Superior Value Research Articles

Diagnostic value of the pepsin concentration in saliva and induced sputum for gastroesophageal reflux-induced chronic cough: a prospective clinical study.

Finding a simple, effective and rapid diagnostic method to improve the diagnosis of gastroesophageal reflux-induced chronic cough (GERC) is indicated. Our objective was to determine the diagnostic value of the pepsin concentration in saliva and induced sputum for GERC. 171 patients with chronic cough were enrolled. The diagnosis and treatment followed the chronic cough diagnosis and treatment protocol. Saliva and induced sputum were collected, and the pepsin concentration was determined using Peptest. A Gastroesophageal Reflux Diagnostic Questionnaire (GerdQ) was completed. The diagnostic value of the pepsin concentration in saliva and induced sputum for GERC was analysed and compared. The salivary pepsin concentration predicted GERC with an area under the receiver operating characteristic curve (AUC) of 0.845. The optimal cut-off value was 76.10 ng·mL-1, the sensitivity was 83.58% and the specificity was 82.69%. The pepsin concentration in the induced sputum supernatant for GERC had an AUC of 0.523. When GerdQ was used for GERC diagnosis, the AUC was 0.670 and the diagnostic value of salivary pepsin was better compared to GerdQ (DeLong test, p=0.0008). Salivary pepsin had a comparable diagnostic value to GerdQ (AUC 0.779 versus 0.826; p=0.4199) in acidic GERC. Salivary pepsin had superior diagnostic value compared to GerdQ (AUC 0.830 versus 0.533; p<0.0001) in non-acidic GERC. A salivary pepsin concentration >76.10 ng·mL-1 is of good diagnostic value for GERC, especially in non-acidic GERC. The pepsin concentration in induced sputum has a low diagnostic value.

The Application of Resin Infiltration Combined with Nano-Hydroxyapatite in the Treatment of Post-Orthodontic White Spot Lesions

This study aims to investigate whether the combination of resin infiltration and nanohydroxyapatite (nHA) can provide a synergistic treatment for post-orthodontic white spot lesions (WSL), thus establishing an experimental basis for its treatment. For this study, a total of 60 bicuspid teeth that required extraction due to orthodontics were collected. Orthodontic models were established for each tooth sample, and demineralization staining was conducted using Coca-Cola. The dental samples were randomly divided into 4 groups: control group, nHA group, resin infiltration group, and resin infiltration+nHA group. The demineralized samples were remineralized with 10% nHA and Icon resin infiltration, respectively. The samples were subjected to color difference observation, microhardness test, energy-dispersive X-ray spectrum analysis, and surface roughness detection. The results showed that the tooth surface of the control group was rough and dull after demineralization. The area around the bracket exhibited a brown color, greatly contrasting with the color of the surrounding tooth enamel. After the remineralization treatment with nHA and resin infiltration, the color difference decreased significantly and the microhardness increased. A significant difference was observed between the nHA group and the resin infiltration group (P &lt; 0.05). However, the combination of resin infiltration and nHA exhibited superior staining resistance and improved WSL microhardness value. Additionally, nHA was found to enhance the mass ratio and molar ratio of calcium to phosphorus in WSL samples, while also reducing the surface roughness Ra, demonstrating a greater effectiveness compared to resin infiltration. The use of nHA following resin infiltration led to a significant improvement in mineral deposition and a substantial reduction in surface roughness. In conclusion, both resin infiltration and nHA play a crucial role in promoting enamel remineralization, offering their respective advantages in reducing color difference, increasing microhardness and calcium and phosphorus content, and reducing surface roughness in WSL samples. Remarkably, the combined application of resin infiltration and nHA enables resin infiltration to improve the remineralization and anti-coloring ability in WSL, while preserving its aesthetic effects. This combination demonstrates a positive application outcome.

Piping network optimization for district heating system using an enhanced Genetic Algorithm searching method

Given the substantial construction and operational expenses associated with a district heating system (DHS), achieving an optimal layout for the piping network is paramount for its successful deployment. In this study, the optimization of DHS piping configuration has been explored. Traditional exhaustive search methods for network design are often impractical due to computational constraints, making Genetic Algorithm (GA) a preferred alternative for navigating the vast search space of optimization problems efficiently. However, GA, which relies on probabilistic heuristics, may not always pinpoints the most effective solutions, particularly those in close proximity to the current near-optimal configuration. To address this issue, an enhanced GA-based neighboring search method for identifying optimal or near-optimal piping layout for DHS more effectively has been developed in this study. When a local minimum is detected in the GA optimization process, the method initiates a neighboring search by selecting an elite candidate. The link with the highest piping cost from the elite candidate's configuration is identified. Subsequently, one of the other nodes in the configuration is connected to the nodes of the identified link to explore solutions beyond the local optimum. This new approach was firstly validated against an Optimal Communication Spanning Tree (OCST) benchmark problem, where it demonstrated superior efficacy by achieving higher success rate and quicker first-hit generation compared to conventional GA method. Specifically, this method showed a 60 % success rate with a minimum first-hit generation number of 140, outperforming the traditional GA's 40 % success rate and 263 generations. Further modification to the OCST benchmark to better simulate a real-world DHS application underscored the ability of this neighboring search method to exceed the previously established optimal configuration, uncovering a “better' solution with superior fitness value. This promising methodology was subsequently applied to the hypothetical design of a DHS piping network, illustrating its potential to enhance the planning and implementation of efficient, cost-effective district heating systems.

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.

Advancing Sustainable Agriculture: A Comprehensive Review of Organic Farming Practices and Environmental Impact

Organic farming, characterized by the exclusion of synthetic fertilizers and chemical pesticides, represents a sustainable approach to agriculture. This study investigates the impact of organic farming practices on soil health, focusing on the proliferation and activity of soil microorganisms. Natural fertilizers such as farmyard manure, poultry litter, composts, green manures, and oil-free cakes were utilized to enhance soil fertility. The research reveals that these practices significantly improve soil health by fostering a robust microbiome, which is essential to the cycling of nutrients and overall health of the soil vitality. The safety and nutritional benefits of organic food products were also evaluated. Comparative analysis with conventionally grown foods indicates that organic produce contains lower levels of harmful residues such as pesticides, nitrates, metals, and antibiotics. This reduction in contaminants mitigates potential risks to human health associated with conventional farming practices. Furthermore, organic foods are found to be richer in essential nutrients, vitamins, and antioxidants, thereby offering superior nutritional value. The economic implications of organic farming were explored, highlighting its potential to enhance the economic viability of small-scale farmers. With increasing consumer awareness and demand for organic products, farmers practicing organic methods can command higher prices for their produce, thereby improving their economic sustainability and resilience. In conclusion, organic farming emerges as an environmentally friendly and economically viable alternative to conventional agriculture. By promoting soil health, reducing chemical residues in food, and meeting consumer demand for nutritious products, organic farming offers a pathway towards sustainable agricultural practices.

Comparison of the diagnostic value of various microRNAs in blood for colorectal cancer: a systematic review and network meta-analysis

BackgroundDespite the existence of numerous studies investigating the diagnostic potential of blood microRNAs for colorectal cancer, the microRNAs under consideration vary widely, and comparative analysis of their diagnostic value is lacking. Consequently, this systematic review aims to identify the most effective microRNA blood tumor markers to enhance clinical decision-making in colorectal cancer screening.MethodA comprehensive search of databases, including PubMed, Embase, Web of Science, Scopus, and Cochrane, was conducted to identify case‒control or cohort studies that examined the diagnostic value of peripheral blood microRNAs in colorectal cancer. Studies were included if they provided sensitivity and specificity data, were published in English and were available between January 1, 2000, and February 10, 2023. The Critical Appraisal Skills Programme (CASP) checklist was employed for quality assessment. A Bayesian network meta-analysis was performed to estimate combined risk ratios (RRs) and 95% confidence intervals (CIs), with results presented via rankograms. This study is registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), 202,380,092.ResultsFrom an initial pool of 2254 records, 79 met the inclusion criteria, encompassing a total of 90 microRNAs. The seven most frequently studied microRNAs (43 records) were selected for inclusion, all of which demonstrated moderate to high quality. miR-23, miR-92, and miR-21 exhibited the highest sensitivity and accuracy, outperforming traditional tumor markers CA19-9 and CEA in terms of RR values and 95% CI for both sensitivity and accuracy. With the exception of miR-17, no significant difference was observed between each microRNA and CA19-9 and CEA in terms of specificity.ConclusionsAmong the most extensively researched blood microRNAs, miR-23, miR-92, and miR-21 demonstrated superior diagnostic value for colorectal cancer due to their exceptional sensitivity and accuracy. This systematic review and network meta-analysis may serve as a valuable reference for the clinical selection of microRNAs as tumor biomarkers.

Thoracic versus coronary calcification for atherosclerotic cardiovascular disease events prediction

This study compared the prognostic value of quantified thoracic artery calcium (TAC) including aortic arch on chest CT and coronary artery calcium (CAC) score on ECG-gated cardiac CT.MethodsA total of...

Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs.

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model's performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50's precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing-detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics.

Predicting prognosis in colorectal cancer patients with curative resection using albumin, lymphocyte count and RAS mutations

Colorectal cancer (CRC) poses a significant global health challenge, demanding reliable prognostic tools to guide treatment decisions. This study introduces a novel prognostic scoring system, the albumin-total lymphocyte count-RAS index (ALRI), integrating serum albumin, lymphocyte count, and RAS gene mutations. A cohort of 445 stage I–III CRC patients undergoing curative resection was analyzed, revealing ALRI's association with clinicopathological factors, including age, tumor location, and invasion depth. The ALRI demonstrated superior prognostic value, with a cutoff value of 2 distinguishing high and low-risk groups. The high-ALRI group exhibited elevated rates of recurrence. Univariate and multivariate analyses identified ALRI as an independent predictor for both 5 year recurrence-free survival (RFS) and overall survival (OS). Kaplan–Meier curves illustrated significant differences in RFS and OS between high and low-ALRI groups, emphasizing ALRI's potential as a prognostic marker. Importantly, ALRI outperformed existing nutritional indices, such as controlling nutritional status and neutrophil-to-lymphocyte ratio, in predicting overall survival. The study underscores the comprehensive insight provided by ALRI, combining inflammatory, nutritional, and genetic information for robust prognostication in CRC patients. This user-friendly tool demonstrates promise for preoperative prognosis and personalized treatment strategies, emphasizing the crucial role of inflammation and nutrition in CRC outcomes.

Energy absorption characteristics of a bio-inspired prepreg carbon fiber crash box under quasi-static axial compression

Reducing vehicle weight is crucial for enhancing fuel efficiency and reducing emissions in transportation. Traditional composite materials offer improved energy absorption over metals yet are limited by brittleness. This study introduces an innovative approach, inspired by the mantis shrimp's natural defense mechanisms, to enhance the crashworthiness and energy absorption of composite structures, optimizing safety and performance. Utilizing a bio-inspired design, we developed corrugated Carbon Fiber Reinforced Polymer (CFRP) crash box structures, aiming to optimize their energy absorption capabilities and crash force efficiency (CFE) for potential applications in transportation safety. Through a series of quasi-static axial compression tests, the corrugated structures' performance was evaluated against traditional crash box designs. The experimental results demonstrate that the bio-inspired configurations improved crashworthiness characteristics. Strategic manipulation of layer numbers and corrugations led to superior CFE values, indicative of safer, more controlled collision behavior. The “7N-6L” configuration featuring seven corrugations with six layers of CFRP demonstrated the highest efficacy, achieving an optimal CFE of 1.08. This configuration demonstrated a Specific Energy Absorption (SEA) of 1.56 J/g and an Energy Absorption (Ea) of 42.56 J. Furthermore, compared to conventional steel crash boxes, the CFRP crash box with 7N-6L corrugated structure showcased competitive energy absorption capabilities with significantly reduced mass, absorbing 2850 J with a CFE of 0.91, nearly matching the ideal CFE and highlighting its superior lightweight performance. These results underline the potential of integrating bio-inspired designs to develop robust, lightweight structures for improved crashworthiness, paving the way for safer and more sustainable transportation solutions.

Evaluation of growth performance, carcass, organ characteristics and digestibility coefficient of finisher broiler chicken fed varying levels of Fennel seed powder

A 28-day trial was conducted to evaluate the impact of different levels of fennel seed powder as a feed additive on the growth performance, carcass yield, internal organ characteristics, and nutrient digestibility of finisher broiler chickens. The study involved 200 finisher broiler chickens divided into four groups of 50 birds each. Four experimental diets were formulated with fennel seed powder at 0g, 150g, 300g, and 450g per 100kg of feed, labelled as T1, T2, T3, and T4, respectively. The treatment groups were further subdivided into five replicates with 10 birds in each group. Collected data were subjected to analysis of variance. The results indicated significant differences (p&lt;0.05) in final weight gain (FWG), daily weight gain (DWG), and feed conversion ratio (FCR) among the groups. Birds in T4 group showed the highest FWG and DWG values (p&lt;0.05). Additionally, T4 had the lowest FCR values (p&lt;0.05), significant differences in feed intake (p&lt;0.05) were similarly observed. Significant differences (p&lt;0.05) were recorded in dressed weight, live weight, dressing percentage, and various cut-parts for the birds, birds in T4 recorded superior values for all carcass traits. The internal organs were not affected by the feed (p&gt;0.05). However, the diet significantly influenced abdominal fat, with T4 showing the lowest values (p&lt;0.05). The nutrient digestibility coefficients were significantly affected by the diet, with T4 showing the highest values in all proximate composition parameters analyzed (p&lt;0.05). It was concluded that adding fennel seed powder at 450g per 100kg of feed can enhance the growth performance, carcass quality, internal organ traits, and nutrient digestibility of broiler chickens.

Femur bone volumetric estimation for osteoporosis classification based on deep learning with tuna jellyfish optimization using X-ray images

Osteoporosis is a disorder, that leads to fractures and fatal problems in bones. It is believed that more than 200 million individuals are affected globally. Furthermore, osteoporosis is caused by micro-architectural degeneration of bone tissues, which increases the risk of bone fragility and fractures. Moreover, the osteoporosis categorization is essential for the medical industry, which classifies the skeleton problems of individuals caused by ageing. This work presented the prediction of femur bone volume for osteoporosis classification. Moreover, the femur bone X-ray image is utilized for the classification. The preprocessing phase is employed to neglect the noise contained in input bone images through a non-local means filter. In the image segmentation process, the SegNet is utilized to isolate the specific portion. Moreover, the template search approach based on femoral geometric estimation is carried out and the feature extraction phase is essential for a significant feature extraction process. The proposed tuna jellyfish optimization based deep batch-normalized eLU AlexNet (DbneAlexNet) is utilized in the osteoporosis classification process. Furthermore, accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR) and True Negative Rate (TNR) are the metrics to validate the model and the superior values 0.913, 0.906, 0.896, 0.923 and 0.932 are achieved.

Analysis of Traditional Menu Promotion Case Study: The City of Pagar Alam

This study compares traditional menu promotion efforts' expectations and reality in Pagar Alam, South Sumatra. The focus of attention of food and beverage industry players is customers and competitors. By generating and offering value to their customers, food and beverage industry players can bind a customer, a food and beverage player who has succeeded in presenting superior value. In order to achieve this goal, every industry player must be able to produce and deliver the goods and services desired by consumers, in this case, tourists, according to the needs and desires of these tourists. The data analysis method uses Important Performance Analysis (IPA) on primary data obtained from 83 consumer respondents. The results showed strategies to improve product branding to introduce and improve the specific menu products of natural fences so that tourists more widely knew them. The results showed that labeling was in quadrant D (low expectation and perception values) at an IPA value of 5.40.

Predictive value of ultrasound doppler parameters in neoadjuvant chemotherapy response of breast cancer: Prospective comparison with magnetic resonance and mammography.

Neoadjuvant chemotherapy (NACT) is a treatment option for breast cancer patients that allows for the assessment of tumor response during treatment. This information can be used to adjust treatment and improve outcomes. However, the optimal imaging modalities and parameters for assessing tumor response to NACT are not well established. This study included 173 breast cancer patients who underwent NACT. Patients were imaged with ultrasound (US), mammography (MMG), and magnetic resonance imaging (MRI) at baseline, after two cycles of NACT, and before breast surgery. US parameters included lesion morphology, Doppler variables, and elastography measurements. MMG and MRI were evaluated for the presence of nodules and tumor dimensions. The pathological response to NACT was determined using the residual cancer burden (RCB) classification. The US parameter with the highest power for predicting pathological complete response (pCR) was shear wave elastography (SWE) maximum speed inside the tumor at baseline. For nonluminal tumors, the end diastolic velocity measured by US after two cycles of NACT showed the highest predictive value for pCR. Similarly, SWE maximum speed after two cycles of NACT had the highest discriminating power for predicting RCB-III in luminal tumors, while the same parameter measured at baseline was most predictive for nonluminal tumors. This study provides evidence that mid-treatment Doppler US and other imaging modalities can be used to predict the response to NACT in breast cancer patients. Functional parameters, such as blood flow velocities and SWE measurements, demonstrated superior predictive value for pCR, while morphological parameters had limited value. These findings have implications for personalized treatment strategies and may contribute to improved outcomes in the management of breast cancer.

Systemic inflammation-modified PG-SGA index in the survival prediction of advanced cancers.

12056 Background: The tight association between inflammation and nutrition has been established. Whether the systemic inflammatory indicators could increase the predictive power of nutrition assessment tools in the survival of advanced cancers remains unclear. Methods: In this retrospective study, advanced cancer patients were enrolled from the West China Hospital between Nov 2019 and Sep 2023. Five systemic inflammatory indicators were selected including the systemic inflammatory index (SII), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and advanced lung cancer inflammation index (ALI). Nutritional status was evaluated by the PG-SGA. Kaplan-Meier curves, restricted cubic splines (RCS), and Cox regression analyses were used to estimate the effects of systemic inflammation indices as well as systemic inflammation-modified PG-SGA index on the survival of cancer patients. Stratified and sensitive analyses were conducted to determine the high-risk subgroups and check the robustness of the main findings. Results: In total, there were 1,065 advanced-stage cancer patients included in this study, with a mean age of 58.18 years old. Advanced lung cancer (265 cases, 24.9%) accounted for relatively higher proportions of the whole study population, followed by colorectal cancer (167 cases, 15.7%), esophagus cancer (143 cases, 13.4%), and gastric cancer (139 cases, 13.0%). With a median follow-up of 1,300 days, 501 deaths (47.04%) were observed. Multivariate Cox regression analysis showed that ALI maintained the best predictive power in predicting the all-cause mortality of advanced-stage cancer patients when compared with other systemic inflammation indicators (4th quartile: hazard ratio (HR)= 0.38, 95% confidence interval (CI): 0.29, 0.50, P&lt;0.001), with a C-index of 0.615. The novel inflammation-modified PG-SGA/ALI index showed better predictive value in identifying the high-risk subgroups of advanced-stage cancer patients, with an AUC of ROC of 0.68 in 365-day mortality, 0.65 in 730-day mortality, and 0.64 in 1300-day survival prediction, respectively. The C-index (0.626), calibration curves, and decision curve analysis validated the promising predictive accuracy of the PG-SGA/ALI index in predicting the all-cause mortality of advanced-stage cancer patients. No significant interaction was observed between subgroups. A series of sensitive analyses supported the consistent findings as we determined in the main regression. Conclusions: Our study highlights that the PG-SGA/ALI index has superior predictive value than original PG-SGA criteria in short- and long-term survival prediction of advanced cancers. The PG-SGA/ALI index would be a more accurate tool for nutritional evaluation and prognosis prediction among cancer patients during clinical practice.

Long non-coding RNA as potential diagnostic markers for acute myeloid leukemia: A systematic review and meta-analysis.

Acute myeloid leukemia (AML) is aggressive type of hematological malignancy. Its poses challenges in early diagnosis, necessitating the identification of an effective biomarker. This study aims to assess the diagnostic accuracy of long noncoding RNAs (lncRNA) in the diagnosis of AML through a meta-analysis. The study is registered on the PROSPERO website with the number 493518. A literature search was conducted in the PubMed, Embase, Hinari, and the Scopus databases to identify relevant studies. We pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristics (ROC) using Stata 14.1 software. Heterogeneity between studies was determined through the I2 statistic and Cochran-Q test. A random effect model was chosen due to significant heterogeneity among included studies. Meta-regression and subgroup analysis were performed to assess the potential source of heterogeneity. Furthermore, potential publication bias was estimated using Deek's funnel plot asymmetry test. A total of 14 articles covering 19 studies were included in this meta-analysis comprising 1588 AML patients and 529 healthy participants. The overall pooled sensitivity, specificity, PLR, NLR, DOR, and the area under the summary ROC curve were 0.85 (95% CI = 0.78-0.91), 0.82 (95% CI = 0.72-0.89), 4.7 (95% CI = 2.9-7.4), 0.18 (95% CI = 0.12-0.28), 26 (95% CI = 12-53), and 0.90 (95% CI = 0.87-0.93), respectively. Moreover, lncRNAs from non-bone marrow mononuclear cells (BMMC) had superior diagnostic value with pooled sensitivity, specificity, and AUC were 0.93, 0.82, and 0.95, respectively. This meta-analysis demonstrated that circulating lncRNAs can serve as potential diagnostic markers for AML. High accuracy of diagnosis was observed in non-BMMC lncRNAs, given cutoff value, and the GADPH internal reference gene used. However, further studies with large sample size are required to confirm our results.

A Systematic Review of Financial Factors Affecting Implementation of Lean Principle in Small and Medium Enterprises of Saudi Arabia

Abstract The application of the lean principle can play an important role in enhancing the competitiveness and performance of small and medium-sized enterprises (SMEs). The lean principle can reduce the cost of production, maximize resource optimization, and enhance the firm’s ability to provide superior value to the customers. The empirical research studying the effect of the implementation of lean principles in the SMEs has been widely studied. The researchers have used varying amounts of contextual variables as antecedents and consequences of the lean principle implementation in SMEs. The purpose of the present research study is to address this gap by reviewing the literature on Lean implementation in SMEs with a focus on finance-related variables as antecedents and consequences. To achieve the purpose of our study, the current research has employed a systematic literature review as a methodology to collect the studies from academic databases of ABI/INFORM world, Taylor &amp; Francis, Emerald, Sage, Inderscience, Premier, ScienceDirect, and Scopus Business Supplies. The results of the study have yielded insight and knowledge into four different themes. Finally, an area for future research has also been developed.

Assessment of the Micro-Tensile Bond Strength of a Novel Bioactive Dental Restorative Material (Surefil One).

The aim of this study is to assess the micro-tensile bond strength and the mode of failure of a bioactive hybrid self-adhesive composite (Surefil one) under various dentin conditions. Thirty-two extracted human molar teeth were used to test the micro-tensile bond strength of Surefil one under different dentine conditions (no treatment, 37% phosphoric acid etching, and universal adhesive) in comparison with a resin-modified glass ionomer (RIVA). All restorations were light cure-bonded onto flat dentine and then sectioned into beams. Then, fractured specimens were observed under a light microscope to evaluate the mode of failure. The Surefil one no-treatment group (NTG) exhibited the highest micro-tensile bond strength. Furthermore, there was no statistically significant difference observed between the Surefil one adhesive group (EAG) and the Surefil one acid etch group (EG). However, compared to other groups, the resin-modified glass ionomer (RIVA) produced the lowest results, which are statistically significant. Surefil one offers superior bond strength values when compared to resin-modified glass ionomers. Furthermore, Surefil one requires no dentin condition and has more straightforward clinical steps.

Artificial Intelligence-Powered Optimization and Milk Permeate Upcycling for Innovative Sesame Milk with Enhanced Probiotic Viability and Sensory Appeal.

Consumer demand for plant-based alternatives drives innovation in nondairy beverages. This study explores the development of a novel sesame milk with enhanced functionality using an artificial neural network (ANN) and milk permeate integration. An ANN model effectively optimized water-based sesame milk (WSM) extraction, maximizing total solids (T.S.) recovery. The ANN model's predicted T.S. yield (99.65%) closely matched the actual value (95.18%), demonstrating its potential for optimizing high-yield production. Furthermore, milk permeate was incorporated (5:1 ratio) to create permeate-based sesame milk (PSM), which supported the growth of lactic acid bacteria, suggesting its potential as a growth medium for future probiotic applications. PSM also displayed superior nutritional value and sensory characteristics compared to WSM. These findings highlight the promise of ANN-powered optimization and milk permeate integration for creating innovative sesame milk alternatives with enhanced probiotic viability and sensory appeal. Future research should focus on ANN optimization of alternative-based-plant milk, including permeate-based sesame milk production, the health benefits of LAB fermentation, and consumer preferences for flavors and textures. Optimizing fermentation and LAB selection remain key for commercial success.

Ni Single Atom Decorated Porous Hollow Carbon Nanosphere-Based Electrodes for High Performance Symmetric Solid-State Supercapacitors.

Ni single atom containing hollow carbon nanospheres with nitrogen doping has been synthesized by carbonization of Ni(NO3)2/phloroglucinol-formaldehyde polymer/silica composite. The samples have been characterized by powder X-ray diffraction, nitrogen adsorption/desorption, electron microscopic, Raman and X-ray photoelectron spectroscopic studies. The microstructure and surface area vary with the amount of Ni(NO3)2 employed in the syntheses and the carbonization environment. An optimized amount of nickel and argon as the carbonization gas afford Ni-1.0@N@HCN-Ar which possesses overall superior features. The uniformly dispersed Ni single atoms within the hollow porous carbon framework fully utilize all the electroactive sites thereby improving the supercapacitive performance. The specific capacitance of Ni-1.0@N@HCN-Ar reaches 777 F g-1 at 1 A g-1 with a Coulombic efficiency of 98.4 % and excellent recyclability. The energy and power density of Ni-1.0@N@HCN-Ar are found to be high; at 1 A g-1 its energy density is 155.4 Wh kg-1 with a power density of 600.3 W kg-1. At a high current density of 10 A g-1 the material shows a high energy density of 118.4 Wh kg-1 with excellent power density of 6003.4 W kg-1. A symmetric solid-state supercapacitor assembled with this material, Ni-1.0@N@HCN-Ar//Ni-1.0@N@HCN-Ar using H2SO4/PVA gel electrolyte shows a superior energy density value of 30 Wh kg-1 at a power density of 1200 W kg-1.