A combined Mixed Integer Programming model of seaside operations arising in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely the Berth Allocation Problem, the Quay Crane Assignment Problem, and the Quay Crane Scheduling problem, which have to be solved to carry out these seaside operations in container ports. Each one of these problems is complex to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence the need to solve them in a combined form. The problem is formulated as a mixed-integer programming model with the objective being to minimise the tardiness of vessels. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX

Isolation and Purification of Chickpea (Cicer arietinum) Seeds Protein, Testing Their Antibacterial Activity, and Using to Extend The Shelf Life of Beef Patties

Antimicrobial proteins (AMP) from chickpea (Cicer arietinum L.) seeds were isolated and purified using the saturation of (NH4)2So4 by 80% and gel filtration through Sephacryl S-200, the inhibition zone of the separated peak was 24, and 22 mm for growth of Escherichia coli and Salmonella typhimurium respectively. The molecular mass was 28385 Da estimated through Sephacryl S-200. The optimum pH for activity was 5.5. It was stable at 4.5-7.5, while it lost 21.07 and 55.65% from its activity at pH  3 and 8 respectively, the optimum temperature for activity was 35°C and it was stable at 35°C for 60 min, while it lost all inhibitory activity at 65°C for the same time. The use of AMP at 100 mg resulted in an inhibition zone of 37± 2.92, 35±1.68, 32±2.33, and 33±2.09 mm, with a significant difference at (P≤0.05) against E. coli and S. Typhimurium, Staphylococcus aureus, and Bacillus cereus, respectively. The use of AMP to extend the shelf life of beef patties at 100 mg.kg-1 resulted in a decrease in the total count of bacteria, as it reached 7.4×102±0.18, 4.6×102±0.22 and 2.8×102±0.19 CFU.g-1, while it was 7.4×102±0.23, 8.2×102±0.31, and 9.5×102±0.27 CFU.g-1 in the control sample during a storage period of 0, 3, and 6 d at 4°C. It was observed that there was no significant difference in the control treatment and AMP added on 0 d, while a significant difference was observed at (P≤0.05) for treatments at a storage period of 3 and 6 d at 4°C

Isolation and Purification of Chickpea (Cicer arietinum) Seeds Protein, Testing Their Antibacterial Activity, and Using to Extend The Shelf Life of Beef Patties

Antimicrobial proteins (AMP) from chickpea (Cicer arietinum L.) seeds were isolated and purified using the saturation of (NH4)2So4 by 80% and gel filtration through Sephacryl S-200, the inhibition zone of the separated peak was 24, and 22 mm for growth of Escherichia coli and Salmonella typhimurium respectively. The molecular mass was 28385 Da estimated through Sephacryl S-200. The optimum pH for activity was 5.5. It was stable at 4.5-7.5, while it lost 21.07 and 55.65% from its activity at pH  3 and 8 respectively, the optimum temperature for activity was 35°C and it was stable at 35°C for 60 min, while it lost all inhibitory activity at 65°C for the same time. The use of AMP at 100 mg resulted in an inhibition zone of 37± 2.92, 35±1.68, 32±2.33, and 33±2.09 mm, with a significant difference at (P≤0.05) against E. coli and S. Typhimurium, Staphylococcus aureus, and Bacillus cereus, respectively. The use of AMP to extend the shelf life of beef patties at 100 mg.kg-1 resulted in a decrease in the total count of bacteria, as it reached 7.4×102±0.18, 4.6×102±0.22 and 2.8×102±0.19 CFU.g-1, while it was 7.4×102±0.23, 8.2×102±0.31, and 9.5×102±0.27 CFU.g-1 in the control sample during a storage period of 0, 3, and 6 d at 4°C. It was observed that there was no significant difference in the control treatment and AMP added on 0 d, while a significant difference was observed at (P≤0.05) for treatments at a storage period of 3 and 6 d at 4°C

Studying the Effect of Cutting Conditions in Turning Process on Surface Roughness for Different Materials

Surfaces quality is one of the most specified customer requirements for machine parts. The major indication of surfaces quality on machined parts is surface roughness. The research aim is to study the cutting conditions and their effects on the surface roughness. This research will use regression models and neuro-fuzzy to predict surface roughness over the machining time for variety of cutting conditions in turning. In the experimental part for turning, different types of materials (Aluminum alloy, brass alloy, and low carbon steel) were considered with different cutting speed, and feed rate. A linear regression and neuro-fuzzy model depending on statistical-mathematical method between surface roughness, Ra, and cutting condition will be derived, for the three materials. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness will be determined. The model will be established between the cutting conditions and surface roughness using regression and neuro-fuzzy model. As the results of this work, the linear regression and neuro-fuzzy model will be used in predicting surface roughness, can be used in manufacturing systems, this modeling helps engineer to reduce the efforts and improve the quality

Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Integrated models of the quay crane assignment problem (QCAP) and the quay crane scheduling problem (QCSP) exist. However, they have shortcomings in that some do not allow movement of quay cranes between vessels, others do not take into account precedence relationships between tasks, and yet others do not avoid interference between quay cranes. Here, an integrated and comprehensive optimization model that combines the two distinct QCAP and QCSP problems which deals with the issues raised is put forward. The model is of the mixed-integer programming type with the objective being to minimize the difference between tardiness cost and earliness income based on finishing time and requested departure time for a vessel. Because of the extent of the model and the potential for even small problems to lead to large instances, exact methods can be prohibitive in computational time. For this reason an adapted genetic algorithm (GA) is implemented to cope with this computational burden. Experimental results obtained with branch-and-cut as implemented in CPLEX and GA for small to large-scale problem instances are presented. The paper also includes a review of the relevant literature

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times

Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated their capability to precisely detect anomalies. This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks. Firstly, a Sparse Autoencoder (SAE) is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error. Secondly, the Convolutional Neural Network (CNN) technique is employed to create a binary classification approach. The proposed SAE-CNN approach is validated using the Bot-IoT dataset. The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%, precision of 99.9%, recall of 100%, F1 of 99.9%, False Positive Rate (FPR) of 0.0003, and True Positive Rate (TPR) of 0.9992. In addition, alternative metrics, such as training and testing durations, indicated that SAE-CNN performs better

Tetralogy of Fallot

Tetralogy of Fallot is a congenital cardiac malformation that consists of an interventricular communication, also known as a ventricular septal defect, obstruction of the right ventricular outflow tract, override of the ventricular septum by the aortic root, and right ventricular hypertrophy

3D Real-Time Echocardiography Combined with Mini Pressure Wire Generate Reliable Pressure-Volume Loops in Small Hearts

BACKGROUND: Pressure-volume loops (PVL) provide vital information regarding ventricular performance and pathophysiology in cardiac disease. Unfortunately, acquisition of PVL by conductance technology is not feasible in neonates and small children due to the available human catheter size and resulting invasiveness. The aim of the study was to validate the accuracy of PVL in small hearts using volume data obtained by real-time three-dimensional echocardiography (3DE) and simultaneously acquired pressure data. METHODS: In 17 piglets (weight range: 3.6–8.0 kg) left ventricular PVL were generated by 3DE and simultaneous recordings of ventricular pressure using a mini pressure wire (PVL3D). PVL3D were compared to conductance catheter measurements (PVLCond) under various hemodynamic conditions (baseline, alpha-adrenergic stimulation with phenylephrine, beta-adrenoreceptor-blockage using esmolol). In order to validate the accuracy of 3D volumetric data, cardiac magnetic resonance imaging (CMR) was performed in another 8 piglets. RESULTS: Correlation between CMR- and 3DE-derived volumes was good (enddiastolic volume: mean bias -0.03ml ±1.34ml). Computation of PVL3D in small hearts was feasible and comparable to results obtained by conductance technology. Bland-Altman analysis showed a low bias between PVL3D and PVLCond. Systolic and diastolic parameters were closely associated (Intraclass-Correlation Coefficient for: systolic myocardial elastance 0.95, arterial elastance 0.93, diastolic relaxation constant tau 0.90, indexed end-diastolic volume 0.98). Hemodynamic changes under different conditions were well detected by both methods (ICC 0.82 to 0.98). Inter- and intra-observer coefficients of variation were below 5% for all parameters. CONCLUSIONS: PVL3D generated from 3DE combined with mini pressure wire represent a novel, feasible and reliable method to assess different hemodynamic conditions of cardiac function in hearts comparable to neonate and infant size. This methodology may be integrated into clinical practice and cardiac catheterization programs and has the capability to contribute to clinical decision making even in small hearts

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic