Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices as a time series task. We proposed utilizing two different approaches to address this problem, namely, 1) machine learning (ML)-based models and 2) deep learning (DL)-based models. Thus, this research tuned a set of well-known predictive models on a real-life dataset. Those models are ensemble learning-based models, random forest, Ridge, Lasso, and Linear regression. Moreover, two new DL architectures based on gated recurrent unit (GRU) and long short-term memory (LSTM) models are proposed. Then, the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics, where the evaluation includes both numerical and visual comparisons of the examined models. The obtained results show that the ensemble learning models outperformed the classic machine learning-based models (e.g., linear regression and random forest) and the DL-based models

Spectral properties of (m;n)-isosymmetric multivariable operators

Inspired by recent works on $m$-isometric and $n$-symmetric multivariables operators on Hilbert spaces, in this paper we introduce the class of $(m, n)$-isosymmetric multivariables operators. This new class of operators emerges as a generalization of the $m$-isometric and $n$-isosymmetric multioperators. We study this class of operators and give some of their basic properties. In particular, we show that if ${\bf \large R} \in {\mathcal B}^{(d)}({\mathcal H})$ is an $(m,n )$-isosymmetric multioperators and ${\bf \large Q}\in {\mathcal B}^{(d)}({\mathcal H})$ is an $q$-nilpotent multioperators, then ${\bf\large R} +{\bf\large Q}$ is an $(m + 2q - 2,n+2q-1)$-isosymmetric multioperators under suitable conditions. Moreover, we give some results about the joint approximate spectrum of an $(m,n)$-isosymmetric multioperators

Marine data prediction : An evaluation of machine learning, deep learning, and statistical predictive models

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

Silent myocardial ischaemia in diabetic patients after general anaesthesia with 24h intravenous opioids or with epidural analgesia

AbstractObjectiveTo evaluate the effect of general anaesthesia with either continuous i.v. opioids (G/O) or thoracic epidural analgesia (G/EP) on postoperative transient myocardial ischaemia (TMI) in type 2 diabetic patients undergoing open cholecystectomy.MethodologyThis randomised controlled study was conducted on 50 patients with D.M. Patients were divided into G/O group or G/EP group. All patients had negative stress exercise test and at least two cardiac risks preoperatively. Epidural analgesia was established by 15ml of ropivacaine 0.2% with fentanyl 2μg/ml followed by 5–8ml/h of ropivacaine 0.1% with fentanyl 1μg for 24h postoperatively. Both studied groups received same general anaesthesia. Continuous i.v. fentanyl 100μg/h was given intraoperatively in group G/O followed by i.v. morphine PCA. Primary outcome measured postoperative TMI using 24h continuous ST segment analysis, endothelin-1(ET-1), troponin T, creatine kinase MB (CK-MB), and CKMB/CK preoperatively, 8h and 24h postoperatively. Second outcome measured dynamic stress (perioperative heart rate, blood pressure and postoperative pain).ResultsEndothelin-1 was above cutoff level preoperatively and rose up dramatically postoperatively in both studied groups. G/EP attenuated ET-1 elevation than G/O. Troponin T and CK-MB did not rise postoperatively in both studied groups. Postoperative CK-MB/ CK ratio was higher than 10% in 12 and eight patients in group G/O and G/EP, respectively. Twelve cardiac ischaemic events were noticed in four patients in group G/O and four events in two patients in group G/EP without significant difference in total duration of ischaemia between groups. G/EP lowered HR more significantly intraoperatively and gave better pain control for 4h postoperatively. In conclusion, D.M was associated with high ET-1 level. Upper abdominal surgery increased ET-1 release. G/EP attenuated ET-1 release more than G/O and produced more stable haemodynamic parameters and less postoperative pain. No superior cardioprotective effect was noticed in G/EP over G/O

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods

Right coronary artery severe stenosis as a predictor of new onset atrial fibrillation after coronary artery bypass surgery

Background: post-operative Atrial fibrillation (POAF) commonly occurs in patients undergoing cardiac surgeries including Coronary artery bypass grafting (CABG). Role of right coronary artery (RCA) stenosis in developing POAF after CABG is not settled yet. Objective: This retrospective study aimed to assess severe RCA stenotic lesion (70% or more narrowing) as a predisposing factor for POAF, in patients undergoing on-pump CABG, whether the RCA was grafted or not. Patients and methods: A total of 100 patients who underwent on-pump CABG in xxxx Hospitals and xxxx Hospitals between January 2022 and June 2022 were divided into two groups: Group (A) had severe right coronary artery disease, and Group (B) did not have severe right coronary artery disease. Following the operation, all patients were examined daily for electrocardiogram (ECG) alterations until they were discharged. Results: The mean age of the included patients was 52.6 (± 3), and 55 % of them were females. The mean Left ventricular ejection fraction was 56 (± 5). Incidence of atrial fibrillation was significantly higher in patients with severe RCA stenosis compared to those without severe RCA stenosis; p= 0.001 (68% vs 34%) denoting positive correlation between significant RCA stenosis and POAF; r=0.340, p=0.001. Conclusion: Severe RCA stenosis is one of the predictors of developing AF after CABG

Real-time and automatic system for performance evaluation of karate skills using motion capture sensors and continuous wavelet transform

In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean  ± standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real-time decisions

Determining of some physical and mechanical properties for designing tomato fruits cutting machine

الهدف من هذه الدراسة هو دراسة بعض الخصائص الفيزيائية والميكانيكية لثلاثة أصناف من الطماطم (نسمة ، ماسة ، 2020) للمساعدة في تصميم وتطوير آلة محددة لتقطيع الطماطم إلى نصفين متطابقين لاستخدامها في التجفيف الشمسي المفتوح. . تم تقدير الخواص عند محتوى رطوبة ثابت لثلاثة أصناف طازجة (نسمة ، ماسة ، 2020) من الطماطم عند 62.57 ، 68.58 ، 69.36٪ ديسيبل على التوالي. أظهرت النتائج أن متوسط قيمة الأبعاد المحورية ، المرتفع (H) ، القطر الأكبر (D max .) ، والقطر الأدنى (D min)..) من العينات كانت 73.98 و 69.26 و 61.03 ملم و 63.28 و 59.89 و 53.32 ملم ، و 70.99 و 53.86 و 49.60 ملم لأصناف ثمار الطماطم نسما وماسة و 2020 على التوالي. بلغ متوسط قيمة القطر الحسابي ، القطر الهندسي لثلاثة أصناف (نسمة ، ماسة ، 2020) للطماطم 69.26 و 58.76 و 58.11 و 67.69 و 58.52 و 57.33 على التوالي. وبلغ متوسط قيمة الكتلة والكثافة 181.74 و 120.14 و 109.96 و 0.991 و 0.991 و 0.972 على التوالي. في حين؛ متوسط قيمة مساحة السطح ومعامل التعبئة والكروية ونسبة العرض إلى الارتفاع كانت 144.61 ، 107.93 ، 103.65. ، 0.533 ، 0.572 ، 0.562. ، 92.13 ، 92.67 ، 81.11 ، 94.48 ، 94.99 ، 76.39 على التوالي لأصناف (نسمة ، ماسة). و 2020). كانت أدنى قيم لمعامل الاحتكاك الساكن 0.427 ، 0.266 ، 0.242 مع الخشب الرقائقي بينما أعلى قيمة كانت 0.566 ، 0.310 ، 0. 388 مطاط من ثلاثة أصناف (نسمة ، ماسة ، 2020) على التوالي. وتعني قيم الصلابة كانت 4.70 و 5.95 و 4.9 نيوتن / سم2 لأصناف (نسمة ، ماسة ، 2020) على التوالي

Stiff Person syndrome: a case report

Stiff person syndrome (SPS) is rather unique among neurologic diagnoses. At relaxation, motor- unit activation, continuous agonist and antagonist muscular contractions, as well as contractions triggered by tactile triggers, quiescent stretch, and involuntary movement of affected or unaffected musculature, startled sounds and emotional stimuli are the clinical signs of SPS. Sleep, general anesthesia, myoneural, and peripheral nerve blockage all help to reduce rigidity and spasms. The syndrome may be a sporadic autoimmune syndrome (associated with anti–glutamic acid decarboxylase (GAD) antibodies and often accompanied by other autoimmune diseases such as type 1 diabetes) or paraneoplastic (associated with anti–amphiphysin antibodies). People with SPS respond to high doses of diazepam and several anti-convulsants, gabapentin and tiagabine. Immunomodulatory drugs including steroids, plasmapheresis, and intravenous immunoglobulin appear to help significantly. The symptoms of our patient progressed slowly over time. Neuroimaging and electrophysiological studies ruled out other possible causes of comparable symptoms such as neuromyotonia. Raised anti-GAD autoantibodies titer in serum found by immunocytochemistry assays, our patient's history, clinical examination findings, and reaction to benzodiazepines all pointed to SPS.