Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut's Good Samaritan Laws and identify effective policy options.

BACKGROUND: Good Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut's Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT). METHODS: We conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD). RESULTS: Our resulting qualitative SD model captures our GMB participants' collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone. CONCLUSIONS: Our qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

Numerical study of diffusive fish farm system under time noise.

In the current study, the fish farm model perturbed with time white noise is numerically examined. This model contains fish and mussel populations with external food supplied. The main aim of this work is to develop time-efficient numerical schemes for such models that preserve the dynamical properties. The stochastic backward Euler (SBE) and stochastic Implicit finite difference (SIFD) schemes are designed for the computational results. In the mean square sense, both schemes are consistent with the underlying model and schemes are von Neumann stable. The underlying model has various equilibria points and all these points are successfully gained by the SIFD scheme. The SIFD scheme showed positive and convergent behavior for the given values of the parameter. As the underlying model is a population model and its solution can attain minimum value zero, so a solution that can attain value less than zero is not biologically possible. So, the numerical solution obtained by the stochastic backward Euler is negative and divergent solution and it is not a biological phenomenon that is useless in such dynamical systems. The graphical behaviors of the system show that external nutrient supply is the important factor that controls the dynamics of the given model. The three-dimensional results are drawn for the various choices of the parameters.

Distinct inflammatory markers in primary and secondary dengue infection: can cytokines CXCL5, CXCL9, and CCL17 act as surrogate markers?

Dengue fever poses a significant global health threat, with symptoms including dengue hemorrhagic fever and dengue shock syndrome. Each year, India experiences fatal dengue outbreaks with severe manifestations. The primary cause of severe inflammatory responses in dengue is a cytokine storm. Individuals with a secondary dengue infection of a different serotype face an increased risk of complications due to antibody-dependent enhancement. Therefore, it is crucial to identify potential risk factors and biomarkers for effective disease management. In the current study, we assessed the prevalence of dengue infection in and around Aligarh, India, and explored the role of cytokines, including CXCL5, CXCL9, and CCL17, in primary and secondary dengue infections, correlating them with various clinical indices. Among 1,500 suspected cases, 367 tested positive for dengue using Real-Time PCR and ELISA. In secondary dengue infections, the serum levels of CXCL5, CXCL9, and CCL17 were significantly higher than in primary infections (P < 0.05). Dengue virus (DENV)-2 showed the highest concentrations of CXCL5 and CCL17, whereas DENV-1 showed the highest concentrations of CXCL9. Early detection of these cytokines could serve as potential biomarkers for diagnosing severe dengue, and downregulation of these cytokines may prove beneficial for the treatment of severe dengue infections.

Synthesis and characterization of X (X = Ni or Fe) modified BaTiO3 for effective degradation of Reactive Red 120 dye under UV-A light and its biological activity.

For the sustainable advancement of industrial expansion that is environmentally conscious, harmful dyes must be removed from wastewater. Untreated effluents containing colors have the potential to harm the ecosystem and pose major health risks to people, animals, and aquatic life. Here, we have fabricated Ni or Fe modified with BaTiO3 materials and effectively utilized them for Reactive Red 120 (RR 120) dye degradation under UV-A light. The synthesized materials were characterized, and their structural, and photo-physical properties were reported. Phase segregation was not present in the XRD pattern, as evidenced by the absence of secondary phase peaks linked to iron, nickel, or oxides. Low metal ion concentrations may be the cause of this, and the presence of those elements was confirmed by XPS measurements. The Raman spectra of the BaTiO3/Ni and BaTiO3/Fe samples show a widened peak at 500 cm-1, which suggests that Ni or Fe are efficiently loaded onto the BaTiO3. RR 120 dye photodegradation under UV light conditions was effectively catalyzed by BaTiO3/Fe, as evidenced by its superior performance in the UV irradiation technique over both BaTiO3 and BaTiO3/Ni. Compared to bare BaTiO3, both metal-modified materials efficiently degraded the RR 120 dye. Acidic pH facilitated the degradation process, which makes sense given that the heterogeneous photo-Fenton reaction was the mechanism of degradation along with BaTiO3 sensitization. High-acidity sewage can be dangerous and carcinogenic, and conventional biological treatment methods are not appropriate for managing it. In the current investigation, it may be used to treat color effluents with extremely low pH levels. Additionally, the ability of the produced nanocomposites to inhibit the growth of twenty pathogens was examined, along with two fungi, fifteen Gram-negative Bacilli (GNB), one Gram-positive Bacilli (GPB), and two Gram-positive Cocci (GBC).

Recent progress in metal oxide-based electrode materials for safe and sustainable variants of supercapacitors.

A significant amount of energy can be produced using renewable energy sources; however, storing massive amounts of energy poses a substantial obstacle to energy production. Economic crisis has led to rapid developments in electrochemical (EC) energy storage devices (EESDs), especially rechargeable batteries, fuel cells, and supercapacitors (SCs), which are effective for energy storage systems. Researchers have lately suggested that among the various EESDs, the SC is an effective alternate for energy storage due to the presence of the following characteristics: SCs offer high-power density (PD), improvable energy density (ED), fast charging/discharging, and good cyclic stability. This review highlighted and analyzed the concepts of supercapacitors and types of supercapacitors on the basis of electrode materials, highlighted the several feasible synthesis processes for preparation of metal oxide (MO) nanoparticles, and discussed the morphological effects of MOs on the electrochemical performance of the devices. In this review, we primarily focus on pseudo-capacitors for SCs, which mainly contain MOs and their composite materials, and also highlight their future possibilities as a useful application of MO-based materials in supercapacitors. The novelty of MO's electrode materials is primarily due to the presence of synergistic effects in the hybrid materials, rich redox activity, excellent conductivity, and chemical stability, making them excellent for SC applications.

Phloroglucinol ameliorated methylglyoxal induced harmful effects in rats.

Glycation is among the underlying mechanisms attributed to ageing and associated morbidities. There is no drug available to combat this deleterious phenomenon. The present study aimed to explore phloroglucinol (PHL) for its anti-glycation potential at preclinical level. The rats were treated with methylglyoxal (MGO, 17.25 mg/kg, i.p. for 14 days) to induce glvcative stress. The treatment groups received additional administration of test drug (PHL; 0.25mg/kg, 0.5mg/kg, and 1mg/kg) or standard aminoguanidine (AG, 50 mg/kg) or saline (control, 5ml/kg). During 14 days, the weight and food intake was noted. Afterwards, the cognitive function was evaluated using Morris Water Maze (MWM) while hepatic and renal functions were assessed through liver function test (bilirubin, alkaline phosphatase, SGPT, and SGOT) and creatinine respectively, using chemical analyzer. The carboxymethyllysine (CML) levels were quantified in the blood using ELISA technique. Histopathological study was performed on the brain, liver, and kidney using H&E staining. Additionally, the qPCR was used to quantify the expression of TNF-α, RAGE and BACE-1 (brain), RAGE, TNF-α, and glyoxalase-I (liver) and RAGE, TNF-α, and VEGF (kidney), while glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference housekeeping gene. The data regarding weight and food intake did not reveal significant alterations. In MWM, the MGO treatment caused significant increase in the time to reach target quadrant, while decrease in the time spent in target quadrant and number of crossings through platform position. All these effects were inhibited by both AG and PHL. The navigation maps also exhibit that the retention of spatial memory. Additionally, the MGO-induced alteration in hepatic and renal function indicators was ameliorated by both AG and PHL treatments. The plasma CML levels were found to be elevated following MGO treatment, while the concomitant administration of AG and PHL has resisted this raise. Histopathological assessment revealed no specific pathology in liver kidney and brain tissues. The qPCR data revealed enhanced expression of all genes, especially TNF-α and BACE, which were found to be reduced following both AG and PHL treatments. PHL prevented the brain, hepatic, and renal impairments caused by MGO induced glycative stress. Hence, the PHL, a clinically used anti-spasmodic drug, presents itself as a potential candidate to be repurposed as anti-glycation drug.

Wearable, Machine Washable, Breathable Polyethylenimine/Sodium Alginate Layer-by-Layer-Coated Cotton-Based Multifunctional Triboelectric Nanogenerators.

Cotton-based textiles are ubiquitous in daily life and are prime candidates for application in wearable triboelectric nanogenerators. However, pristine cotton is vulnerable to bacterial attack, lacks antioxidant and ultraviolet (UV)-protective abilities, and shows lower triboelectric charge generation against tribonegative materials because it is present in the neutral region of the triboelectric series. To overcome such drawbacks, herein, a facile layer-by-layer method is proposed, involving the deposition of alternate layers of polyethylenimine (PEI) and sodium alginate (SA) on cotton. Such modified fabric remains breathable and flexible, retains its comfort properties, and simultaneously shows multifunctionalities and improved triboelectric output, which are retained even after 50 home laundering cycles. Also, the modified fabric becomes more tribopositive than nylon, silk, and wool. A triboelectric nanogenerator consisting of modified cotton and polyester fabric is proposed that shows a maximum power density of 338 mW/m2. An open-circuit voltage of ∼97.3 V and a short-circuit current of ∼4.59 µA are obtained under 20 N force and 1 Hz tapping frequency. Further, the modified cotton exhibits excellent antibacterial, antioxidant, and UV-protective properties because of the incorporation of PEI, and its moisture management properties are retained due to the presence of sodium alginate in the layer. This study provides a simple yet effective approach to obtaining durable multifunctionalities and improved triboelectric performance in cotton substrates.

Immobilization of collagenase in inorganic hybrid nanoflowers with enhanced stability, proteolytic activity, and their anti-amyloid potential.

Organic-inorganic hybrid nanomaterials are considered as promising immobilization matrix for enzymes owing to their markedly enhanced stability and reusability. Herein, collagenase was chosen as a model enzyme to synthesize collagenase hybrid nanoflowers (Col-hNFs). Maximum collagenase activity (155.58 µmol min-1 L-1) and encapsulation yield (90 %) were observed in presence of Zn(II) ions at 0.05 mg/mL collagenase, 120 mM zinc chloride and PBS (pH 7.5). Synthesized Col-Zn-hNFs were extensively characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR), circular dichroism (CD), fluorescence spectroscopy, dynamic light scattering (DLS) and zeta potential measurements. SEM images showed flower-like morphology with average size of 5.1 µm and zeta potential of -14.3 mV. Col-Zn-hNFs demonstrated superior relative activity across wide pH and temperature ranges, presence of organic solvents and surfactants as compared to its free form. Moreover, Col-Zn-hNFs exhibited excellent shelf life stability and favorable reusability. Col-Zn-hNFs showed the ability to suppress and eradicate fully developed insulin fibrils in vitro (IC50 = 2.8 and 6.2 µg/mL, respectively). This indicates a promising inhibitory potential of Col-Zn-hNFs against insulin amyloid fibrillation. The findings suggest that the utilization of Col-Zn-hNFs as a carrier matrix holds immense potential for immobilizing collagenase with improved catalytic properties and biomedical applications.

Correlation of Serum Sodium and Potassium With Thiamin Levels in Type 1 and Type 2 Diabetic Patients.

Introduction Chronic metabolic disorders such as diabetes mellitus (DM) are becoming a global health concern. According to recent studies, the pathophysiology of DM may involve factors other than traditional glycemic control, such as electrolyte balance and thiamin status. Therefore, this study evaluated the relationship between sodium and potassium and serum thiamin levels in patients with type 1 and type 2 DM. Methods This study was conducted in multiple diabetic outpatient clinics and centers in Karachi, Pakistan, using a non-probability convenience sampling method. The study lasted for approximately six months after the synopsis was approved. A total of 64 patients were selected, 32 of whom each had type 1 and type 2 DM. All patients who were between the ages of 25 and 46 years old and had either type 1 or type 2 DM were included in the study. A Mann-Whitney test and an independent t-test were used to compare the means between the two study groups. Pearson's correlation and chi-square tests were used to determine the variables, correlations, and associations with type 1 and type 2 DM. Results The study findings showed that the distribution of gender among diabetic patients revealed that among males, eight (25.0%) had type 1 DM, and 10 (31.2%) had type 2 DM. Among females, 24 (75.0%) had type 1 DM, and 22 (68.8%) had type 2 DM. Significant correlations were observed in the means of blood glucose levels, such as glycated hemoglobin (HbA1c), fasting blood sugar (FBS), and serum thiamin levels, among patients with type 1 and type 2 DM (p < 0.001). The HbA1c, FBS, and serum thiamin levels were significantly higher in type 2 DM patients than in type 1 DM patients. Among patients with type 1 DM, sodium levels were not substantially correlated with thiamin levels (p = 0.570, r = 0.104), whereas potassium levels were significantly correlated with thiamin levels (p = 0.005, r = 0.263). Conclusion We conclude that the sodium level was not significantly correlated with serum thiamin status in type 1 and type 2 DM, whereas a low positive correlation was observed between potassium and serum thiamin levels in type 1 DM. However, there was no significant correlation concerning potassium levels in type 2 DM.

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.

Background: Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods: A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion: ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article's incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database.

Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction.

Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches.

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.

Toxic elements identified in breast milk of mothers residing in water contaminated region of Sindh and their impact on infants' growth patterns: A case-control study.

Breast milk is a vital source of nutrition for breastfed infants, providing essential nutrients and elements but, in some cases, toxic ones. This is the first case-control study that investigated the elemental profile of breast milk samples collected from mothers residing in Matiari (Sindh), a region with insufficient industrial waste management, and its potential impact on infants' anthropometrics. Precisely, 62 milk samples, including 42 cases and 20 controls, were analyzed using the ICP-MS technique. Overall, six elements showed significance between the two groups, arsenic (As) was present at 0.68 µg/L in cases and absent in controls, while lead (Pb) exhibited elevated concentrations in the case group at 4.56 µg/L compared to 0.25 µg/L in controls, well-known for their toxicity. Barium (Ba) and manganese (Mn) levels were also higher in cases, associated with reported health effects on child well-being. Essential elements molybdenum (Mo) and selenium (Se) were higher in the controls. Furthermore, the association of these metals with the child growth standards as per WHO guidelines was calculated. Linear regression analysis revealed As negatively associated with WAZ and WHZ scores, while Mo was positively associated with WAZ, WHZ, and HAZ scores. These findings highlight serious health concerns in the region, where toxic elements pervade drinking water and food sources. Immediate actions are imperative to maintain the wellness of future generations.

Efficacy and Safety of Rivaroxaban Versus Enoxaparin in Prevention of Recurrence of Venous Thrombo-Embolism Events in Cancer Patients: A Meta-Analysis.

OBJECTIVE: To examine the effectiveness of rivaroxaban compared to enoxaparin in patients diagnosed with cancer and venous thromboembolism. METHODS: A search of Pub Med, Scopus, and Google Scholar, from inception through April 2023 was conducted. Articles comparing rivaroxaban with enoxaparin in patients with cancer and VTE/PE/DVT were included. Review Manager Version 5.2 was utilised for the analysis of the following outcomes; VTE, PE, DVT, major bleeding, and mortality. RESULTS: A total of 8 articles and 2276 patients were included in the final analysis. Pooled analysis showed that rivaroxaban had a statistically insignificant reduced association with VTE occurrence (RR:0.83, 95% CI:0.58-1.18, P:0.3) as well as a statically insignificant reduction in major bleeding (RR:0.79, 95% CI:0.53-1.18, P:0.25). Analysis showcased that there was an insignificant reduction of mortality rivaroxaban as compared to enoxaparin (RR:0.74, 95% CI: 0.46-1.20, P:0.23). CONCLUSION: Rivaroxaban can serve as a viable alternative to enoxaparin, with no appreciable drawbacks, for preventing and managing VTE in patients with malignancy.

Preparation and Spectrochemical characterization of Ni-doped ZnS nanocomposite for effective removal of emerging contaminants and hydrogen Production: Reaction Kinetics, mechanistic insights.

In this study, we report the successful synthesis of Ni-doped ZnS nanocomposite via a green route using ethanolic crude extract of Avena fatua. The as-synthesized nanocomposite was comprehensively characterized using Dynamic light scattering (DLS), Zeta potential, scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and Atomic force microscopy (AFM). These analyses provided detailed insights into the size, morphology, composition, surface properties, and structural characteristics of the nanocomposite. Subsequently, the synthesized nanocomposite was evaluated for their photocatalytic performance against the organic dye Methyl orange. Remarkably, the nanocomposite exhibited rapid and efficient degradation of Methyl orange, achieving 90 % degradation within only 30 min of irradiation under UV light. Moreover, the photocatalyst demonstrated an exceptional hydrogen production rate, reaching 167.73 µmolg-1h-1, which is approximately 4.5 times higher than that of its pristine counterparts. These findings highlight the significant potential of Ni-doped ZnS nanocomposite as highly efficient photocatalysts for wastewater treatment and hydrogen production applications.

Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Evaluation of adjective and adverb types for effective Twitter sentiment classification.

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework.

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.