Low-quality Ones Research Articles

Gastrointestinal cancer and occupational diesel exhaust exposure: a meta-analysis of cohort studies.

Diesel exhaust exposure and cancer other than the lungs have been limitedly investigated. To conduct a systematic review and meta-analysis on the association between occupational exposure to diesel exhaust and gastrointestinal cancers. Two researchers performed a systematic literature review to identify all cohort studies on occupational exposure to diesel exhaust and risk of cancers other than lung. Of the 30 retained studies, 10 reported risk estimates for oesophageal, 18 on gastric, 15 on colon and 14 on rectal cancer. We performed random-effects meta-analyses to calculate summary relative risks (RRs) and 95% confidence intervals (CIs) for ever-exposure to diesel exhaust. We calculated summary RR = 1.08 (95% CI 0.97-1.21, P heterogeneity = 0.06) for oesophageal, 1.06 (95% CI 0.99-1.14, P < 0.001) for gastric, 0.98 (95% CI 0.96-1.00, P = 0.453) for colon, and RR = 1.04 (95% CI 0.97-1.11, P = 0.013) for rectal cancer. Drivers showed an association with oesophageal (RR = 1.26, 95% CI 0.99-1.62), gastric (RR = 1.20, 95% CI 0.91-1.59) and rectal cancer (RR = 1.41, 95% CI 1.13-1.75); machine operators with oesophageal (RR = 1.09, 95% CI 1.00-1.20) and gastric (RR = 1.15, 95% CI 1.10-1.20) and handlers with oesophageal cancer (RR = 1.95, 95% CI 1.23-3.09). Studies from Europe revealed an association with gastric cancer while those from North America did not (P < 0.05). No difference was found by quality score except for gastric cancer, where high-quality studies but not low-quality ones showed increased risk (P heterogeneity = 0.04). There was no evidence of publication bias. An increased but insignificant risk of oesophageal, gastric and rectal, but not colon cancer, was suggested in workers exposed to diesel exhaust. Residual confounding cannot be excluded.

Markers for egg quality in European eel derived from offspring of females subjected to different gonadotropic treatments

Broodstock management techniques in general, and gonadotropic treatments for vitellogenesis induction in particular, can influence the maternal provisioning of different compounds to the eggs, thus affecting embryo and larval competence. Among these, the abundance of certain transcripts as well as lipid content and fatty acid composition have been used as egg quality markers alongside other egg morphological parameters, such as egg size and the occurrence of abnormal cleavages during the first stages of embryonic development. Here, we evaluated the use of different markers of egg quality from farmed-raised European eel, Anguilla anguilla, broodstock reared on the same formulated diet but subjected to different pituitary extract administration schemes: weekly injections of salmon pituitary extract (SPE) in a constant dose or weekly injections of carp pituitary extract (CPE) at a stepwise increasing dose. Egg batches were categorized in two quality groups (high and low) according to their survival to the first feeding larval stage. In agreement with previous studies, unfertilized eggs and 4-h post fertilization embryos (16-cell stage) from SPE treated females had higher epcam expression levels than CPE ones, which is a molecule involved in cell-cell adhesion. When comparing treatments, batches from the SPE treatment had a lower incidence of cleavage abnormalities which could originate from compromised cell adhesion. On the other hand, this difference was not observed when comparing high-quality batches (i.e. those surviving up to the first-feeding stage) and low-quality ones. From the five genes analysed: epcam, dcbld1, plec, cenpf, and cenpk, only the latter had a differential expression between high- and low-quality groups both in unfertilized eggs and 4 h-post-fertilization embryos. This gene, cenpk, is involved in kinetochore assemblage during cell division and appears to be promising as egg quality marker in European eel in combination with epcam and dcbld1. Common biomarkers, such as egg size, total protein and lipid content, were similar between groups. However, although fatty acid composition was similar between quality groups, DHA and EPA content in unfertilized eggs was affected by the gonadotropic treatment applied to the female broodstock, and higher for the CPE treated females than the SPE ones. This entails that the hormonal treatments applied for vitellogenesis induction can influence the amount of specific fatty acids being transferred to the eggs, even for female broodstock that were fed the same high-quality diet.

Leveraging a self-adaptive mean teacher model for semi-supervised multi-exposure image fusion

Deep learning-based methods have recently shown remarkable advancements in multi-exposure image fusion (MEF), demonstrating significant achievements in improving the fusion quality. Despite their success, the majority of reference images in MEF are artificially generated, inevitably introducing a portion of low-quality ones. Existing methods either utilize these mixed-quality reference images for supervised learning or heavily depend on source images for unsupervised learning, making the fusion results challenging to accurately reflect real-world illumination conditions. To overcome the impact of unreliable factors in references, we propose a self-adaptive mean teacher-based semi-supervised learning framework tailored for MEF, termed SAMT-MEF. Its self-adaptiveness is reflected from two perspectives. Firstly, we establish a self-adaptive set to retain the best-ever outputs from the teacher as pseudo labels, employing a well-crafted hybrid metric for its updates. Secondly, we employ contrastive learning to assist the self-adaptive set further in alleviating overfitting to inferior pseudo labels. Our proposed method, backed by abundant empirical evidence, outperforms state-of-the-art methods quantitatively and qualitatively on both reference and non-reference datasets. Furthermore, in some scenarios, the fusion results surpass the reference images, showcasing superior performance in practical applications. Source code are publicly available at https://github.com/hqj9994ever/SAMT-MEF.

EXPRESS: Self-Donations and Charitable Contributions in Online Crowdfunding: an Empirical Analysis

Many charitable projects have started using online crowdfunding platforms to raise donations. The rise of these platforms as fundraising vehicles has been partially driven by easy access to a large pool of potential donors without the significant marketing costs that commonly accompany traditional fundraising. However, such a low cost of entry also results in a significant "crowding" of projects, making it difficult for donors to decide which projects to donate to. Thus, a charitable project encounters a fundamental marketing challenge of standing out from other projects when conventional techniques like advertising and promotion are limited. In this paper, we posit that a project can credibly signal its quality via a strategy of "self-donation," whereby the project steward donates to her own project. Our empirical setting is an online education crowdfunding platform. By examining millions of donations, we find that self-donations improve the donation pace, contributed amount, and funding success. We show that the self-donation strategy works only when a self-donation is visible to potential donors and is especially effective at the early stage of the funding cycle or when project stewards are inexperienced, where the projects face significant uncertainty. We find evidence for self-donation as a quality signal through various observable proxies like impact letters to donors and corporate matching. Overall, our findings are consistent with a signaling mechanism that allows the separation of high-quality projects from lower-quality ones.

Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation

Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing action recognition using low-quality skeletons through a general knowledge distillation framework. The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons. To bridge the gap between heterogeneous high-quality and low-quality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge. A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches. Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework.

Youtube as a source of information vagal nerve stimulation: A quality analysis

PurposeTo evaluate the quality of available information on Youtube videos about VNS as a “source of health information” for patients with drug resistant epilepsy. MethodsYoutube videos were searched and screened using the terms “VNS for seizures,” and “VNS surgery,” “VNS epilepsy” in May 2023. The quality of the videos was evaluated the Quality Criteria for Consumer Health Information (DISCERN) and Global Quality Scale (GQS). The videos were analyzed in terms of content, accuracy, reliability, and quality. ResultsA total of 104 videos were searched. After excluding duplicate or inappropriate videos, 51 videos were analyzed. Youtube videos about vagal nerve stimulation are mostly on general information, surgical procedures, patient experiences and magnet use. When video sources are examined according to the quality, according to GQS, 75 % of high quality videos were narrated by physicians, all intermediate quality videos were narrated by physicians, and low quality ones narrated by physicians were 47.4 % and 28.9 % by the patients. All of the videos narrated by the patients were of low quality. There was a significant and strong correlation between GQS and Discern score (r = 0.807, p < 0.001). Only GQS scores of videos with different content were found to be statistically significant (p < 0.05). Two pediatric neurologists independently reviewed the videos and classified the videos as useful or misleading according to the GQS, DISCERN scala. The overall κ value for interobserver consistency according to Global Quality Scale was 0.781 (p < 0.001). (95 % confidence interval), indicating a very good level of agreement. ConclusionYoutube videos may provide a worthful source for patients and parents seeking to find more information about VNS However incorrect information could easily be disseminated by high number of views of videos with low quality. High skilled and experienced professionals should create videos on Youtube to ensure that patients and parents can access more useful, high-quality, and accurate information on VNS.

EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment

The scheduling of tasks in the cloud is a major challenge for improving resource availability and decreasing the total execution time and energy consumption of operations. Due to its simplicity, efficiency, and effectiveness in identifying global optimums, electric fish optimisation (EFO) has recently garnered a lot of interest as a metaheuristic method for solving optimisation issues. In this study, we apply electric fish optimisation (EAEFA) to the problem of cloud task scheduling in an effort to cut down on power usage and turnaround time. The objective is to finish all tasks in the shortest possible time, or makespan, taking into account constraints like resource availability and task dependencies. In the EAEFA approach, a school of electric fish is used to solve a multi-objective optimization problem that represents the scheduling of tasks. Because electric fish are drawn to high-quality solutions and repelled by low-quality ones, the algorithm is able to converge to a global optimum. Experiments validate EAEFA's ability to solve the task scheduling issue in cloud computing. The suggested scheduling strategy was tested on HPC2N and other large-scale simulations of real-world workloads to measure its makespan time, energy efficiency and other performance metrics. Experimental results demonstrate that the proposed EAEFA method improves performance by more than 30% with respect to makespan time and more than 20% with respect to overall energy consumption compared to state-of-the-art methods.

Mobile App Crowdsourced Test Report Consistency Detection via Deep Image-and-Text Fusion Understanding

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.

Scales and size-quality outcomes in adult learning disability residential care: evidence from the UK.

Residential care services are under increasing pressure to lower service provision costs while maintaining quality of care. Using a translog cost function, this paper examines the relationship between cost, quality and output in England's learning disability (LD) residential care sector. It finds genuine but diminishing economies of scale in LD residential care services vis-à-vis output (i.e., care weeks). However, some variation exists: higher-quality LD residential care homes appear to have larger economies of scale than lower-quality ones. Supplementary regression analysis, examining quality-size, further finds quality (a) is negatively associated with LD care homes of six or fewer beds; (b) shows no association with homes of more than six beds. These findings enhance residential care literature and raise the possibility that, by promoting the establishment of larger high-quality care homes, cost savings may be achieved without sacrificing quality.

Incorporating logic rules with textual representations for interpretable knowledge graph reasoning

Reasoning on knowledge graphs (KGs) is significant for downstream applications, such as question answering and information extraction. On the basis of using factual triples in KGs, learning logic rules has been explored to handle KG reasoning in an interpretable pattern. Some rule learning methods simply apply thresholds to reduce the exponential complexity caused by the vast candidate rules derived from KGs. Others are designed to prune candidates on the basis of embedding facts in the KG. However, these methods only consider the factual triples, which mainly contain structural information, and ignore sufficient semantic information in real-world KGs. This situation weakens the methods in searching for high-quality rules. To this end, we propose a joint method by incorporating logic rules with textual representations for interpretable KG reasoning, named LoTus. Firstly, LoTus obtains the structural embeddings with only factual triples, and the textual embeddings with descriptions of entities, which are later integrated into the representations of entities and relations. Secondly, top candidate rules are selected by a proposed joint pruning strategy incorporating structural and textual representations in KGs. Finally, LoTus captures high-quality rules and removes low-quality ones by a filtering process with metrics of rules. It reduces the time complexity on KG reasoning by learning rules and provides interpretability simultaneously. Extensive experiments on different datasets show that LoTus obtains competitive performance comparing to baselines on KG reasoning metrics Hits@1, Hits@10 and mean reciprocal rank (MRR), and achieves significant superiority among the recent rule learning methods.

Deceased Donor Kidney Transplantation for Older Transplant Candidates: A New Microsimulation Model for Determining Risks and Benefits.

Under the current US kidney allocation system, older candidates receive a disproportionately small share of deceased donor kidneys despite a reserve of potentially usable kidneys that could shorten their wait times. To consider potential health gains from increasing access to kidneys for these candidates, we developed and calibrated a microsimulation model of the transplantation process and long-term outcomes for older deceased donor kidney transplant candidates. We estimated risk equations for transplant outcomes using the Scientific Registry of Transplant Recipients (SRTR), which contains data on all US transplants (2010-2019). A microsimulation model combined these equations to account for competing events. We calibrated the model to key transplant outcomes and used acceptance sampling, retaining the best-fitting 100 parameter sets. We then examined life expectancy gains from allocating kidneys even of lower quality across patient subgroups defined by age and designated race/ethnicity. The best-fitting 100 parameter sets (among 4,000,000 sampled) enabled our model to closely match key transplant outcomes. The model demonstrated clear survival benefits for those who receive a deceased donor kidney, even a lower quality one, compared with remaining on the waitlist where there is a risk of removal. The expected gain in survival from receiving a lower quality donor kidney was consistent gains across age and race/ethnic subgroups. Limited available data on socioeconomic factors. Our microsimulation model accurately replicates a range of key kidney transplant outcomes among older candidates and demonstrates that older candidates may derive substantial benefits from transplantation with lower quality kidneys. This model can be used to evaluate policies that have been proposed to address concerns that the current system disincentivizes deceased donor transplants for older patients. The microsimulation model was consistent with the data after calibration and accurately simulated the transplantation process for older deceased donor kidney transplant candidates.There are clear survival benefits for older transplant candidates who receive deceased donor kidneys, even lower quality ones, compared with remaining on the waitlist.This model can be used to evaluate policies aimed at increasing transplantation among older candidates.

Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning

Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-aligned data is expensive to obtain. However, this distant supervision method of training induces an over-generation problem since the automatically aligned text includes hallucination. These expressions cannot be inferred from the data, misguiding the model to produce unfaithful text. To exploit the noisy dataset while maintaining faithfulness, we empower the neural data-to-text model by dynamically increasing the weights of those well-aligned training instances and reducing the weights of the low-quality ones via meta learning. To our best knowledge, we are the first to alleviate the noise in distantly-supervised data-to-text generation via meta learning. In addition, we rewrite those low-quality texts to provide better training instances. Finally, we construct a new distantly-supervised dataset, DIST-ToTTo (abbreviation for Distantly-supervised Table-To-Text), and conduct experiments on both the benchmark WITA (abbreviation for the data source Wikipedia and Wikidata) and DIST-ToTTo datasets. The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). Furthermore, based on human evaluation, our model can generate more grammatically correct and more faithful text compared to the state-of-the-art DSG model.

Exchange Rate Shocks and Quality Adjustments

Abstract Do firms respond to cost shocks by reducing the quality of their products? Using microdata from a large Russian retailer that refreshes its product line twice-yearly, we document that higher quality products are more profitable than lower quality ones, but that the number of high-quality products experiences a relative decrease after a large ruble devaluation in 2014. We show that rising firm costs—and not shrinking consumer incomes—explains the reallocation, and rationalize the data with a model that features consumer expenditure switching between high and low qualities. The reallocation to lower quality products reduces average pass-through by 26%.

Investment, consumption smoothing with credit guarantee and adverse selection

This paper develops a single-period model of investments with credit guarantee swaps including Fee-for-Guarantee Swaps (FGSs), Equity-for-Guarantee Swaps (EGSs) and Option-for-Guarantee Swaps (OGSs). Enterprises have two types and high-quality enterprises suffer from adverse selection. The entrepreneurs are risk-averse while insurers are risk-neutral, taking on all default losses. We show that in pooling equilibria, high-quality enterprises are forced to transfer partial profits to low-quality ones. Even in separating equilibria, it is possible that high-quality enterprises suffer losses from adverse selection. As far as high-quality enterprises are concerned, OGS financing modes are the best, and they are more likely to reach separating equilibria than others. Only if the guarantee cost is feasible, the higher the strike price of the option, the higher the project value, and the optimal strike price makes guarantee cost arrive at its maximum feasible value.

Application of Molecular spectroscopy and chemometrics for authentication and quality control of tea (Camellia sinensis L.): a review

Tea, derived from Camellia sinensis L., is considered as the most popular beverages in the world. The quality of teas may vary depending on harvesting location and geographical origins, thus the traceability of teas according to their origins is very essential to assure tea’s quality. Due to economic reasons, high quality tea products may be added with foreign materials or adulterated with low quality ones, as a consequence, some analytical methods have been proposed and developed to quality control of tea. During the recent years, the application of molecular spectroscopic techniques (UV-Vis, Fluorescence, Near Infrared, Mid Infrared, Raman) in combination with multivariate data analysis has emerged as rapid and reliable analytical tool in the quality control of food, including food authentication. The objective of this review is to update the application of molecular spectroscopy (UV-Vis, fluorescence, infrared and Raman) for the quality control and authentication of tea products either geographical origins issue or detection of potential adulterants. The variables obtained during molecular spectral measurement involve hundreds or thousands of data, which make data analysis rather complex. Fortunately, the specific chemometrics tools can solve the problems arising from big data coming from analyte signals, spectral interferences and overlapping peaks. This review paper provides an overview of the recently developed approaches and latest research carried out in molecular spectroscopic techniques in combination with chemometrics for the quality control and for authentication of teas

Augmented Equivariant Attention Networks for Microscopy Image Transformation.

It is time-consuming and expensive to take high-quality or high-resolution electron microscopy (EM) and fluorescence microscopy (FM) images. Taking these images could be even invasive to samples and may damage certain subtleties in the samples after long or intense exposures, often necessary for achieving high-quality or high-resolution in the first place. Advances in deep learning enable us to perform various types of microscopy image-to-image transformation tasks such as image denoising, super-resolution, and segmentation that computationally produce high-quality images from the physically acquired low-quality ones. When training image-to-image transformation models on pairs of experimentally acquired microscopy images, prior models suffer from performance loss due to their inability to capture inter-image dependencies and common features shared among images. Existing methods that take advantage of shared features in image classification tasks cannot be properly applied to image transformation tasks because they fail to preserve the equivariance property under spatial permutations, something essential in image-to-image transformation. To address these limitations, we propose the augmented equivariant attention networks (AEANets) with better capability to capture inter-image dependencies, while preserving the equivariance property. The proposed AEANets captures inter-image dependencies and shared features via two augmentations on the attention mechanism, which are the shared references and the batch-aware attention during training. We theoretically derive the equivariance property of the proposed augmented attention model and experimentally demonstrate its consistent superiority in both quantitative and visual results over the baseline methods.

Quality evaluation of cell spheroids for transplantation by monitoring oxygen consumption using an on-chip electrochemical device

Three-dimensional cell spheroids are superior cell-administration form for cell-based therapy which generally exhibit superior functionality and long-term survival after transplantation. Here, we nondestructively measured the oxygen consumption rate of cell spheroids using an on-chip electrochemical device (OECD) and examined whether this rate can be used as a marker to estimate the quality of cell spheroids. Cell spheroids containing NanoLuc luciferase-expressing mouse mesenchymal stem cell line C3H10T1/2 (C3H10T1/2/Nluc) were prepared. Spheroids of high or low quality were prepared by altering the medium change frequency. After transplantation into mice, the high-quality C3H10T1/2/Nluc spheroids exhibited a higher survival rate than the low-quality ones. The oxygen consumption rate of the high-quality C3H10T1/2/Nluc spheroids was maintained at high levels, whereas that of the low-quality spheroids decreased with time. These results indicate that OECD-based measurement of the oxygen consumption rate can be used to estimate the quality of cell spheroids without destructive analysis of the spheroids.

Fishing behavior in the red fox: Opportunistic-caching behavior or surplus killing?

Fishing behavior in the red fox: Opportunistic-caching behavior or surplus killing?

Biodiesel fuel. Part I. Methods of obtaining

THE PURPOSE. The presented work aims to analyze the realities and prospects for the use of working media in the production of biodiesel fuel, including the supercritical fluid state. METHODS. Methods for obtaining biodiesel fuel are considered, including the method of transesterification, as the most common, as well as methods of pyrolysis and the combined process of hydrolysis and esterification. RESULTS. Traditional (industrially used methods for producing biodiesel fuel), as well as methods involving supercritical fluid media at their core, are considered. Along with a description of the state of affairs on the issues under discussion in the world, the results of our own research carried out by the team of authors of this article are also presented. Attention is drawn to the prospects of ultrasonic emulsification of the reaction mixture and the use of heterogeneous catalysts in order to mitigate supercritical fluid conditions for the process of obtaining biodiesel fuel and save energy. The conditions for obtaining biodiesel fuel without free glycerol and converting it into a fuel component are also discussed. CONCLUSION. Transesterification carried out under supercritical fluid conditions provides significant advantages over the traditional process and, especially in terms of the possibility of using a variety of raw materials, including low-quality ones, facilitates the procedure for isolating the final product and, finally, makes it possible to switch from relatively small-scale implementations with batch reactors to high-performance plants with flow reactors.

Increasing Halal Awareness through Spiritual Capital Awareness Case Study on Traditional Market Traders in Surabaya

Today, news regarding unethical behavior in traditional markets is frequently reported in a variety of mass media. Some of them involve lowering the weights of trading goods, trading or combining high-quality commodities with low-quality ones, and the mystical belief of black magic. Because business players have poor spiritual capital, they are more susceptible to moral hazard, which is harmful to the transaction process and gives rise to this social problem. In order to understand the urgency of social capital in business actors in traditional marketplaces, it is fascinating to analyze this situation. Snowball sampling is used in this study's qualitative phenomenological methodology. The results of research conducted on Muslim traders in traditional markets show that spiritual capital is well understood, even though in practice it is often not realized. An understanding of good spiritual capital has a huge influence on their mindset so it also has an impact on their patterns of economic behavior in conducting trading businesses in accordance with Islamic laws and principles (halal trading). Keywords: Spiritual Capital, Mindset, Behavior