A polynomial-time algorithm for a flow-shop batching problem with equal-length operations

A flow-shop batching problem with consistent batches is considered in which the processing times of all jobs on each machine are equal to p and all batch set-up times are equal to s. In such a problem, one has to partition the set of jobs into batches and to schedule the batches on each machine. The processing time of a batch B _i is the sum of processing times of operations in B _i and the earliest start of B _i on a machine is the finishing time of B _i on the previous machine plus the set-up time s. Cheng et al. (Naval Research Logistics 47:128–144, 2000) provided an O(n) pseudopolynomial-time algorithm for solving the special case of the problem with two machines. Mosheiov and Oron (European Journal of Operational Research 161:285–291, 2005) developed an algorithm of the same time complexity for the general case with more than two machines. Ng and Kovalyov (Journal of Scheduling 10:353–364, 2007) improved the pseudopolynomial complexity to \(O(\sqrt{n})\). In this paper, we provide a polynomial-time algorithm of time complexity O(log?³ n).     

Numerical integration of sparsely sampled data

In experimental work as well as in computational applications for which limited computational resources are available for the numerical calculations a coarse mesh problem frequently appears. In particular, we consider here the problem of numerical integration when the integrand is available only at nodes of a coarse uniform computational grid. Our research is motivated by the coarse mesh problem arising in ecological applications such as pest insect monitoring and control. In our study we formulate a criterion for assessing mesh coarseness and demonstrate that the definition of a coarse mesh depends on the integrand function. We then discuss the accuracy of computations on coarse meshes to conclude that the conventional methods used to improve accuracy on fine meshes cannot be applied to coarse meshes. Our discussion is illustrated by numerical examples.     

Can I find a partner? Undecidability of partner existence for open nets

We study open nets as Petri net models of web services, with a link to the practically relevant language WS-BPEL. For those nets, we investigate the problem of operability which we consider as fundamental as the successful notion of soundness for workflow nets, i.e., Petri net models of business processes and workflows. While we could give algorithmic solutions to the operability problem for subclasses of open nets in earlier work, this article shows that the problem is in general undecidable.     

Computer game development as a literacy activity

This study examined computer game development as a pedagogical activity to motivate and engage students in curriculum-related literacy activities. We hypothesized that as a consequence, students would improve their traditional reading and writing skills as well as develop new digital literacy skills. Eighteen classes of grade 4 students were assigned to either an experimental or control group. Both groups studied the same curriculum unit over a 10 week period, however, in addition the experimental group developed computer games related to the unit using a game development shell. An analysis of pre- and post-unit scores on two standardized literacy test batteries revealed that the experimental students performed significantly better on one of the subtests, a measure of logical sentence construction (p = .002). Field notes and teacher interview data indicated that game development helped improve student content retention, ability to compare and contrast information presented, utilize more and different kinds of research materials including digital resources, editing skills, and develop an insight into questioning skills.     

A-type K(+) current can act as a trigger for bursting in the absence of a slow variable

Models of bursting in single cells typically include two subsystems with different timescales. Variations in one or more slow variables switch the system between a silent and a spiking state. We have developed a model for bursting in the pituitary lactotroph that does not include any slow variable. The model incorporates fast, noninactivating calcium and potassium currents (the spike-generating mechanism), as well as the fast, inactivating A-type potassium current (I(A)). I(A) is active only briefly at the beginning of a burst, but this brief impulse of I(A) acts as a burst trigger, injecting the spike trajectory close to an unstable steady state. The spiraling of the trajectory away from the steady state produces a period of low-amplitude spiking typical of lactotrophs. Increasing the conductance of A-type potassium current brings the trajectory closer to the unstable steady state, increasing burst duration. However, this also increases interburst interval, and for larger conductance values, all activity stops. To our knowledge, this is the first example of a physiologically based, single-compartmental model of bursting with no slow subsystem.     

An Ensemble Machine Learning Technique for Stroke Prognosis

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting model based on three Machine Learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). We apply data preprocessing to manage the outliers and useless instances in the dataset. Furthermore, to address the problem of imbalanced data, we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique (SMOTE), allowing it to engage in the learning process actively. Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96% accuracy, 0.97% precision, 0.97% recall, and 0.96% F1-score. The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.     

Ensemble Learning for Fetal Health Classification

: Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories: healthy, suspected, and pathological. Secondly, we employ an ensemble of the above-described classifiers with the over-sampling method. We use a random over-sampling technique to balance CTG records to train the ensemble models. We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them. The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results. Results:: Each classifier evaluates accuracy, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values. Results reveal that the XGBoost, LGBM, and CatBoost classifiers yielded 99% accuracy. Conclusion:: Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment. A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.     

SDN-based teleprotection and control power systems: A study of available controllers and their suitability

With the popularization of the smart grid paradigm, substation automation technology has gradually been upgraded with additional processing and communication capabilities within control and protection devices. Hence, the research community is pressured to find new, efficient, and reliable communication solutions for such a scenario. That task is complicated by the stringent temporal requirements defined by the International Electrotechnical Commission (IEC) 61850 standard for power system communication comprising teleprotection schemes. Further compounding the issue is the critical nature of the power grid, in which availability is definitely a strong requirement. Software-defined networking (SDN) may provide powerful tools to fulfill those requirements at acceptable costs. As such, this paper surveys and compares several available SDN controllers and their applicability to the teleprotection scenario. It further evaluates controllers in terms of capacity and resilience through experiments in an emulated environment.     

Co-Evaporated MAPbI3 with Graded Fermi Levels Enables Highly Performing,Scalable, and Flexible p-i-n Perovskite Solar Cells

Recent progress of vapor-deposited perovskite solar cells (PSCs) has proved the feasibility of this deposition method in achieving promising photovoltaic devices. For the first time, it is probed the versatility of the co-evaporation process in creating perovskite layers customizable for different device architectures. A gradient of composition is created within the perovskite films by tuning the background chamber pressure during the growth process. This method leads to co-evaporated MAPbI₃ film with graded Fermi levels across the thickness. Here it is proved that this growth process is beneficial for p-i-n PSCs as it can guarantee a favorable energy alignment at the charge selective interfaces. Co-evaporated p-i-n PSCs, with different hole transporting layers, consistently achieve power conversion efficiency (PCE) over 20% with a champion value of 20.6%, one of the highest reported to date. The scaled-up p-i-n PSCs, with active areas of 1 and 1.96 cm², achieved the record PCEs of 19.1% and 17.2%, respectively, while the flexible PSCs reached a PCE of 19.3%. Unencapsulated PSCs demonstrate remarkable long-term stability, retaining ≈90% of their initial PCE when stored in ambient for 1000 h. These PSCs also preserve over 80% of their initial PCE after 500 h of thermal aging at 85 °C.