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Cumulating evidence suggests that high levels of spirituality can foster integrity. However, integrity violations remain prevalent, even among populations with strong religious beliefs. This study collected data from 2,800 students across 17 academic institutions in Indonesia and Malaysia to examine the relationship between their levels of spirituality and their perceptions of academic cheating. Although both countries have predominantly Muslim populations, they differ in how religious practices are implemented. The results revealed a significant correlation between spirituality and perceptions of cheating in both countries. However, one aspect of spirituality in Indonesia diverged from the broader concept, underscoring the complex and nuanced relationship between spirituality and religion. While these concepts often overlap, they also exhibit distinct differences. Such differences in spirituality and religiosity may help explain the paradox of integrity issues, including the prevalence of cheating within religious communities.

AbstractMicroneedle (MN) delivery devices are more accepted by people than regular traditional needle injections (e.g. vaccination) due to their simplicity and adaptability. Thus, patients of chronic diseases like diabetes look for alternative pain-free treatment regimens circumventing regular subcutaneous injections. Insulin microneedles (INS-MNs) are a thoughtfully researched topic (1) to overcome needle phobia in patients, (2) for controlled delivery of the peptide, (3) decreasing the frequency of drug administration, (4) to ease the drug administration procedure, and (5) thus increasing patient adherence to the treatment dosage regimes. MNs physically disrupt the hard outer skin layer to create minuscule pores for insulin (INS) to pass through the dermal capillaries into the systemic circulation. Biodegradable polymeric MNs are of greater significance for INS and vaccine delivery than silicon, metal, glass, or non-biodegradable polymeric MNs due to their ease of fabrication, mass production, cost-effectiveness, and bioerodability. In recent years, INS-MNs have been researched to deliver INS through the transdermal implants, buccal mucosa, stomach wall, intestinal mucosal layers, and colonic mucosa apart from the usual transdermal delivery. This review focuses on the design characteristics and the applications of biodegradable/dissolvable polymeric INS-MNs in transdermal, intra-oral, gastrointestinal (GI), and implantable delivery. The prospective approaches to formulate safe, controlled-release INS-MNs were highlighted. Biodegradable/dissolvable polymers, their significance, their impact on MN morphology, and INS release characteristics were outlined. The developments in biodegradable polymeric INS-MN technology were briefly discussed. Bio-erodible polymer selection, MN fabrication and evaluation factors, and other design aspects were elaborated.

Overuse of synthetic antibiotics and the emergence of multidrug resistance (MDR) in ESKAPE bacteria are major clinical concerns on a global scale. ESKAPE bacteria are responsible for most hospital-acquired infections because of their capacity to create robust biofilms. One of the most important targets for medications is biofilm-regulated bacterial pathogenicity. In the Indian medical system, Withania somnifera (L.) Dunal (WS) is an important medicinal plant used to treat illnesses. However, no research has been conducted to assess its antibiofilm potential against MDR pathogens and anti-aging potential. The reported study assessed the potential of WS root aqueous extract against ESKAPE biofilms and linked traits including EPS and alginate production, CSH, swarming and swimming motilities, and production of matrix components (eDNA, eProtein, and eCarbohydrates) of such pathogens in vitro. Concurrently, the root extract was tested for its ability to inhibit enzymes such as tyrosinase, collagenase and elastase contributed to skin aging. The root extract's MIC value against all six ESKAPE pathogens was 2.0 mg/mL. Bacterial biofilms were quantified at a sub-MIC value of 1.0 mg/mL using a microtiter plate assay and matrix components were evaluated using Dispersin B (DspB) extractant. The sub-MIC concentration significantly reduced biofilm formation and associated characteristics; the utmost inhibition was 59.9 % in all six tested pathogens. A likewise pattern of inhibition in matrix components was recorded, and highest reduction was 53.4 % in the ESKAPE pathogens. Additionally, the root extract was tested for its anti-aging potential. The sub-MIC concentrations suppressed the three major aging enzymes up to 31.47 %. The present study reveals that WS root extract imparts a novel anti-biofilm effect against ESKAPE, as well as offering skin anti-aging potential, for the first time. Thus, WS root could be utilized as a potential herbal remedy against nosocomial infection and aging of skin.

Alternative fuel recovery from used engine lubricant (UEL) and empty fruit bunch (EFB) was achieved through microwave co-pyrolysis. Co-pyrolysis was chosen for its potential to improve the quality of pyrolytic oil by generating synergistic effects between two distinct feedstocks, reducing activation energy, and enhancing pyrolytic oil quality. The central composite design (CCD) of response surface methodology (RSM) was used to optimise the temperature and EFB ratio. Atomic absorption spectrometry (AAS) was employed to characterise the heavy metal concentration in the pyrolytic oil. The optimised pyrolytic oil (UE450) produced the highest oil yield (25.17 wt%) with the lowest metal concentration at 450 °C with a 50 % EFB ratio. The fuel’s characteristics were similar to those of conventional diesel, with a higher value of HHV (45.17 MJ/kg). However, the oil was slightly acidic, with a pH of 4.3. GC–MS analysis of UE450 revealed the presence of alkanes and monoaromatic-rich hydrocarbons. Additionally, the UE450 biochar was characterised using FTIR, FESEM, and XRF. FTIR analysis showed that the carbonyl group (C = O) peaks at 1730 and 1440 cm−1 disappeared, indicating that heavy metals were bound to the biochar surface. Likewise, XRF analysis of UE450 biochar revealed that zinc (Zn) exhibited a high metal adsorption capacity, following the sequence Zn > Fe > Pb (1.96, 1.06, and 0.81 mmol/g). The XRF results also indicated a significant removal of SO3 at approximately 10.37 mmol/g.

This study explores the relationship between light physical activity (LPA) and moderate-vigorous physical activity (MVPA), depression and physical fitness among Shanghai junior high school students. A nationwide offline survey was conducted in Shanghai between August and December 2023 using a purposive sampling design. A total of 432 questionnaires were administered to six seventh-grade classes and eighth-grade classes from six schools in Shanghai; 416 responses were included in the final analysis. SPSS software version 29, the Correlation and Multiple Linear Regression techniques were employed to analyze the collected data. Higher levels of LPA and MVPA were correlated with lower levels of depression. However, only LPA was a significant positive predictor for depression among Shanghai junior high school students. MVPA did not show significant predictive power for depression. Only MVPA was positively correlated with 50 meters (50M), standing long jump (SLJ), 800/1000 meters (800/1000M), and total physical fitness (TPF), but not for sitting forward bend (FB) and vital capacity (VC); various measures of physical fitness were found highly correlated with each other. MVPA was also a significant positive predictor for 800/1000M, SLJ, 50M and TPF but not for VC and FB. LPA did not show significant predictive power for any physical fitness measures. The findings highlighted the importance of MVPA in enhancing physical fitness outcomes, while LPA primarily impacted depression levels. Various measures of physical fitness were highly interrelated. The results underscored the significance of promoting MVPA to improve physical fitness and reduce depressive symptoms in this population.

This study examines the relationship between factors (social support, self-esteem, emotional intelligence [EI], mindfulness, school PA program engagement [SPPE]), physical activity (PA), academic self-efficacy (ASE), and depression in Shanghai junior high school students. A nationwide offline survey was conducted in Shanghai between August and December 2023 using a purposive sampling design. A total of 432 questionnaires were administered to six seventh- and eighth-grade classes from six schools in Shanghai; 416 responses were included in the final analysis. SPSS software version 29, the Correlation and Multiple Linear Regression techniques were employed to analyze the collected data. Social support, self-esteem, EI, mindfulness, SPPE, PA, ASE, and depression were found to be correlated. None of the common factors significantly predict PA, as the p-values were more than 0.05. Social support, self-esteem, EI, and SPPE had a positive significant influence on ASE except for mindfulness. Social support, self-esteem, and mindfulness had a negative significant influence on depression among Shanghai junior high school students. This study highlights the interconnectedness of social support, self-esteem, EI, mindfulness, and SPPE with ASE and depression among junior high school students in Shanghai. While none of the factors predicted PA levels, social support, self-esteem, EI, and mindfulness emerged as important contributors to ASE, while social support, self-esteem, and mindfulness emerged as important contributors to depression.

The lack of breaststroke swimming ability among athletes has led to a decline in breaststroke swimming performance in the Solok city swimming association. This problem is thought to be related to the low endurance of leg and arm muscles to the swimming speed of 200 meters breaststroke. This correlational study aims to reveal the contribution of leg muscle endurance, arm muscles, and the joint contribution of both to swimming speed 200 meters breaststroke in athletes coaching achievements in the Solok city swimming association. This study involved 25 swimming athletes in Solok city, with total sampling technique. Data on leg muscle strength endurance was measured through the squat jump test, arm muscle strength endurance through the push up test, and swimming speed through the 200 meter breaststroke swimming test. Data were analyzed using simple and multiple correlations with a significant level of α = 0.05, and the contribution was calculated by the formula r² x 100%. The results of data analysis showed that leg muscle strength endurance contributed 74.65%, while arm muscle strength endurance contributed 70.06%. The combination of both contributed 74.99% to the ability of 200 meters breaststroke swimming speed of athletes coaching achievements in swimming associations in Solok city.

Phenotypic switching of vascular smooth muscle cells (VSMCs) is crucial in atherosclerosis. Gypenoside granules, used clinically for hyperlipidemia, show significant atheroprotective effects, but their impact on VSMC phenotypic switching is unclear. This study evaluated the effects and mechanisms of gypenoside granules on VSMC phenotypic switching. Using ApoE−/− mice on a high-fat diet, the study found that gypenoside granules reduced blood lipid levels, decreased necrotic core areas, increased fibrous cap thickness, and inhibited inflammation. In PDGF-BB-induced VSMCs, gypenoside granules suppressed phenotypic switching, proliferation, and migration by downregulating KLF4 expression. The beneficial effects were reversed by KLF4 overexpression. HPLC analysis identified ten key components in the granules. These findings suggest that gypenoside granules may alleviate atherosclerosis by modulating the MAPK/KLF4 pathway and could serve as an alternative treatment for the disease.

1. Abstract: Acute myeloid leukemia (AML) is a highly aggressive cancer form that affects myeloid cells, leading to the excessive growth of immature white blood cells (WBCs) in both bone marrow and peripheral blood. Timely AML detection is crucial for effective treatment and patient well-being. Currently, AML diagnosis relies on the manual recognition of immature WBCs through peripheral blood smear analysis, which is time-consuming, prone to errors, and subject to inter-observers’ variation. This study aimed to develop a computer-aided diagnostic framework for AML, called ''CAE-ResVGG FusionNet'', that precisely identifies and classifies immature WBCs into their respective subtypes. The proposed framework leverages an integrated approach, by combining a convolutional autoencoder (CAE) with finely tuned adaptations of the VGG19 and ResNet50 architectures to extract features from CAE-derived embeddings. The process begins with a binary classification model distinguishing between mature and immature WBCs followed by a multiclassifier further classifying immature cells into four subtypes: myeloblasts, monoblasts, erythroblasts, and promyelocytes. The CAE-ResVGG FusionNet workflow comprises four primary stages, including data preprocessing, feature extraction, classification, and validation. The preprocessing phase involves applying data augmentation methods using geometric transformations and synthetic image generation using the CAE to address imbalance in the WBC distribution. Feature extraction involves image embedding and transfer learning, where CAE-derived image representations are used by a custom integrated model of VGG19 and ResNet50 pretrained models. The classification phase employs a weighted ensemble approach that leverages VGG19 and ResNet50, where the optimal weighting parameters are selected using a grid search. The model performance was assessed during the validation phase using the overall accuracy, precision, and sensitivity, while the area under the receiver characteristic curve (AUC) was used to evaluate the model’s discriminatory capability. The proposed framework exhibited notable results, achieving an average accuracy of 99.9%, sensitivity of 91.7%, and precision of 98.8%. The model demonstrated exceptional discriminatory ability, as evidenced by an AUC of 99.6%. Significantly, the proposed system outperformed previous methods, indicating its superior diagnostic ability.

The combination of the Finite Element Method (FEM) with Convolutional Neural Networks (CNNs) presents a key breakthrough in the assessment of the structural integrity of offshore pipelines. The advantage of the standard FEM is in stress visualization, but it is time-consuming due to high computational analysis. This research aims to quickly and accurately determine the severity of pipeline corrosion categorized as high, intermediate, or low through stress images generated from FEM. A transfer-learning algorithm was applied to refine and validate the model using a diverse image dataset of uniformly corroded pipelines (200x200 mm, 100x100 mm, 75x75 mm, 50x50 mm, and 10x10 mm), annotated with corresponding severity levels. Moreover, the model was validated for prediction with irregular-sized corroded pipelines (50x100 mm and 10x100 mm). Both samples are modeled for degrees of corrosion, 30%, 50%, and 70% of the corrosion depth, with API 5L X42 specifications. An exceptional predictive accuracy was observed, attaining average confidence levels between 97% and 100%. This work substantially augments the effectiveness of structural analyses that provide a better safety feature for critical infrastructural assets within the oil and gas industry and has great advantages to engineers, researchers, and academicians working on pipeline integrity management.

In Algeria, glass waste is underutilized in the industrial sector; however, its potential use in civil engineering offers a significant ecological and economic opportunity. This approach could be a solution to eliminating illegal dumping sites, reduce pollution, and provide a new source of sustainable construction materials. In this context, this research aimed to produce self-compacting concrete (SCC) mixtures using recycled glass powder as a replacement for limestone filler. This research presents an experimental study investigating the impact of glass powder waste as a replacement for the traditionally used limestone filler in self-compacting concrete. To investigate the workability and compressive strength of the SCC studied, eleven concrete mixtures were prepared with varying substitution rates of limestone filler (0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) with powder glass. The use of powdered glass waste has beneficial effects, as the pozzolanic reaction generates an additional amount of hydrated calcium silicates. The results of the investigation showed an increase in compressive strength compared to the control concrete specimen (without glass powder). The best results were observed when incorporation ranged between 10% and 50%, with the optimal level being 30%, resulting in an 8.47% strength gain. This study contributes to the valorization of glass powder as a substitute for limestone filler. The results demonstrate positive effects on both fresh and hardened characteristics when using glass powder in proportions ranging from 10% to 50% of the filler mass.

Efficient identification of road defects is a critical concern for road safety and infrastructure upkeep. This research employs drone-captured imagery and advanced object detection algorithms to expedite defect recognition, with a specific focus on determining the optimal algorithm for prompt and precise detection. The importance of timely road defect detection, crucial for mitigating potential hazards, remains central. A comprehensive comparative analysis of contemporary object detection algorithms, encompassing YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv7. The results of this study highlight YOLOv7 as the most efficient, with a notable mAP of 68.3%, closely followed by YOLOv5l (66.8%), YOLOv5m (66.3%), YOLOv5x (66%), and YOLOv5s (63%). The integration of drone-derived imagery, capturing distinct gradients, significantly enhances defect detection accuracy. Beyond road safety, this study offers valuable insights to computer vision and machine learning practitioners. By bridging technological innovation with practical implementation, it holds potential to advance road safety and transportation infrastructure quality and the use of revolutionary drone technology.

Background: The large number of mildly disabled elderly people in China has resulted in the occupation of a large amount of public social resources. They have also imposed a serious social burden on the society. This study predicts that changes in the level coefficients of physical functioning, motor participation, and external environment are associated with quality of life (QoL) among the elderly with mild disabilities in nursing homes. Methods: A simulation model was created using the system dynamics method. Meanwhile, a 50-day simulation was used to predict the subsequent changes in the level of QoL of the elderly. This study was completed in the nursing homes of Chengdu, China, using random sampling and 85 elderly subjects participated to obtain the primary data. The data were analyzed using the Vensim software. Results: The results showed that the level of physical functioning (pain value of 0.241, autonomy of 0.163, and chronic disease of 0.192), the level of motor participation (education value of 0.186, and rehabilitation partner of 0.21), and the level of external environment (medical care value of 0.26, and rehabilitation environment value of 0.184) lead to subsequent changes in the level of QoL of mildly disabled elderly in nursing homes in 30 days. Conclusion: Considering the results of this simulation study, changes in the level of physical functioning, exercise participation, and external environment could forecast the QoL of mildly disabled elderly in the nursing homes of Chania, China. It is necessary to make the required changes in the nursing homes to promote the QoL of these groups.

AbstractEx utero intrapartum treatment (EXIT) has been described as a safe procedure to secure challenging fetal airways while on placental support. Here we present an extremely rare case of huge infantile tongue fibrosarcoma diagnosed prenatally and successfully delivered via EXIT. Initial diagnosis of infantile tongue haemangioma was made based on physical examination, radiological findings and high incidence of infantile haemangioma in the first year of life hence trial of oral propranolol was given. Tracheostomy was performed at 1 week of life in anticipation of airway compromise and failed extubation. A biopsy of the tongue tumour was performed to rule out soft tissue malignancy. Histopathological examination (HPE) revealed a malignant tumour suggestive of infantile fibrosarcoma. The management of infantile fibrosarcoma is a multidisciplinary team’s involvement. However, non-mutilating surgery should be the primary treatment for infantile fibrosarcoma aiming for complete excision. Neoadjuvant chemotherapy is indicated when upfront resection is unfeasible as in the present case.

Moringa oleifera L. is a valuable multipurpose tree species widely planted for centuries due to its high medicinal value and antifungal, antiviral, antidepressant, and anti-inflammatory properties in the food industry. However, its cultivation is hindered by production constraints such as the unavailability of planting material and the inadequate number of high-yielding clones. Thus, a study was initiated to select high-yielding clones in terms of growth and chemical content for the mass propagation of superior moringa trees. Screening on high-yielding clones with high astragalin content was conducted through the high-performance liquid chromatography (HPLC) analysis of moringa leaf extract. Selected genotypes were evaluated for their anti-inflammatory potential through in vitro bioactivity assays of leaf methanol extract. The effects of the rooting hormone, rooting substrates, and size of the cutting on the rooting response of branch cuttings of moringa were investigated. Results found that samples collected from different ecological zones of Peninsular Malaysia show significant variation in terms of astragalin content. The extracts were observed to show considerable variation in biological activity against the pro-inflammatory enzymes. The size of the cuttings had significant effects on the rooting of the cuttings as longer cuttings with bigger diameters rooted better than shorter cuttings with smaller diameters. Several genotypes of M. oleifera with superior phenotypic characteristics and bioactive compounds have been identified. Factors affecting the rooting efficiency and optimal conditions of rooting are suggested, which provides valuable information for the propagation of the superior planting material of moringa. This effort will ensure the sustainable production and supply of good quality raw materials for the production of quality end-products for the food and pharmaceutical industry.

Abstract High levels of particulate matter in the air, caused by air pollution from neighbouring countries, have always been a major problem in Malaysia. For many years, Malaysia has experienced a hazy atmosphere due to high levels of particulate matter (PM10) from regional biomass burning and other human activities. This study aims to analyse the changes in PM10 levels over time in Malaysian Borneo. Data collected from air quality monitoring stations over a 10-year period (2006–2016) were obtained from the Malaysian Department of Environment (DOE). Statistical analyses were conducted using the Mann Kendall test to examine trends in PM10 concentrations. The study divided Sabah and Sarawak into three regions: Northern Malaysian Borneo, Central Malaysian Borneo, and Southern Malaysian Borneo. Throughout the studied period, the highest levels of PM10 were primarily found in Southern Malaysian Borneo, with the highest concentration recorded in Sibu (434.38 µg m−3). The monitoring stations in Miri, Limbang, and Kota Kinabalu showed an increasing pollution trend, while Kuching, Sri Aman, Bintulu, Kapit, Sandakan, Tawau, and Keningau showed a significant decreasing trend. No significant trend was observed in Kota Samarahan and Sarikei. The highest annual PM10 exceedances, surpassing the Recommended Malaysian Ambient Air Quality Guideline (RMAAQG) of 150 µg m−3, occurred in 2015 and 2009 with 80 and 65 days respectively in 2006. Biomass burning is identified as the primary source of emissions, contributing to the significant monthly and seasonal variations in this region. Meteorological conditions and the El Niño phenomenon were observed to exert a significant influence on the concentration and distribution of PM10 in this area. In order to improve air quality in Malaysian Borneo, it is necessary to take a multifaceted approach encompassing source emissions reduction, inter-country collaboration, region-wide strategies for land and forest management improvement, and reinforced cooperation on pollution monitoring, reporting and reduction efforts.

Strengthening the evaluation of teaching satisfaction plays a crucial role in guiding teachers to improve their teaching quality and competence, as well as in aiding educational institutions in the formulation of effective teaching reforms and plans. The evaluation process for teaching satisfaction is usually regarded as a typical multi-attribute decision-making (MADM) process, which inherently possesses uncertainty and fuzziness due to the subjective nature of human cognition. In order to improve the subtle discrimination of evaluation information data and enhance the accuracy of the evaluation results, we have developed an integrated MADM method by combining a new distance measure and an improved TOPSIS method for interval-valued intuitionistic fuzzy sets (IvIFSs). First, a novel distance measure for IvIFSs based on triangular divergence is proposed to capture the differences between two IvIFSs, and some properties of this distance measure are investigated. Then, the superiority of this new distance measure is compared with some existing distance measures. Afterward, an improved TOPSIS method is also established based on the proposed triangular distance under the interval-valued intuitionistic fuzzy setting. Besides, to illustrate the practicality of the new method, a numerical example is presentedto evaluate mathematics teaching satisfaction. Moreover, a comparative analysis that includes existing TOPSIS methods, is presented to demonstrate the superiority of the given method. The comparison outcomes show that the proposed technique can effectively discern uncertainties or subtle differences in IvIFSs, resulting in more accurate and comprehensive evaluation results for teaching satisfaction. Overall, the findings of this study emphasize the importance of incorporating the new distance measure in MADM. The proposed approach serves as a valuable tool for decision-makers to compare and evaluate alternatives effectively.

Soybean-wheat is the predominant cropping system covering >2.5 Mha area in India. The lower productivity of soybean-wheat cropping system (SWCS), remains a serious concern primarily due to inadequate nutrient management. Increasing sulfur (S) deficiency is widespread, especially under oilseed-based cropping system. Hence, to standardize the S requirement through customized fertilization, an experiment was conducted in completely randomized block design (RBD) comprised of 12 nutrient sources, replicated thrice. The study aims to evaluate the agronomic performance of sulfonated nitrogen (SN) in comparison to conventional S nutrient sources in SWCS. The maximum soybean productivity was recorded under NPK + S through 40-0-0-13 (SN1), although NPK + 50% S (15 kg/ha) as basal and 50% (15 kg/ha) as top dressing through SN2 10-0-0-75 produced maximum wheat grain yield. When compared with no nitrogen (control), the application of 30 kg S ha−1 to both crops increased the productivity of the soybean-wheat cropping system up to 39%. The maximum system (SWCS) productivity (8.45 tha-1) was obtained with the application of 50% S as basal and 50% as top dressing (SN2-based), remaining N through urea. The highest sustainable yield index of soybean (SYIS), i.e. 0.90 was under SN1+ remaining N through urea and likewise highest sustainable yield index of wheat (SYIW) was under S splitting. The application of SN also improved the nutrient acquisition and grain quality of soybean and wheat with a positive nutrient balance in the soil. The protein content and yield of soybean and wheat grains also improved. The higher gluten content in wheat grain was produced with 60 kg S ha−1 applied. The agronomic efficiency of N and S (AEN and AEs) were highest under SN1 and SN2, respectively (32.8 kg grain/kg N applied; 15 kg grain/kg S applied) in soybean, however in wheat, S splitting and urea application resulted in highest agronomic efficiency (AEN and AES) of N and S (17.1 kg grain/kg N applied; 22.3 kg grain/kg S applied respectively). Hence splitting of S doses of SN along with urea and recommend P, K was found efficient for the soybean-wheat cropping system.

Abstract Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification, including ResNet50, VGG19, and InceptionV4; They were trained and tested on an open-source satellite image dataset to analyze the algorithms' efficiency and performance and correlated the classification accuracy, precisions, recall, and f1-score. The result shows that InceptionV4 gives the best classification accuracy of 97% for cloudy, desert, green areas, and water, followed by VGG19 with approximately 96% and ResNet50 with 93%. The findings proved that the InceptionV4 algorithm is suitable for classifying oil spills and no spill with satellite images on a validated dataset.

AbstractPoverty reduction and sustainability are the two major issues in achieving sustainable development. Microfinance emerged as an essential catalyst for socioeconomic development and financial inclusion to reduce poverty. MFIs cannot meet their primary objective of poverty reduction if they are not sustainable financially. With the theoretical support of the Profit Incentive theory, this paper examines the financial sustainability of microfinance providers (MFPs) in Bangladesh. A financial sustainability index (FSI) is developed by using Principal Component Analysis (PCA). This study analyzes the data using two-step system GMM from 2006 to 2018 collected from the MIX market of the World Bank. The results show that loan size, number of borrowers, percentage of women borrowers, and inflation significantly impact FSI positively. Organizational structure, liquidity, leverage, cost per borrower and GDP have significant negative impacts on the financial sustainability of the microfinance sector of Bangladesh. Upon further analysis, the estimates demonstrated that national governance indicators have a negative impact on the relationship between organizational structure, average loan balance per borrower and FSI. Similarly, a stronger national governance reduces (erases) the negative effect of number of borrowers and cost per borrower on FS of MFPs of Bangladesh. This study incorporated all six dimensions of the national governance indicators and developed a new financial sustainability index for measuring the financial sustainability of microfinance providers.

The article presents data on the quantification of gallic acid (GA) and the assessment of the antioxidant and anti-inflammatory properties of Kacip Fatimah (Labisia pumila Benth. & Hook. f.) leaves using various solvents. GA was quantified using high-performance liquid chromatography analysis. Total phenolic content (TPC) was assessed using the Folin-Ciocalteu method. The antioxidant activities were evaluated using xanthine oxidase superoxide (XOD-Superoxide) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays, while anti-inflammatory activities were examined through lipoxygenase (LOX) and xanthine oxidase (XOD) inhibition assays. Results showed that the water-extracted sample had the highest GA and TPC among the solvents tested, along with the strongest inhibition activities in the XOD-Superoxide and DPPH assays. Both water and ethanol extracts showed significant inhibitory activities in the LOX assay but were inactive in the XOD assay. These findings suggest that the bioactivity of L. pumila leaf extract is associated with GA and TPC. GA and TPC strongly correlated with antioxidant and anti-inflammatory activities, except for the XOD assay. The dataset highlights the potential dietary benefits of L. pumila leaves as a natural source of antioxidants and anti-inflammatory properties for pharmaceutical and nutraceutical applications.

Nutrient enrichment is one of the main reasons causing water quality deterioration and eutrophication in aquaculture systems, such as tanks, ponds, and natural water bodies where cage aquaculture is located. The integration of aquatic plants and substrates synergistically utilises nutrients for the development of biofilm and plant growth, thus improving the water quality in tanks. The experiment was performed in three trials to assess the use of commercial plants integrated with substrates in enhancing nutrient removal in nutrient-enriched mesocosms. Among the plants, the combination of water spinach and lemon basil exhibited significantly higher (P < 0.05) nutrient removal without causing adverse effects on plant growth when compared to the other treatments. For the substrates, the lava rock and bamboo showed the highest (P < 0.05) periphyton development and productivity. The integration of water spinach and lava rock resulted in the highest (P < 0.05) nutrient removal and plant relative growth rates indicating the positive effects of synergistic interaction between plants and microbial-periphyton colonised substrates. A total of 27 bacterial phyla (mainly non-pathogenic) were identified in the integrated water spinach and lava rock substrate treatment. Microbial community structure analysis showed that Proteobacteria, Planctomycetota, Verrucomicrobiota and Bacteroidota were the main groups found in water, roots of water spinach, and substrates. The highest (P < 0.05) bacterial diversity was observed in the substrates, followed by plant roots and water. This study illustrated that the water quality could be significantly improved by integrating suitable plants and microbial-periphyton colonised substrates in tanks.

AbstractRecently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on the Scopus database. Therefore, this article aims to provide an update on the relevance of ANN in offshore engineering over the past 18 years (2005–2023) through a bibliometric analysis using Excel and VOS Viewer software. This analysis highlights the yearly increase in publications related to ANN implementations in offshore engineering and identifies the most cited publications, citation network analysis, authors, keywords, journals, institutions, and leading countries. The objective of this bibliometric analysis is to assist subsequent research and collaboration in this field by shedding light on ANN’s potential and identifying areas for further application. The identified cluster area publications encompass a range of topics, including drilling systems and the assessment of pipes. Furthermore, the significant fourfold increase in publications since 2005 indicates a growing interest among researchers in adapting ANN for various applications within this field. This could lead to further advancements, innovations, and improved solutions to promote collaboration and knowledge-sharing among researchers in this domain.

Probabilistic linguistic terms set (PLTS), a new tool for expressing uncertain decision information, is composed of all possible linguistic terms (LTs) and their related probabilities. It also increases the corresponding probability of LTs in hesitant fuzzy linguistic term set (HFLTS). On the other hand, aggregation operator is an important information fusion tool, the Maclaurin symmetric mean (MSM) operator can provide more flexibility and robustness in information fusion, and make it more suitable for solving MADM problems with independent attributes. This current study adopts the merits of PLTS and MSM operator, and then a novel probabilistic linguistic decision making approach are targeted. Firstly, the operations of two PLTSs are redefined based upon Archimedean t-norm (ATN) and Archimedean t-conorm (ATC); Secondly, the probabilistic linguistic generalized MSM operator (PLGMSM) is proposed based on ATN and ATC, some properties of PLGMSM are investigated, then some special PLGMSM operators have been studied in detail when the parameters take different values and the generator of ATN takes different functions. Thirdly, the weighted probabilistic linguistic generalized MSM operator (WPLGMSM) is studied along with some properties of PLGMSM, some special WPLGMSM operators have been also investigated in detail when the parameters take different values and the generator of ATN takes different functions. Finally, on the basis of our proposed aggregation operators, the aggregated-based decision making approach is designed and an example is supplied to manifest the effectiveness of the proposed approach. Furthermore, some comparison analyses with extant decision approaches are carried out to illustrate the validity and feasibility of the proposed approach.