Search results for: generalized autoregressive score model

Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Azeez Balogun

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Inexperienced building score practices are continually evolving and vary across areas. Yet, a few middle ideas stay steady, such as website selection, design, energy efficiency, water and material conservation, indoor environmental great, operational optimization, and waste discount. The essence of green building lies inside the optimization of 1 or more of those standards. This paper conducts a comparative analysis of 7 extensively recognized sustainable score structures—BREEAM, CASBEE, green GLOBES, inexperienced superstar, HK-BEAM, IGBC green homes, and LEED—based totally on the perceptions and opinions of stakeholders in Nigeria certified in green constructing rating systems. The purpose is to pick out and adopt an appropriate green building rating device for Nigeria. Numerous components of those systems had been tested to determine the high-quality health of the Nigerian built environment. The findings imply that LEED, the important machine within the USA and Canada, is the most suitable for Nigeria due to its sturdy basis, extensive funding, and confirmed blessings. LEED obtained the highest rating of eighty out of one hundred points on this assessment.

Keywords: structure, built surroundings, inexperienced building score gadget, Nigeria Inexperienced Constructing Council, sustainability

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: Koko Iwan Agus Kurniawan, Dwi Purnomo, Anas Bunyamin, Arif Rahman Jaya

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Fruters model is a concept of technopreneurship-based on empowerment, in which technology research results were designed to create high value-added products and implemented as a locomotive of collaborative empowerment; thereby, the impact was widely spread. This model still needs to be inventoried and validated concerning the influenced variables in the business growth process. Model validation accompanied by mapping was required to be applicable to Small Medium Enterprises (SMEs) agro-industry based on sustainable social business and existing real cases. This research explained the empowerment model of Yourgood, an SME, which emphasized on empowering the farmers/ breeders in farmers in rural areas, Cipageran, Cimahi, to housewives in urban areas, Bandung, West Java, Indonesia. This research reviewed some works of literature discussing the agro-industrial development associated with the empowerment and social business process and gained a unique business model picture with the social business platform as well. Through the mapped business model, there were several advantages such as technology acquisition, independence, capital generation, good investment growth, strengthening of collaboration, and improvement of social impacts that can be replicated on other businesses. This research used analytical-descriptive research method consisting of qualitative analysis with design thinking approach and that of quantitative with the AHP (Analytical Hierarchy Process). Based on the results, the development of the enterprise’s process was highly affected by supplying farmers with the score of 0.248 out of 1, being the most valuable for the existence of the enterprise. It was followed by university (0.178), supplying farmers (0.153), business actors (0.128), government (0.100), distributor (0.092), techno-preneurship laboratory (0.069), banking (0.033), and Non-Government Organization (NGO) (0.031).

Keywords: agro-industry, small medium enterprises, empowerment, design thinking, AHP, business model canvas, social business

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Authors: Tahsien Mohamed Okasha

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We hypothesized that Post cardiac arrest patients with Glasgow coma scale (GCS) score of less than (8) and who will be exposed to therapeutic hypothermia protocol will exhibit improvement in their neurological performance. Purposive sample of 17 patients who were fulfilling the inclusion criteria during one year collected. The study carried out using Quasi-experimental research design. Four Tools used for data collection of this study: Demographic and medical data sheet, Post cardiac arrest health assessment sheet, Bedside Shivering Assessment Scale (BSAS), and Glasgow Pittsburgh cerebral performance category scale (CPC). Result: the mean age was X̅ ± SD = 53 ± 8.122 years, 47.1% were arrested because of cardiac etiology. 35.3% with initial arrest rhythm ventricular tachycardia (VT), 23.5% with ventricular fibrillation (VF), and 29.4% with A-Systole. Favorable neurological outcome was seen among 70.6%. There was significant statistical difference in WBC, Platelets, blood gases value, random blood sugar. Also Initial arrest rhythm, etiology of cardiac arrest, and shivering status were significantly correlated with cerebral performance categories score. therapeutic hypothermia has positive effects on neurological performance among post cardiac arrest patients with GCS score of less than (8). replication of the study on larger probability sample, with randomized control trial design. Further study for suggesting nursing protocol for patients undergoing therapeutic hypothermia.

Keywords: therapeutic hypothermia, neurological performance, after resuscitation from cardiac arrest., resuscitation

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Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

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Authors: Novi Prasanty, Mustafa Ma, Elmeida Effendy

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Background: Schizophrenia is a psychotic disorder that most often encountered. Nearly 1% of the world population suffers from schizophrenia during their lifetime. Schizophrenia is a severe form of psychotic disorders, and tend to be chronic. Vitamin D plays crucial roles in neuroprotection and neurodevelopment, and low levels are commonly associated with schizophrenia. Lower vitamin D levels were correlated with more severe positive, negative, and overall symptoms in schizophrenia patient men and women. Methods: 54 schizophrenic patients, male and female, who are diagnosed with semistructured MINI ICD-X. A symptom of schizophrenia was measured by using positive and negative Syndrome Scale (PANSS). Examination of serum vitamin D using ELFA. Analysis to compare the serum levels of vitamin D male and female with Independent T-test, and the relationship between serum level of vitamin D and symptom with correlation. Results: In this study serum levels in male schizophrenic patients 22.12 (4.16), and 16.54 (2.88) in female schizophrenic patients. There are differences in male schizophrenic patients and women (p < 0.001). The negative correlation between serum levels of vitamin D in the PANSS total score in patients with schizophrenic male with r -0.58, p (0,016), and the female schizophrenic patients with r -0.69, p (0.031). Conclusion and Suggestion: There is a negative correlation between serum levels of vitamin D with a total score of PANSS, the lower the serum levels of vitamin D, the higher the total score of the PANSS.

Keywords: PANSS, schizophrenia, serum levels of vitamin D, severity illness

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Ms Azam Najafkouchak, David Todem, Dorothy Pathak, Pramod Pathak, Joseph Gardiner

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Propensity score (PS) methods have recently become the standard analysis as a tool for the causal inference in the observational studies where exposure is not randomly assigned, thus, confounding can impact the estimation of treatment effect on the outcome. For the binary outcome, the effect of treatment on the outcome can be estimated by odds ratios, relative risks, and risk differences. However, using the different PS methods may give you a different estimation of the treatment effect on the outcome. Several methods of PS analyses have been used mainly, include matching, inverse probability of weighting, stratification, and covariate adjusted on PS. Due to the dangers of discretizing continuous variables (exposure, covariates), the focus of this paper will be on how the variation in cut-points or boundaries will affect the average treatment effect (ATE) utilizing the stratification of PS method. Therefore, we are trying to avoid choosing arbitrary cut-points, instead, we continuously discretize the PS and accumulate information across all cut-points for inferences. We will use Monte Carlo simulation to evaluate ATE, focusing on two PS methods, stratification and covariate adjusted on PS. We will then show how this can be observed based on the analyses of the data from a case-control study of breast cancer, the Polish Women’s Health Study.

Keywords: average treatment effect, propensity score, stratification, covariate adjusted, monte Calro estimation, breast cancer, case_control study

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Authors: Mohammad Tahir Yousafzai, Amna Rehana Siddiqui, Naveed Zafar Janjua

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We assessed the frequency and predictors of sharp injuries (SIs) among health care workers (HCWs) at first level care facilities (FLCF) in rural Pakistan. HCWs working at public clinic (PC), privately owned licensed practitioners’ clinic (LPC) and non-licensed practitioners’ clinic (NLC) were interviewed on universal precautions (UPs) and constructs of health belief model (HBM) to assess their association with SIs through negative-binomial regression. From 365 clinics, 485 HCWs were interviewed. Overall annual rate of Sis was 192/100 HCWs/year; 78/100 HCWs among licensed prescribers, 191/100 HCWs among non-licensed prescribers, 248/100 HCWs among qualified assistants, and 321/100 HCWs among non-qualified assistants. Increasing knowledge score about bloodborne pathogens (BBPs) transmission (rate-ratio (RR): 0.93; 95%CI: 0.89–0.96), fewer years of work experience, being a non-licensed prescriber (RR: 2.02; 95%CI: 1.36–2.98) licensed (RR: 2.86; 9%CI: 1.81–4.51) or non-licensed assistant (RR: 2.78; 95%CI: 1.72–4.47) compared to a licensed prescriber, perceived barriers (RR: 1.06;95%CI: 1.03–1.08), and compliance with UPs scores (RR: 0.93; 95%CI: 0.87–0.97) were significant predictors of SIs. Improved knowledge about BBPs, compliance with UPs and reduced barriers to follow UPs could reduce SIs to HCWs.

Keywords: health belief model, sharp injuries, needle stick injuries, healthcare workers

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Authors: Debarag Banerjee, Pooja Singh, Arjun Avadhanam, Saksham Srivastava

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Autonomous conversational agents, i.e., chatbots, are becoming an increasingly common mechanism for enterprises to provide support to customers and partners. In order to rate chatbots, especially ones powered by Generative AI tools like Large Language Models (LLMs), we need to be able to accurately assess their performance. This is where chatbot benchmarking becomes important. In this paper, authors propose the use of a benchmark that they call the E2E (End to End) benchmark and show how the E2E benchmark can be used to evaluate the accuracy and usefulness of the answers provided by chatbots, especially ones powered by LLMs. The authors evaluate an example chatbot at different levels of sophistication based on both our E2E benchmark as well as other available metrics commonly used in the state of the art and observe that the proposed benchmark shows better results compared to others. In addition, while some metrics proved to be unpredictable, the metric associated with the E2E benchmark, which uses cosine similarity, performed well in evaluating chatbots. The performance of our best models shows that there are several benefits of using the cosine similarity score as a metric in the E2E benchmark.

Keywords: chatbot benchmarking, end-to-end (E2E) benchmarking, large language model, user centric evaluation.

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Authors: Long-Shan Wu, Ming-Chen Ko, Chien-Chang Ho, Po-Fu Lee, Jenn-Woei Hsieh, Ching-Yu Tseng

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This study aimed to determine the effects of whole-body vibration (WBV) training on lower-extremity muscle strength and balance control performance among community-dwelling middle-aged and older adults in the United States. Twenty-nine participants without any contraindication of performing WBV exercise completed all the study procedures. Participants were randomly assigned to do body weight exercise with either an individualized vibration frequency and amplitude, a fixed vibration frequency and amplitude, or no vibration. Isokinetic knee extensor power, limits of stability, and sit-to-stand tests were performed at the baseline and after 8 weeks of training. Neither the individualized frequency-amplitude WBV training protocol nor the fixed frequency-amplitude WBV training protocol improved isokinetic knee extensor power. The limits of stability endpoint excursion score for the individualized frequency-amplitude group increased by 8.8 (12.9%; p = 0.025) after training. No significant differences were observed in fixed and control group. The maximum excursion score for the individualized frequency-amplitude group at baseline increased by 9.2 (11.5%; p = 0.006) after training. The average weight transfer time score significantly decreased by 0.21 s in the fixed group. The participants in the individualized group showed a significant increase (3.2%) in weight rising index score after 8 weeks of WBV training. These results suggest that 8 weeks of WBV training improved limit of stability and sit-to-stand performance. Future studies need to determine whether WBV training improves other factors that can influence posture control.

Keywords: whole-body vibration training, muscle strength, balance control, middle-aged and older adults

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Authors: Donna-Marie Phyllis Lanton

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Sarcopenia is a well-known geriatric syndrome characterized by the progressive and generalized loss of muscle quantity or quality. The incidence of sarcopenia increases with age and is associated with adverse outcomes such as the increased risk of falls, cognitive impairment, loss of independence, diminished quality of life, increased health costs, need for care in a skilled nursing facility, and increased mortality. Physical activity, including resistance training, is the most prevalent recommendation for treating and preventing sarcopenia. Residents (N = 23) of a skilled nursing facility in East Orlando, Florida, participated in a 90-day strength training program designed using the PARIHS framework to improve measures of muscle mass, muscle strength, physical performance, and quality of life. Residents engaged in both resistance and balance exercises for 1 hour two times a week. Baseline data were collected and compared to data at Days 30, 60, and 90. T tests indicated significant gains on all measures from baseline to 90 days: muscle mass increased by 1.2 (t[22] = 2.85, p = .009), grip strength increased by 4.02 (t[22] = 8.15, p < .001), balance increased by 2.13 (t[22] = 18.64, p < .001), gait speed increased by 1.83 (t[22] = 17.84, p < .001), chair speed increased 1.87 (t[22] = 16.36, p < .001), and quality of life score increased by 17.5 (t[22] = 19.26, p < .001). For residents with sarcopenia in skilled nursing facilities, a 90-day strength training program with resistance and balance exercises may provide an option for decreasing the incidence of sarcopenia among that population

Keywords: muscle mass, muscle strength, older adults, PARIHS framework

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Saw Thiha, Jay Rajasekera

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A boom in video conferencing generated millions of hours of video data daily to be analyzed. However, such enormous data pose certain scalability issues to be analyzed efficiently, let alone do it in real-time, as online conferences can involve hundreds of people and can last for hours. This paper proposes an efficient online proctoring solution that can analyze the online conferences real-time on edge devices such as Android, iOS, and desktops. Since the computation can be done upfront on the devices where online conferences take place, it can scale well without requiring intensive resources such as GPU servers and complex cloud infrastructure. According to the linear models, face orientation does indeed impact the perceived eye openness. Also, the proposed z score facial landmark standardization was proven to be functional in detecting face orientation and contributed to classifying eye blinks with single eyelid distance computation while achieving a better f1 score and accuracy than the Eye Aspect Ratio (EAR) threshold method. Last but not least, the authors implemented the solution natively in the MediaPipe framework and open-sourced it along with the reproducible experimental results on GitHub. The solution provides face orientation, eye blink, facial activity, and translation detections out of the box and is highly customizable and extensible.

Keywords: android, desktop, edge computing, blink, face orientation, facial activity and translation, MediaPipe, open source, real-time, video conference, web, iOS, Z score facial landmark standardization

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Authors: Allyson Augusta Shrikhande, Alexa Rains, Tayyaba Ahmed, Marjorie Mamsaang, Rakhi Vyas, Janaki Natarajan, Erika Moody, Christian Reutter, Kimberlee Leishear, Yogita Tailor, Sandra Sandhu-Restaino, Lora Liu, Neha James, Rosemarie Filart

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Characterized by consistent difficulty getting and keeping an erection firm enough for intercourse, Erectile Dysfunction may affect up to 15% of adult men. Although awareness and access to treatment have improved in recent years, many patients do not actively seek diagnosis or treatment due to the stigma surrounding this condition. Patients who do seek treatment are often dissatisfied by the efficacy of the medication. The condition inhibits patients’ quality of life by worsening mental health and relationships. The purpose of this study was to test the effectiveness of an outpatient neuromuscular treatment protocol in treating the symptoms of Chronic Pelvic Pain and Erectile Dysfunction, improving pain and function. 56 patients ages 20-79 presented to an outpatient clinic for treatment of pelvic pain and Erectile Dysfunction symptoms. These symptoms had persisted for an average of 4 years. All patients underwent external ultrasound-guided hydro-dissection technique targeted at pelvic peripheral nerves in combination with pelvic floor musculature trigger-point injections. To measure the effects of this treatment, a five question Erectile Dysfunction questionnaire was completed by each patient at their first visit to a clinic and three months after treatment began. Answers were summed for a total score of 5-25, with a higher score indicating optimal function. The average score before treatment was 14.125 (SD 5.411) (a=0.05; CI 12.708-15.542), which increased by 18% to an average of 16.625 (SD 6.423) (a=0.05; CI 14.943-18.307) after treatment (P=0.0004). Secondary outcome variables included a Visual Analogue Scale (VAS) to measure pelvic pain intensity and the Functional Pelvic Pain Scale (FPPS) to measure function across multiple areas. VAS scores reduced by 51% after three months. Before treatment, the mean VAS score was 5.87, and the posttreatment mean VAS score was 2.89. Pelvic pain functionality improved by 34% after three months. Pretreatment FPPS scores averaged at 7.48, decreasing to 4.91 after treatment. These results indicate that this unique treatment was very effective at relieving pain and increasing function for patients with Erectile Dysfunction.

Keywords: chronic pelvic pain, erectile dysfunction, nonsurgical, outpatient, trigger point injections

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Authors: Maico R. Severino, Laura G. Caixeta, Nadine M. Costa, Raísa L. T. Napoleão, Éverton F. V. Valle, Diego D. Calixto, Danielle Oliveira

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One of the biggest barriers that companies find nowadays is the coordination of production flow in their Supply Chains (SC). In this study, coordination is understood as a mechanism for incorporating the entire production channel, with everyone involved focused on achieving the same goals. Sometimes, this coordination is attempted by the use of logistics practices or production plan and control methods. No papers were found in the literature that presented the combined use of logistics practices and production plan and control methods. The main objective of this paper is to propose solutions for six case studies combining logistics practices and Ordering Systems (OS). The methodology used in this study was a conceptual model of decision making. This model contains six phases: a) the analysis the types and characteristics of relationships in the SC; b) the choice of the OS; c) the choice of the logistics practices; d) the development of alternative proposals of combined use; e) the analysis of the consistency of the chosen alternative; f) the qualitative and quantitative assessment of the impact on the coordination of the production flow and the verification of applicability of the proposal in the real case. This study was conducted on six Brazilian SC of different sectors: footwear, food and beverages, garment, sugarcane, mineral and metal mechanical. The results from this study showed that there was improvement in the coordination of the production flow through the following proposals: a) for the footwear industry the use of Period Bath Control (PBC), Quick Response (QR) and Enterprise Resource Planning (ERP); b) for the food and beverage sector firstly the use of Electronic Data Interchange (EDI), ERP, Continuous Replenishment (CR) and Drum-Buffer-Rope Order (DBR) (for situations in which the plants of both companies are distant), and secondly EDI, ERP, Milk-Run and Review System Continues (for situations in which the plants of both companies are close); c) for the garment industry the use of Collaborative Planning, Forecasting, and Replenishment (CPFR) and Constant Work-In-Process (CONWIP) System; d) for the sugarcane sector the use of EDI, ERP and CONWIP System; e) for the mineral processes industry the use of Vendor Managed Inventory (VMI), EDI and MaxMin Control System; f) for the metal mechanical sector the use of CONWIP System and Continuous Replenishment (CR). It should be emphasized that the proposals are exclusively recommended for the relationship between client and supplier studied. Therefore, it cannot be generalized to other cases. However, what can be generalized is the methodology used to choose the best practices for each case. Based on the study, it can be concluded that the combined use of OS and logistics practices enable a better coordination of flow production on SC.

Keywords: supply chain management, production flow coordination, logistics practices, ordering systems

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Authors: D. Mokatsanyane, J. Jansen Van Rensburg

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This paper analyses the influence and impact of COVID-19 on major agricultural commodity prices in South Africa. According to a World Bank report, the agricultural sector in South Africa has been unable to reduce the domestic food crisis that has been occurring over the past years, hence the increased rate of poverty, which is currently at 55.5 percent as of April 2020. Despite the significance of this sector, empirical findings concluded that the agricultural sector now accounts for 1.88 percent of South Africa's gross domestic product (GDP). Suggesting that the agricultural sector's contribution to the economy has diminished. Despite the low contribution to GDP, this primary sector continues to play an essential role in the economy. Over the past years, multiple factors have contributed to the soaring commodities prices, namely, climate shocks, biofuel demand, demand and supply shocks, the exchange rate, speculation in commodity derivative markets, trade restrictions, and economic growth. The COVID-19 outbursts have currently disturbed the supply and demand of staple crops. To address the disruption, the government has exempted the agricultural sector from closure and restrictions on movement. The spread of COVID-19 has caused turmoil all around the world, but mostly in developing countries. According to Statistic South Africa, South Africa's economy decreased by seven percent in 2020. Consequently, this has arguably made the agricultural sector the most affected sector since slumped economic growth negatively impacts food security, trade, farm livelihood, and greenhouse gas emissions. South Africa is sensitive to the fruitfulness of global food chains. Restrictions in trade, reinforced sanitary control systems, and border controls have influenced food availability and prices internationally. The main objective of this study is to evaluate the behavior of agricultural commodity prices pre-and during-COVID to determine the impact of volatility drivers on these crops. Historical secondary data of spot prices for the top five major commodities, namely white maize, yellow maize, wheat, soybeans, and sunflower seeds, are analysed from 01 January 2017 to 1 September 2021. The timeframe was chosen to capture price fluctuations between pre-COVID-19 (01 January 2017 to 23 March 2020) and during-COVID-19 (24 March 2020 to 01 September 2021). The Generalised Autoregressive Conditional Heteroscedasticity (GARCH) statistical model will be used to measure the influence of price fluctuations. The results reveal that the commodity market has been experiencing volatility at different points. Extremely high volatility is represented during the first quarter of 2020. During this period, there was high uncertainty, and grain prices were very volatile. Despite the influence of COVID-19 on agricultural prices, the demand for these commodities is still existing and decent. During COVID-19, analysis indicates that prices were low and less volatile during the pandemic. The prices and returns of these commodities were low during COVID-19 because of the government's actions to respond to the virus's spread, which collapsed the market demand for food commodities.

Keywords: commodities market, commodity prices, generalised autoregressive conditional heteroscedasticity (GARCH), Price volatility, SAFEX

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Authors: Hyeonkyeong Yang, Minseok Son, Taekbeom Yoo, Woojin Park

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Human error is the most salient contributing factor to railway accidents. To reduce the frequency of human errors, many researchers and train designers have adopted ergonomic design principles for designing display-control layout in rail cab. There exist a number of approaches for designing the display control layout based on optimization methods. However, the ergonomically optimized layout design may not be the best design for train drivers, since the drivers have their own mental models based on their experiences. Consequently, the drivers may prefer the existing display-control layout design over the optimal design, and even show better driving performance using the existing design compared to that using the optimal design. Thus, in addition to ergonomic design principles, train drivers’ mental models also need to be considered for designing display-control layout in rail cab. This paper developed an ergonomic assessment system of display-control layout design, and an interactive layout design system that can generate design alternatives and calculate ergonomic assessment score in real-time. The design alternatives generated from the interactive layout design system may not include the optimal design from the ergonomics point of view. However, the system’s strength is that it considers train drivers’ mental models, which can help generate alternatives that are more friendly and easier to use for train drivers. Also, with the developed system, non-experts in ergonomics, such as train drivers, can refine the design alternatives and improve ergonomic assessment score in real-time.

Keywords: display-control layout design, interactive layout design system, mental model, train drivers

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Authors: L. Madouni, M. Ould Ouali, N. E. Hannachi

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Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model.

Keywords: Abaqus, concrete, constitutive model, numerical simulation

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Authors: Ana Serafimovic, Karthik Devarajan

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Non-negative matrix factorization approximates a high dimensional non-negative matrix V as the product of two non-negative matrices, W and H, and allows only additive linear combinations of data, enabling it to learn parts with representations in reality. It has been successfully applied in the analysis and interpretation of high dimensional data arising in neuroscience, computational biology, and natural language processing, to name a few. The objective of this paper is to assess a hybrid algorithm for non-negative matrix factorization with multiplicative updates. The method aims to minimize the symmetric version of Kullback-Leibler divergence known as intrinsic information and assumes that the noise is signal-dependent and that it originates from an arbitrary distribution from the exponential family. It is a generalization of currently available algorithms for Gaussian, Poisson, gamma and inverse Gaussian noise. We demonstrate the potential usefulness of the new generalized algorithm by comparing its performance to the baseline methods which also aim to minimize symmetric divergence measures.

Keywords: non-negative matrix factorization, dimension reduction, clustering, intrinsic information, symmetric information divergence, signal-dependent noise, exponential family, generalized Kullback-Leibler divergence, dual divergence

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Authors: Folajinmi Oluwasina, Magbagbeola Dairo, Ikeoluwapo Ajayi

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Contraception has proven to be an effective way of controlling fertility and spacing births. Studies have shown that contraception can avert the high-risk pregnancies and consequently reduce maternal deaths up to 32%. Uptake of modern contraception is promoted as a mechanism to address the reproductive health needs of men and women, as well as the crucial challenge of rapid population increase. A cross- sectional descriptive study using a two- stage systematic sampling technique was used to select 530 women of reproductive age out of 20,000 households. Respondents were interviewed using a semi-structured questionnaire. Knowledge was assessed on a 5 point score in which a score of ≤ 2 rated poor while perception was scored on 36 points score in which a score of ≤ 18 was rated low. Data were analyzed using descriptive statistics, Chi-square test and logistic regression at p< 0.05. There were 530 respondents. Age of respondents was 30.3 ±7.8 years, and 73.0% were married. About 90% had good knowledge of contraception while 60.8% had used contraceptives. The commonest source of information about contraception was mass media (72.8%). Minority (26.1%) obtained husbands approval before using contraceptive while 20.0% had used modern contraceptives before the first birth. Many (54.5%) of the respondents agreed that contraception helps in improving standard of living and 64.7% had good perception about contraception. Factors that hindered effective uptake of contraception services included poor service provider’s attitude (33.3%) and congestion at the service centers (4.5%). Respondents with nonuse of contraceptive before first birth are less likely to subsequently use contraceptives (OR= 0.324, 95% CI= 0.1-0.5). Husband approval of contraceptives use was the major determinant of women’s contraceptive use (OR = 3.4, 95% CI = 1.3-8.7). Respondents who had family planning centers not more than 5 kilometers walking distance to their residence did not significantly use contraception services (41.5%) more than 21.1% of those who had to take means of transportation to the service venues. This study showed that majority of the respondents were knowledgeable and aware of contraception services, but husband’s agreement on the use of modern contraceptives remains poor. Programmes that enhances husbands approval of modern contraception is thus recommended.

Keywords: contraception services, service provider’s attitude, uptake, husbands approval

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Authors: Kholil Kholil, Aniwidayati

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MDS (Multi-Dimensional Scaling) is one method that has been widely used to evaluate the use of natural resources. By using Raffish software tool, we will able to analyze sustainability level of the natural resources use. This paper will discuss the level of sustainability of the reservoir using MDS (Multi-Dimensional Scaling) based on five dimensions: (1) Ecology & Layout, (2) Economics, (3) Social & Culture, (4) Regulations & Institutional, and (5) Infrastructure and Technology. MDS analysis results show that the dimension of ecological and layout, institutional and the regulation are lack of sustainability due to the low index score of 45.76 and 42.24. While for the economic, social and culture, and infrastructure and technology dimension reach each score of 63.12, 64.42, and 68.64 (only the sufficient sustainability category). It means that the sustainability performance of Cirata Reservoir seriously threatened.

Keywords: MDS, cirata reservoir, carrying capacity, water quality, sustainable development, sedimentation, sustainability index

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

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In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: anthraquinones, physiologically active substances, phytopreparation, Ramon

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Neal Rupani

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Background: A surgical trainee has limited learning opportunities in the operating room in order to gain an ever-increasing standard of surgical skill, competency, and proficiency. These opportunities continue to decline due to numerous factors such as the European Working Time Directive and increasing requirement for service provision. It is therefore imperative to obtain the highest educational value from each educational opportunity. A measure that has yet to be validated in England on surgical trainees called the Operating Room Educational Environment Measure (OREEM) has been developed to identify and evaluate each component of the educational environment with a view to steer future change in optimising educational events in theatre. Aims: The aims of the study are to assess the reliability of the OREEM within England and to evaluate the surgical trainee’s objective perspective of the current operating room educational environment within one region within England. Methods: Using a quantitative study approach, data was collected over one month from surgical trainees within Health Education Thames Valley (Oxford) using an online questionnaire consisting of demographic data, the OREEM, a global satisfaction score. Results: 140 surgical trainees were invited to the study, with an online response of 54 participants (response rate = 38.6%). The OREEM was shown to have good internal consistency (α = 0.906, variables = 40) and unidimensionality, along with all four of its subgroups. The mean OREEM score was 79.16%. The areas highlighted for improvement predominantly focused on improving learning opportunities (average subscale score = 72.9%) and conducting pre- and post-operative teaching (average score = 70.4%). The trainee perception is most satisfactory for the level of supervision and workload (average subscale score = 82.87%). There was no differences found between gender (U = 191.5, p = 0.535) or type of hospital (U = 258.0, p = 0.099), but the learning environment was favoured towards senior trainees (U = 223.5, p = 0.017). There was strong correlation between OREEM and the global satisfaction score (r = 0.755, p<0.001). Conclusions: The OREEM was shown to be reliable in measuring the educational environment in the operating room. This can be used to identify potentially modifiable components for improvement and as an audit tool to ensure high standards are being met. The current perception of the education environment in Health Education Thames Valley is satisfactory, and modifiable internal and external factors such as reducing service provision requirements, empowering trainees to plan lists, creating a team-working ethic between all personnel, and using tools that maximise learning from each operation have been identified to improve learning in the future. There is a favourable attitude to use of such improvement tools, especially for those currently dissatisfied.

Keywords: education environment, surgery, post-graduate education, OREEM

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Babak Safaei, A. M. Fattahi

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In the present study we have investigated axial buckling characteristics of nanocomposite beams reinforced by single-walled carbon nanotubes (SWCNTs). Various types of beam theories including Euler-Bernoulli beam theory, Timoshenko beam theory and Reddy beam theory were used to analyze the buckling behavior of carbon nanotube-reinforced composite beams. Generalized differential quadrature (GDQ) method was utilized to discretize the governing differential equations along with four commonly used boundary conditions. The material properties of the nanocomposite beams were obtained using molecular dynamic (MD) simulation corresponding to both short-(10,10) SWCNT and long-(10,10) SWCNT composites which were embedded by amorphous polyethylene matrix. Then the results obtained directly from MD simulations were matched with those calculated by the mixture rule to extract appropriate values of carbon nanotube efficiency parameters accounting for the scale-dependent material properties. The selected numerical results were presented to indicate the influences of nanotube volume fractions and end supports on the critical axial buckling loads of nanocomposite beams relevant to long- and short-nanotube composites.

Keywords: nanocomposites, molecular dynamics simulation, axial buckling, generalized differential quadrature (GDQ)

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

Procedia PDF Downloads 355