic calculation was performed. Results 81 studies were found in the bibliographic sources consulted and all were evaluated. Considering the exclusion criteria, seven guidelines were selected for final evaluation by AGREE II. The squared weighted Kappa coefficient calculated after the first round of evaluation by AGREE II was 0.85, within an almost perfect agreement rate. The domains “stakeholder engagement”, “applicability”, and “editorial independence” had the lowest mean scores and the highest standard deviation indices. They had, respectively, a mean score of (63.7%) with a standard deviation of (17%), a mean score of (65.7%) with a standard deviation of (22%), and a mean score of (35%) with a standard deviation of (23%). The use of penicillin, erythromycin, ampicillin, amoxicillin, azithromycin and clarithromycin were recommended in (75%) of all guidelines that presented pharmacological measures. As a non-pharmacological measure, oral rehydration and gargling were recommended by two guidelines. Conclusion The statistical results indicate that all guidelines were considered as recommended. However, no uniformity was observed in the recommendations of these seven guidelines with regard to non-pharmacological and pharmacological treatment.

Similarity or complementarity? Understanding marital relationships in terms of sexual dimorphism in brain morphometry and gender roles.

"Birds of a feather flock together" and "opposites attract" are two contrasting statements regarding interpersonal relationships. Sex differences provide a theoretical integration of these two conflicting statements. Here, we explored the relationship between marital satisfaction and sex differences in social attributes and neuroanatomical characteristics in 48 married couples. Sexually dimorphic neuroanatomy was investigated for gray matter volume (GMV), which was estimated by voxel-based morphometry. The brain regions that showed typically larger GMV in males compared with that in females were defined as the male-typical brain regions; otherwise, they were defined as the female-typical brain regions. We found that masculine gender roles and the individual deviation index (IDI) of the GMV in the male-typical brain region were positively correlated with marital satisfaction in males but were negatively correlated in females, demonstrating the "complementarity" nature of masculine characteristics, which was further supported by the negative correlation between couple-wise morphological similarity in male-typical brain region and marital satisfaction. Conversely, feminine characteristics reflected the "similarity" nature of married couples; i.e., feminine gender roles and IDI in the female-typical brain region were positively correlated with marital satisfaction in both males and females, and couple-wise morphological similarity in the female-typical brain region was positively correlated with marital satisfaction. The actor-partner interdependence model also supports the similarity/complementarity hypothesis. Additionally, the sexual dimorphism of brain morphometry and marital satisfaction were found to share a similar transcriptional mechanism. Our findings highlight the relationship among gender roles, brain morphology, and marital satisfaction, providing important implications for understanding marital bonding.

Comparing the Robustness of Intensity-modulated Proton Therapy and Proton-arc Therapy Against Interplay Effects of 4D Robust-optimised Plans for Lung Stereotactic Body Radiotherapy.

To assess the robustness of 4D-optimised IMPT and PAT plans against interplay effects in non-small cell lung cancer (NSCLC) patients with respiratory motion over 10 mm, and to provide insights into the use of proton-based stereotactic body radiotherapy (SBRT) for lung cancer with significant tumour movement. Fourteen patients with early-stage NSCLC and tumour motion >10 mm were selected. Three hypofraction regimens were generated using 4D robust optimisation with the IMPT and PAT techniques. The nominal plan qualities for both techniques were compared, and their robustness against setup and range uncertainties was evaluated. 4D dynamic dose and the 4D static dose were generated to calculate ΔIMR(%) for interplay effects. PAT plans demonstrated superior target metrics such as D95 and D2, and offered enhanced protection for organs at risk (OARs), particularly in lung metrics, across multiple fractionation schemes (p < 0.05). The robustness of target coverage against setup and range uncertainties was better in PAT plans than IMPT, with average pass rates of 97.8% and 95.4%, respectively (p < 0.01). The interplay effect significantly affected target metrics in single-fraction plans, decreasing with more fractions, while its effect on OAR metrics was minimal. Median values for single-fraction plans were: ΔID98GTV was -3% for IMPT and -0.7% for PAT (p < 0.01); ΔID95GTV was -2.4% for IMPT and -0.6% for PAT (p < 0.01); ΔID2GTV was 3.2% for IMPT and 0.9% for PAT (p < 0.05). The interplay effects resulted in median homogeneity index deviations of 9.1% and 2% for the IMPT and PAT plans, respectively (p < 0.01). Different starting phases affected IMPT more significantly than PAT. PAT demonstrated greater robustness to interplay effects than IMPT for hypofractionated treatments of early-stage NSCLC, particularly in single-fraction schemes. Additionally, PAT showed good resilience to variations in different starting phases.

LONGITUDINAL MEASURES OF FRAILTY AND MORTALITY

Abstract Assessment of frailty has become common in clinical settings to risk-stratify older adults. Understanding how to use repeated measurements answers important questions both for the clinical use of serial assessments and understanding frailty trajectories. Using the Health and Retirement Study, we calculated the most recent value, maximum, minimum, mean, standard deviation (SD), and slope of a deficit accumulation frailty index (FI) and the Fried frailty phenotype (FFP). We assessed the association of scaled values with mortality by Cox proportional hazards regression with adjustment for age, gender, and smoking status. We then used machine learning by LASSO regression to parsimoniously select variables associated with mortality. We found that the strongest association for the FI was its maximum value, with hazard ratio (HR) (95% CI) 1.67 (1.60-1.74). For FFP this was the mean value, HR 1.56 (1.44-1.70). For both FI and FFP, HRs of the most recent, maximum, and mean values were similar in magnitude, with estimates ranging from 1.62-1.67 and 1.50-1.56 respectively. In LASSO regression, the FI measures (HRs) retained were the maximum (1.38), most recent (1.18), and SD (1.01). For FFP, these were the mean (1.37), maximum (1.07), most recent (1.05). These findings show that maximum and most recent values of frailty tend to be most strongly associated with mortality. For clinicians, this means that the most recent assessment may be sufficient for many purposes. The associations of maximum frailty suggest that recovery may not lead to improved outcomes.

Research on the Analysis Model of Regional Economic Growth Differences in China Based on Big Data and Intelligent Algorithms

China’s growing economy contributes significantly to worldwide consumption. Economic rebalancing promises opportunities for manufacturing exporters, and that can weaken commodity demand in the long term. China exerts increasing influence on emerging countries through trade, investment, and ideas. China faces several important economic issues that can hinder future development, including distortive economic policies that have evolved into an overreliance on fixed investment and exports for economic growth, government assistance for state-owned businesses, and a weak banking system. Building an information infrastructure and enhancing the technical level and application capability of big data (BD) remain under the purview of the Chinese Government. Artificial intelligence (AI)-based hybrid artificial neural network (HANN) might boost total factor productivity by a large margin, influencing many sectors in China in ways that official statistics would miss, including changes to the labor market, investment patterns, and overall productivity. Hence, BD-HANN has a coefficient of variation, quantity graph analysis, standard deviation, and entropy index, some of the most conventional quantitative research tools used to examine disparities in regional economic growth. According to the neoclassical growth model, regions with lower starting values of the capital-labor ratio anticipate higher per capita income growth rates. Thus, poor areas will grow faster than rich ones, assuming that the only difference between regional economies is the level of their initial stock of capital.

Northern European Countries in Modern Global Value Chains: Comparative Analysis

This article dwells upon the comparative aspects of Northern European countries’ participation in modern global value chains (GVCs). The aim of the research was to identify economic sectors in which the analysed countries maintain the most beneficial position in GVCs. Methodology applied is based on the calculation of the vertical specialisation indices and indicators of centrality and clustering in international production networks. New indices of relative GVC specialisation, namely GVC participation intensity index and deviation index, were introduced and applied by the author. Quantitative assessment of the network metrices is based on a construction of the international production network graph. The nodes represented 76 world largest economies and the edges were non-negative normalised quantities of intermediate products interflows. The main source of raw data is the OECD Data Explorer statistical database. Geographical scope of research covers Denmark, Norway, Finland, and Sweden. The time period is 2010‒2019, before the COVID-19 pandemics. The results obtained suggest that Northern European countries participate in GVCs differently. Denmark and Sweden are well-positioned at higher stages of the value chain, whereas Norway and Finland occupy lower stages. At the same time, Denmark and Norway display a distinctive specialisation in water transport, Finland specialises in wood and paper manufacturing, and Sweden has a competitive edge in transport manufacturing. Denmark and Sweden are firmer integrated into the international production network, and they cooperate with relatively more influential partners than that of Norway and Finland. It has been unveiled that GVC participation of the two latter countries depends greatly on the state of the global market.

Study of Seasonal Variability of Ground Water Quality in Sarguja District (C.G.), India

Water is the vital essence, miracle of nature and the great sustainer of life. It is extremely essential for survival of all living organism. Ground water serves as a major source for drinking, irrigation and industrial purpose all over the world. Many factors affect the quality of groundwater and cause major health issues. Therefore regular monitoring of ground water quality is necessary. Present paper has formulated on the basis of assessment of impact of geological features and agricultural runoff in ground water sources of Sarguja district (C.G.) India. Water samples were collected from ten environmentally significant spots for analysis of physical, minerals, nutrients, agricultural qualities and toxic metallic and non metallic elements. The procured experimental values were subjected for the statistical evaluation and compared with the standard values prescribed by the water monitoring agencies BIS (2012) and WHO (2011). The result for pH recorded up to 10.0, turbidity (15.67 NTU), TS (720 mg/L), F- (15.67 mg/L), Fe (2.366 mg/L), Na (115 mg/L), in maximum concentration. The statistical parameter such as mean, standard deviation, standard error, % CV, correlation matrix and water quality index (WQI) were systematically calculated for pre monsoon, monsoon and post monsoon session. The obtained results are far away from the standard values which showed the investigated water samples are not suitable for drinking and agricultural purpose without prior treatment.

Exploring Sustainable Innovation Level, Spatial Inequities, and Convergence Trends in China’s Wood Industry

The importance of sustainable innovation in the wood industry is growing, but there is a lack of comprehensive analysis of its evolution, regional differences, and patterns of convergence in China. Based on the panel data of 31 provinces in China from 2011 to 2021, the sustainable innovation index of the wood industry is measured by the projection pursuit method. On this basis, the kernel density estimation method and Dagum Gini coefficient are used to study the dynamic evolution trend, regional differences, and sources of the index, and the convergence characteristics are examined using the coefficient of variation method. The study shows that (1) China’s overall wood industry sustainable innovation index shows a decreasing trend from 2011 to 2021. (2) The differences in the four regions mainly come from inter-regional differences. (3) The index shows significant nonequilibrium characteristics and progressive evolution patterns, and the spatial agglomeration is significant. The magnitude of the index deviation from the average did not decrease over time for the northern and southwestern forest regions. (4) The wood industry sustainable innovation index of the four major forest regions has obviously converged to the same level; under the condition of considering multifactors differentiation, the growth rate of the index of the lower regions is significantly higher than that of the higher regions. The study concludes that current regional imbalances in sustainable innovation in China impede progress and equitable distribution of benefits in the wood industry, and that the impact of regional differences on the β-convergence of sustainable innovation varies according to specific regional characteristics and conditions. These findings provide important theoretical contributions and practical guidance for the development of targeted innovation strategies for the sustainable development of the wood industry, as well as for the promotion of balanced regional development.

The impacts of convergence policies on macroeconomic stability in the West African monetary zone

PurposeThis paper examines the effectiveness of the joint impact of convergence policy measures on macroeconomic stability conditions in the West African monetary zone (WAMZ) countries, using an annual panel data from 1990 to 2021.Design/methodology/approachIt uses the dynamic autoregressive distributed lag (ARDL) approach to exploit the short and long run dynamics macro stability. A composite convergence index was constructed and its impact was examined on three different macroeconomic stability measures representing: internal stability, external stability and overall stability.FindingsFindings from the study suggest that a unit deviation of the convergence policy index from its mean, in the short run, leads to worsening external and overall macroeconomic stability conditions in the zone, but it leads to improvement in the internal macroeconomic stability conditions. However, in the long run, the opposite occurs, a unit deviation of the convergence policy index from its mean, it leads to improvement in both external and overall macroeconomic stability situations and deterioration in internal macroeconomic stability situation.Practical implicationsIt is concluded that the convergence policy criteria are effective only in the long run and therefore these policies alone cannot be used to achieve short run macroeconomic stability within the zone. Thus, policymakers should have a second look at how fiscal and monetary policies are conducted and also work to address structural challenges.Originality/valueThe paper examines the effectiveness of the joint impact of convergence policy measures on macroeconomic stability conditions in the WAMZ countries.