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Kalu Kumale and the Aesthetics of Wrath: Sculptural Practice, Affective Labor, and Cultural Resilience in Contemporary Nepal
Full text linkThis article offers a critical examination of the life, work, and legacy of Kalu Kumale, a pioneering figure in contemporary Nepali sculpture whose oeuvre spans over seven decades. Drawing upon a multidisciplinary qualitative methodology—comprising in-depth interviews, ethnographic observation, and archival analysis—this study investigates how Kumale’s sculptural practice engages with, reinterprets, and transcends traditional Newar iconography. Central to this inquiry are two seminal works, The Corpse of Sati Devi and Two Farmers Fighting, which serve as case studies for exploring the intersection of personal affect, socio-political commentary, and religious symbolism in his art.
Framed within the theoretical perspectives of postcolonial aesthetics and affect theory, the article contends that Kumale’s sculptures function as embodied texts that mediate grief, resilience, and communal memory. His integration of wrathful deity motifs and autobiographical narratives exemplifies a form of cultural hybridity that challenges static notions of tradition and modernity. Moreover, Kumale’s sustained engagement in artistic philanthropy and community activism positions him not merely as an artisan, but as a cultural agent who mobilizes art for ethical and social transformation.
By situating Kumale within both local traditions and transnational visual discourses, this article contributes to broader debates on indigenous visual sovereignty, cultural resilience, and the evolving role of the artist in postcolonial South Asia
US Vicarious Liability of Parents for Copyright Infringement by Minors: Review and Reform
Full text linkVicarious liability is one of the types of responsibilities arising from the acts of others. In US law, there are general rules that, if the persons under the control, commit copyright infringement and a financial benefit reach to the person with the right of control, the latter will have a vicarious liability. Undoubtedly, minors are one of the most obvious examples of people under control, which is often done by their parents. Moreover, in the current era, it is very likely that many infringements are committed by minors, especially in the Internet environment. Therefore, parents are generally subject to vicarious copyright liability arising from the infringing acts minors. the probability of vicarious liability of Parents for chides copyright infringement, has been given under general rule of this type of liability, while the nature of the relationship between parents and children and the basis of parents' responsibility for the fault of their children, Requires some differences in this regard
Finance, Financial Crime and Regulation: Can Generative AI (Artificial Intelligence) Help Face the Challenges?
Full text linkGenerative artificial intelligence (Gen AI) has helped change the trajectory of Banking (FinTech) and Law (Reg Tech/Law Tech). Technology innovates at an astounding rate. AI and Gen AI can not only simulate human intelligence (human thinking) but also perform tasks independently. It can develop intelligence based on its experiences, process detailed and complex information whilst continually learning and re-learning to be able to undertake complex, technical, and time-consuming tasks in real time. It can identify objects, patterns, people and voices(etc.) and look for problems far earlier – this also means it can come up with solutions quickly which in critical situations is of salient importance. The economic, political, and social benefits cannot be underestimated, but must be balanced against its disruptive and destructive potential. This article explores whether Gen AI can help further revolutionise the finance industry and how it can help with risks, and the various regulatory and operational challenges faced by those firms in the United Kingdom (UK). Data is analysed alongside domestic and international published literature. The article starts by summarising current risks and challenges and then discusses how Gen AI can be embedded as part of an arsenal of tools that financial institutions can use to develop and provide solutions to regulatory and operational challenges as at January 2025
Scoring System Model for Early Detection of Maternity Blues in Bukittinggi, West Sumatera, Indonesia
Full text linkBackground: Maternity blues creates emotional instability in moms, causing them to become irritated, overly nervous, and feel incapable of being a good mother. Maternity blues may interfere with infant care and raise the risk of postpartum depression symptoms, disrupting mother and baby interactions. Maternity blues is often ignored so it is not diagnosed and if not treated properly it can become a problem and develop into postpartum depression or postpartum psychosis. Maternity blues is a serious condition that poses risks to both mothers and infants. If left untreated, Maternity blues can progress into postpartum depression, which has significant physical and psychological consequences. Early detection of Maternity blues is crucial for timely intervention and prevention.
Objectives: This study aims to develop a Scoring System Model for the early detection of maternity blues , allowing for effective screening and timely management.
Methods: A cross-sectional study was conducted in Bukittinggi City, West Sumatra, Indonesia, involving 126 postpartum mothers recruited consecutively. Data analysis included the calculation of odds ratios, logistic regression, and ROC curve analysis to determine the sensitivity and specificity of the prediction model. The scoring system's performance was assessed using calibration and discrimination values.
Results: The developed scoring system demonstrated good calibration and discrimination, with an Area Under the Curve (AUC) value of 0.806 (95% CI: 0.732–0.881). The Hosmer & Leme show test showed a p-value of 0.724, indicating a good fit for the model.
Conclusion: The proposed scoring system is a reliable tool for the early detection of maternity blues . By identifying at-risk mothers through prediction scores, appropriate interventions can be implemented to prevent the progression of maternity blues into more severe postpartum mental health disorders
Determining Internal and External Risks in a Medical Center
Full text linkAn enterprise stores information in the cloud providing virtual storage of data as virtual memory. Cloud increases the enterprise’s ability to offer data and service delivery, however it also increases the chances of a cybersecurity threat, and cyber risks, and increases the vulnerability of the enterprise to risks. It is important for the organization to perform risk management to determine cybersecurity risks. Cybersecurity is a key need for hospitals to manage threats of all types. Healthcare is notoriously vulnerable to cyber-attacks due to the valuable nature of patient information and the lack of updated medical equipment. In this paper, we discuss medical applications in cybersecurity, AI's role in cybersecurity, and risk management in medical cybersecurity
Compositional Study of Polymer Blend PVA, Pectin, Sodium Alginate, and Gelatin Electrospun Nanofiber for Wound Dressing Application
Full text linkElectrospun nanofibers are a biomaterial effective for wound healing due to their high surface area, tunable properties, and resemblance to the extracellular matrix. Nanofibers from the mixture of polymeric materials like gelatin, sodium alginate, pectin, and polyvinyl alcohol (PVA) were investigated in this study. Pectin, sodium alginate, and gelatin are selected for their nature of being applied as tissue carriers, and they have the properties of being biocompatible and biodegradable while inducing cell proliferation. Unfortunately, these polymers have some drawbacks: most of them have poor mechanical strength or poor processing ability through electrospinning. To enhance these properties, PVA was incorporated. The result showed that an optimal blend ratio of 20% PVA, 40% pectin, 25% sodium alginate, and 15% gelatin yielded a fibrous structure with an average diameter of the fibers equal to 174.82 ± 13 nm, surface tension of 33.29 mN/m, and viscosity at 7,378 cP, which facilitated the uniform fiber formation and a porous structure for enhanced gas exchange and moisture retention, significantly aiding wound healing
Biochemical Investigation of Serum Iron Level in Water Buffaloes in Three Regions of Babylon, Iraq
Full text linkBuffalo is a multipurpose ruminant that can adjust to a variety of environmental conditions. The quality of soil and forages determines iron (Fe) availability to ruminants. The objectives of this study were to examine how serum Fe levels and related physiological and biochemical indices in water buffaloes affected by regional differences in Fe levels of soil and forages from three regions (South, Middle, and North) of the Babylon Province, Iraq. A total of 180 water buffaloes of various ages and sexes were randomly selected from three regions (South, Middle, and North) of Babylon Province, Iraq. All buffaloes were clinically examined. Then, fecal samples for parasitology examination and blood samples were collected for use in hematology and biochemical analysis.Soil and forage samples were collected and analyzed for Fe levels using atomic absorption spectrophotometry (AAS). Data were statistically evaluated by using SPSS. Our study revealed a significant regional variation in soil Fe levels, highest in the South, moderate in the Middle, and lowest in the North. Forage Fe content varied by type and region, with decreased Fe levels in barley grasses in the North region, while other regions showed Fe levels within the normal range. The Fe levels in alfalfa grass in the North region declined, while Fe levels in the South and Middle regions were within normal range. The Fe level in fresh rice straw decreased in the South, Middle, and North regions. Markedly, 96.11% of buffaloes had serum Fe levels below the normal range. The body temperature was within normal range, while respiratory and pulse rates were increased. (92.22%), (0%), and (2.22%) of buffaloes had ferritin, transferrin, and total iron binding capacity (TIBC) below normal levels, respectively; and (6.11%), (3.33%), and (0%) of buffaloes had normal levels, respectively; and (1.66%), (96.66%), and (97.77%) of buffaloes had higher than normal levels, respectively. (98.88%), (95.55%), and (98.88%) of buffaloes had red blood cells (RBCs), hemoglobin (Hb), and hematocrit (Hct) below normal levels, respectively; (1.12%), (4.44%), and (1.12%) of buffaloes had normal levels, respectively; and (0%) of buffaloes had greater than normal levels. To the best of our knowledge, this is the first study in Babylon province, Iraq, to identify how the serum Fe levels of buffaloes are affected by the regional differences in levels of Fe in soil and three different types of forages
To Identify the Predictors of Mortality in Renal Patients Undergoing Dialysis
Full text linkChronic Kidney Disease (CKD) patients undergoing dialysis experience high mortality risk due to complex clinical factors and multiple comorbidities. Precise identification of mortality predictors is vital for early risk stratification and improving patient management. This study aimed to identify key predictors of mortality among renal patients undergoing dialysis using a combination of statistical and machine learning techniques on a dataset comprising 224 observations and 33 clinical features. Associations between mortality and clinical variables were assessed using chi-square tests and independent samples t-tests. Feature selection methods—LASSO regression, Random Forest, and Gradient Boosting—were employed to identify important predictors. Machine learning models were developed to evaluate predictive performance. LASSO regression emphasized sparsity, selecting critical features including total dialysis sessions, heart, and lung disease. Random Forest highlighted age, diabetes, and cardiovascular comorbidities, capturing nonlinear relationships. Gradient Boosting identified additional hemodynamic variables such as pre- and post-dialysis blood pressures. The combined feature set aggregated predictors from all methods, enhancing robustness. The Random Forest model achieved the highest discriminative performance (AUC = 0.851), with LASSO demonstrating higher sensitivity for deceased patients. Cardiovascular and metabolic comorbidities, dialysis parameters, and age are pivotal predictors of mortality in CKD patients on dialysis. Integrating multiple analytical methods strengthens predictive accuracy, facilitating better-informed clinical decision-making and targeted interventions. Multivariable Cox regression revealed that age was a significant predictor of mortality, with each additional year increasing the hazard by approximately 3% (HR = 1.028; 95% CI: 1.006–1.050; p = 0.0122). Conversely, a higher number of dialysis sessions was associated with a reduced mortality risk, decreasing the hazard by 3.8% per session (HR = 0.962; 95% CI: 0.952–0.973; p < 0.001). Lung involvement more than doubled the risk of death (HR = 2.226; 95% CI: 1.088–4.557; p = 0.0285), while the presence of anaemia and diabetes independently increased mortality risk by nearly threefold (HR = 2.846 and 2.848, respectively; p < 0.01). These results highlight the importance of managing comorbid conditions to improve survival outcomes
An Empirical Comparison among Four Estimation Methods for the Laplace Distribution and Its Potential Application in Medical Research
Full text linkThis study investigates the performance of four parameter estimation methods for the Laplace distribution: Method of Moments (MM), Maximum Likelihood Estimation (MLE), Minimum Chi-Square Estimation using equiprobable cells (MCE-EQ), and Minimum Chi-Square Estimation using Representative Points (MCE-RP). Through comprehensive Monte Carlo simulations with sample sizes ranging from 50 to 400, we compare the root mean squared error (RMSE) of the location (μ) and scale (b) parameter estimates. Our results demonstrate that while MLE remains robust for location estimation, the MCE-RP method consistently outperforms other estimators—including MLE—for the scale parameter, particularly in small to moderate samples. The use of Representative Points, which provide an optimal discretization of the distribution, significantly enhances estimation precision. These findings are especially relevant for medical research, where accurate estimation of variability—such as in biomarker concentration levels or physiological response times—is critical for reliable sample size determination, risk assessment, and clinical decision-making. MCE-RP thus offers a superior, reliable estimator for the Laplace scale parameter, with direct implications for improving statistical inference in applied biomedical studies.
Purpose: The purpose of this research is to empirically evaluate and compare the finite-sample performance of four estimation methods for the Laplace distribution’s parameters, with a focus on the novel application of Representative Points in minimum chi-square estimation. This work seeks to bridge the gap between theoretical estimation methods and practical applications, providing applied researchers with a more robust estimation tool when modeling data with Laplace characteristics, such as those commonly encountered in medical and biomedical studies.
Methods: We conducted an extensive Monte Carlo simulation study to compare the four estimation methods: MM, MLE, MCE-EQ, and MCE-RP. For each method, we generated independent and identically distributed samples from a standard Laplace distribution (μ=0, b=1) with sample sizes n = 50, 100, 200, and 400. Each scenario was replicated 1,000 times. The performance of each estimator was assessed using the root mean squared error (RMSE) for both μ and b. The MCE-RP method utilized pre-computed Representative Points for the standard Laplace distribution, which were transformed according to preliminary MLE estimates to form an optimal cell structure for chi-square minimization. All nonlinear optimizations required for MCE-EQ and MCE-RP were implemented programmatically.
Results: The simulation results indicate that MLE performs best for estimating the location parameter μ across all sample sizes. However, for the scale parameter b, the MCE-RP method consistently yields lower RMSE values compared to MLE, MM, and MCE-EQ. In many cases, particularly for smaller samples, the RMSE of MCE-RP is approximately half that of MLE for b. The advantage of MCE-RP is evident across varying numbers of Representative Points (m = 5, 10, 15, 20), with optimal performance often observed at m = 10 or 15. These findings confirm that MCE-RP provides a more precise and reliable estimator for the scale parameter, making it particularly advantageous in small-sample settings.
Contribution: This paper contributes to the statistical methodology for the Laplace distribution by introducing and validating the use of Representative Points within a minimum chi-square estimation framework. The key contributions are: (1) demonstrating that MCE-RP significantly outperforms established methods for estimating the scale parameter; (2) providing empirical evidence that RP-based discretization enhances estimation efficiency, especially in finite samples; (3) offering practical guidance for applied researchers in fields such as medical statistics, where accurate scale estimation is crucial for variability assessment, power analysis, and reliable inference; and (4) laying a methodological foundation for extending the RP approach to other location-scale distributions
Processability Assessment of HDPE/UHMWPE Blends for Fused Deposition Modeling Applications
Full text linkUltra-high molecular weight polyethylene (UHMWPE) is highly regarded for its superior mechanical properties, chemical resistance, and biocompatibility. However, its extremely high melt viscosity inhibits direct use in extrusion-based additive manufacturing techniques like fused deposition modeling (FDM). This study explores enhancing the processability and FDM compatibility of UHMWPE by blending it with high-density polyethylene (HDPE) and polyethylene glycol (PEG). Three formulations were assessed: neat HDPE, a 70:30 (w/w) binary HDPE/UHMWPE blend, and a ternary blend of HDPE/UHMWPE/PEG at 60:30:10 (w/w/w). Consistent with prior literature, pure HDPE displayed stable extrusion and excellent filament quality facilitating high-fidelity prints. The binary blend allowed filament formation but showed rough surface morphology and compromised print quality due to poor miscibility, echoing similar challenges reported in polymer blend studies. The ternary blend, intended to improve melt flow via PEG plasticization, resulted in erratic filament diameter and unreliable extrusion, highlighting the delicate balance needed in additive incorporation. These outcomes confirm that HDPE incorporation improves UHMWPE extrusion capabilities; however, advanced compatibilization techniques and refined processing, such as twin-screw extrusion, remain essential for achieving dependable FDM performance. The findings offer critical insights for designing UHMWPE-based filaments tailored for biomedical and industrial additive manufacturing applications