Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of the incidence of worm parasites in laying chickens in Abak Local Government Area, Akwa Ibom State, Nigeria

This research is based on parasitic helminthes of laying birds conducted in July 2018 in the Department of Animal Science, Akwa Ibom State University, Akwa Ibom State, Nigeria. This work aimed at identifying the types of worm parasites that may be found in laying birds (poultry) farms and to determine their prevalent rates in layers together with the percentage infestation of these worm parasites in Abak Local Government Area. A total of 270 sterile samples were collected randomly from 10 farms in 3 weeks at Abak Local government Area. These sterile samples were transported immediately for analysis in the laboratory using floatation method with saturated salt solution. With the aid of x10 microscope, it was discovered that out of the 270 (or 100%) samples examined for helminthes only two types of nematodes were identified. A sum total of 196 (or 73%) nematode eggs were found and a total of 113 (or 42%) Ascardia gallinarum eggs were tested positive and 83 (or 31%) Heterakis gallinarum were tested positive while a total of 74 (or 27%) fecal sample tested negative. The high prevalence of gastrointestinal helminthes as observed in this area has a strong relationship with their mode of feeding and living conditions which lead to reduced body weight, decreased egg production and deaths thus affecting the meat quality and nutrient content of the chicken. Laying birds should be dewormed every three months and high level of hygiene should be maintained in the farm. It was concluded that there is a high prevalence of helminthes in Abak Local government Area in laying birds, which may lead to high losses. Thus, improved farm management and disease control to enhance their potentials is necessary.
Clustering-based solution approach for a capacitated lot-sizing problem on parallel machines with sequence-dependent setups*

ABSTRACT This paper studies an industrial lot-sizing and scheduling problem coming from the food-industry that extends the multi-item capacitated lot-sizing and includes lost sales, overtimes, safety stock and non-uniform sequence-dependent setups on parallel machines. We introduce two different formulations and adapt the well-known Relax-and-Fix and Fix-and-Optimise heuristics in order to quickly obtain feasible solutions on large industrial instances. The complexity of our problem prevents the procedure to obtain good solutions within the time allocated by practitioners on real-life cases, hence we propose to use a clustering approach to approximate the sequence-dependent setup times. The resulting problem is significantly smaller to solve and experimental results suggest that this transformation effectively improves the solutions found on industrial instances. In particular, the combination of this clustering method and Relax-and-Fix and Fix-and-Optimise procedure turns out to be a promising approach to obtain good solutions in the given time-limit.
The Modularity of Technology and Organisations. Implications for the Theory of the Firm

This paper gives a selective overview on contributions studying issues of complexity, near-decomposability and modularity in relation to economic behaviour and the theory of the firm. In the first part the paper reviews contributions studying the relationship between human problem solving in the face of complex problems and the emergence of specific technological and organisational designs. The second part of the paper reviews recent research that has studied the impact of modular designs in the organisation of production at the firm level on industrial organisation and dynamics. The paper draws some conclusions on future avenues of research.
Radionuclide Imaging of Invasive Fungal Disease in Immunocompromised Hosts

Invasive fungal disease (IFD) leads to increased mortality, morbidity, and costs of treatment in patients with immunosuppressive conditions. The definitive diagnosis of IFD relies on the isolation of the causative fungal agents through microscopy, culture, or nucleic acid testing in tissue samples obtained from the sites of the disease. Biopsy is not always feasible or safe to be undertaken in immunocompromised hosts at risk of IFD. Noninvasive diagnostic techniques are, therefore, needed for the diagnosis and treatment response assessment of IFD. The available techniques that identify fungal-specific antigens in biological samples for diagnosing IFD have variable sensitivity and specificity. They also have limited utility in response assessment. Imaging has, therefore, been applied for the noninvasive detection of IFD. Morphologic imaging with computed tomography (CT) and magnetic resonance imaging (MRI) is the most applied technique. These techniques are neither sufficiently sensitive nor specific for the early diagnosis of IFD. Morphologic changes evaluated by CT and MRI occur later in the disease course and during recovery after successful treatment. These modalities may, therefore, not be ideal for early diagnosis and early response to therapy determination. Radionuclide imaging allows for targeting the host response to pathogenic fungi or specific structures of the pathogen itself. This makes radionuclide imaging techniques suitable for the early diagnosis and treatment response assessment of IFD. In this review, we aimed to discuss the interplay of host immunity, immunosuppression, and the occurrence of IFD. We also discuss the currently available radionuclide probes that have been evaluated in preclinical and clinical studies for their ability to detect IFD.
Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

Objectives Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study’s main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients. Methods In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data. Results Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method. Conclusions This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.
Contamination by organochlorine pesticides in the aquifer of the Ring of Cenotes in Yucatán, México

Banned or restricted organochlorine pesticides in many countries worldwide are still in use in developing countries for agricultural and livestock activities, as well as for vector control in public health campaigns. The present study was conducted to map estimated concentrations of organochlorine pesticides in a karstic region in the Yucatan Peninsula, Mexico, known as ‘Ring of Cenotes’. Water samples from 20 sinkholes (‘cenotes’) were collected during the dry and rainy seasons in 2010–11, analysed by gas chromatography and maps of pesticide concentrations were produced using geographical information system. Results show the presence of banned pesticides, all of them exceeding the limits stated by the Mexican Official Norm. The number and concentration of pesticides during the dry season were qualitatively and quantitatively higher than in the rainy season. The spatial distribution of pesticide concentrations shows that causes of pesticide pollution in the aquifer of the Ring of Cenotes are multifactorial.
Analysis of Risk Features and Performance Evaluation of Shariah-Compliance Stocks using Standard-Risk Adjusted Techniques

The Islamic Capital Market (ICM) has grown rapidly in line with the increasing demand for various Shariah-compliant investment instruments. As the fastest growing investment component, Shariah-compliant stocks in Bursa Malaysia have attracted more attention not only to Muslim investors but also to non-Muslims investors to meet their investment objectives. However, most of the literature focused on the performance comparisons of Islamic and conventional indices. This study contributes to examine the overall performance of Shariah-compliant stocks across sectors which has significant implications into the relationship of risk-return. Therefore, the main objective of this study is to analyse the risk features of 57 Shariah-compliant stocks from 12 sectors from 2013 to 2018. Next, this study also focuses on evaluating the performance of Shariah-compliant at the sector level by using the standard-risk adjusted performance methods towards KLCI. Methods employed were Sharpe, Treynor and Jensen’s Alpha ratios. The empirical results reveal that the unsystematic risk component in Shariah-compliant stocks is very high with an average diversifiability measure of 0.914. The results indicate that all Shariah-compliant stocks are not fully diversified and there are still great opportunities for diversification. While the results show that the Islamic sectors have slightly outperformed the KLCI, it also highlights that application of different risk variables used such as standard deviation and beta will lead to differences in the performance ranking. At best, investors could use this ranking to make investment decisions without undertaking time-consuming analysis to determine the highest and lowest performing sectors in the stock market.
Efficacy of selected herbs for their potential antioxidant activity in burfi, an Indian sweetmeat

© Abstract: Turmeric, ginger and cardamom were incorporated into burfi from 0.5-1.5% level and evaluated for anti-oxidative, instrumental color and sensorial attributes. Anti-oxidative activity was found to increase significantly (p<0.05) with increase in the level of herbs. Hunter colour values also changed significantly (p<0.05) with herbs level. Increasing turmeric level increased (p<0.05) redness (a* value) and yellowness (b* value, and cardamom decreased lightness (L* value). Addition of more than 0.5% turmeric, 1.0% ginger and 1.0% cardamom resulted in significant (p<0.05) decrease in sensory scores. These levels were considered optimum for incorporation into burfi . Highest anti-oxidative activity was observed with turmeric added samples followed by ginger and cardamom exhibited the least, but all the samples had significantly (p<0.05) higher activity than control and lower than BHA (200 ppm) added samples. Considering all the studied attributes, ginger (1%) was found to be the most suitable herb for incorporation into burfi the keeping quality and functional attributes. The present study was undertaken to investigate the effect of incorporation of turmeric, ginger and cardamom on various physicochemical and sensorial
A Pilot Study to Evaluate Early Predictive Value of Thorax Perfusion-CT in Advanced NSCLC

Simple Summary The use of targeted drugs has brought about the development of new imaging techniques which are able to assess in vivo processes and changes in vascularization parameters can be captured as part of the antitumor response to antiangiogenic therapies. This pilot study (IMPACT trial, NCT02316327) aimed to explore the capacity of Perfusion-Computed Tomography (pCT) to detect early changes in tumor vascularization in non-small cell lung cancer (NSCLC) patients treated with an antiangiogenic-based therapy. Our results confirm the feasibility of pCT to capture early changes in tumor vasculature and suggest the potential of blood volume (BV) to early identify differential tumor responses to antiangiogenic therapy. Abstract Background: The role of perfusion computed tomography (pCT) in detecting changes in tumor vascularization as part of a response to antiangiogenic therapy in non-small cell lung cancer (NSCLC) remains unclear. Methods: In this prospective pilot study (IMPACT trial, NCT02316327), we aimed to determine the ability of pCT to detect early changes in blood flow (BF), blood volume (BV), and permeability (PMB), and to explore whether these changes could predict the response at day +42 in patients with advanced, treatment-naive, non-squamous NSCLC treated with cisplatin and gemcitabine plus bevacizumab. Results: All of the perfusion parameters showed a consistent decrease during the course of treatment. The BV difference between baseline and early assessment was significant (p = 0.013), whereas all perfusion parameters showed significant differences between baseline and day +42 (p = 0.003, p = 0.049, and p = 0.002, respectively). Among the 16 patients evaluable for efficacy, a significant decline in BV at day +7 from baseline was observed in tumors with no response (p = 0.0418). Conclusions: Our results confirm that pCT can capture early changes in tumor vasculature. A substantial early decline of BV from baseline might identify tumors less likely responsive to antiangiogenic-drugs.
The combined impact of food antistaling agents and super-chilling on chicken breast meat by physicochemical and dynamic rheological properties

ABSTRACT For shelf-life prolongation, the effect of super-chilling (SC, −1.5 °C) combined with food antistaling agents (FAAs) on the chemical and microbiological proprieties of chicken breast were investigated. The results showed a significant difference (P < .05) in protein solubility in the chicken breast stored under SC plus FAA for 8 and 16 days compared with 4 °C storage; dynamic rheological analysis revealed that they had better gel characteristics than those under SC or 4 °C storage. Moreover, SC with FAA significantly decreased the chicken breast pH, total viable count, total volatile basic nitrogen and thiobarbituric acid reactive substance contents, and electrical conductivity (P < .01), and significantly increased the water-holding capacity (P < .01). Conclusively, SC plus FAA storage is recommended for meat and meat products because of its effectiveness at preserving the quality of fresh chicken meat, providing longer shelf lives than chicken under SC or 4 °C storage.
Handbook of Petroleum Refining Processes

Whether you're gearing up for refinery expansion, building a new plant from the ground up or evaluating methods to improve existing operations, you need this single-source guide to proven, cost-effective refining processes direct from the development companies and licensors themselves. You get instant data on: alkylation and polymerization technologies, base aeromatics production processes, catalytic cracking methods, hydrocracking procedures, separation processes, and much more - 57 technologies in all. Each technology presented gives you: General process description; Process chemistry and thermodynamics; process perspective; Detailed process description; product and by-product specifications, wastes and emissions; process economics.
An Ensemble CNN for Subject-Independent Classification of Motor Imagery-based EEG

Deep learning methods, and in particular Convolutional Neural Networks (CNNs), have shown breakthrough performance in a wide variety of classification applications, including electroencephalogram-based Brain Computer Interfaces (BCIs). Despite the advances in the field, BCIs are still far from the subject-independent decoding of brain activities, primarily due to substantial inter-subject variability. In this study, we examine the potential application of an ensemble CNN classifier to integrate the capabilities of CNN architectures and ensemble learning for decoding EEG signals collected in motor imagery experiments. The results prove the superiority of the proposed ensemble CNN in comparison with the average base CNN classifiers, with an improvement up to 9% in classification accuracy depending on the test subject. The results also show improvement with respect to the performance of a number of state-of-the-art methods that have been previously used for subject-independent classification in the same datasets used here (i.e., BCI Competition IV 2A and 2B datasets).
Environmental effects of tourism industry investments: an inter‐temporal trade‐off

Efficient investment programmes in touristic infrastructure have to take into consideration that any kind of tourism reduces the environmental quality. Since pollution shows negative repercussions as concerns the attractiveness of a touristic region, tourism planners have to determine a trade‐off between adequate services for tourists and a clean environment. To deal with this problem in a dynamic context, a three‐state optimal control model is formulated. It turns out that, even if pollution reduction is not a goal in itself, the profit‐maximizing tourism industry should care for ecological conservation. The paper further shows that persistent periodic investment policies are optimal for realistic parameter sets, and provides an economic intuition for such behaviour. From an economic point of view, this result implies that expansionary periods with high investment are followed by periods of stagnation with low investment. Copyright © 2002 John Wiley & Sons, Ltd.
A fuzzy data augmentation technique to improve regularisation

Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real‐world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C‐Means clustering and fuzzy membership grades. Fuzzy‐related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias‐variance tradeoff. Our proposed technique is evaluated using four popular data sets and is shown to provide better regularisation and higher classification accuracy compared with popular regularisation approaches.
Competitive and optimal control strategies for groundwater pumping by agricultural production units

A theoretical model of aquifer exploitation, where groundwater is devoted to agricultural production, is presented here. As new aspects with regard to other models found in literature, the model has explicit formulations for the number of production units and the natural discharge of the aquifer. It is specialized to competitive and optimal control strategies, and a comparison between steady states and trajectories under both strategies is made for ideal aquifers of very different dimensions. The commonly accepted conclusion of little differences between control and no control results to be expected in aquifers of a high “storage capacity” is confirmed for the area‐times‐storativity dimension but not for the vertical dimension of the aquifer, for which the conclusion could be quite the opposite. Sensitivity analyses are also carried out on both versions of the model where it is shown that a limitation to the entry of production units is the best instrument to improve important features regarding the competitive steady states and also to bring closer control and no control strategies.
Prevalence of subclinical mastitis in cows, isolation of agents and determination of their antibiotic susceptibility

Eosin Methylene Blue Agar. For Mycoplasma spp. isolation, milk samples were transferred to PPLO broth and PPLO agar. For phenotypic identification, cultures were evaluated according to the colony morphology, hemolysis on BA and Gram staining properties, and then were subjected to biochemical tests. Antibiotic susceptibilities of isolates were determined by Kirby Bauer Disc Diffusion Test. Results: As a result of bacteriological studies, 229 Staphylococcus spp. , 7 Corynebacterium spp. , 16 Bacillus spp. , 5 Acinetobacter spp. , 2 Escherichia coli , and 2 Mycoplasma spp. were isolated from milk samples of 120 cows. As a result of antibiogram, the majority of isolates have found sensitive to cefoxitin (30 μg) and imipenem (10 μg) and the highest resistance among the isolates was determined against to penicillin (10 μg). Conclusion: The screening of subclinical mastitis at certain periods in enterprises, the proper characterization to be determined by isolation and identification of agent are crucial for the profitability and sustainability of dairy farming
Relationships between fear of cancer recurrence, anxiety and worry, and health behaviors and health service use: a systematic review

Abstract Background: This systematic review was conducted to improve our understanding of the relationship between fear of cancer recurrence (FCR), anxiety and worry, and engagement in health behaviors and/or health service use. Methods: A systematic review following PRISMA methodology was performed to identify any publications that examined FCR, anxiety and worry, and health behaviors and/or health service use in the following databases: PubMed, CINAHL, and PsychINFO from January 1990 to April 2021. Quality was assessed using QualSyst, a standard quality assessment for evaluating primary research articles. Results: Seventy articles met the inclusion criteria, but the outcomes were too heterogeneous to combine in statistical meta-analysis. The measurement of FCR also varied widely and was inconsistent across studies. Among FCR specific measures, 36% used some version of the Fear of Cancer Recurrence Inventory. Prevention and detection of new and recurrent cancers accounted for 60% of all included studies, with the most frequently examined health behaviors being smoking status (17%) and physical activity (16%). Most associations indicated a significant relationship between FCR and current smoking, with those who are currently smoking experiencing higher levels of FCR. Relatively few studies explored the relationship between FCR and surveillance. Associations categorized under Interventions for Late Effects included a range of health services use with largely mixed results. Overall, studies had moderate to high quality. Conclusions: Given the measurement heterogeneity of FCR, future research would benefit from consistent operationalization and measurement. Some evidence suggests that FCR is associated with poor health behaviors. More studies are needed examining the relationship between FCR and surveillance. Research and clinical interventions may choose to focus on early post-treatment survivorship, given greater opportunities for support and intervention around FCR and other psychosocial concerns.