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The rich and extensively studied archaeological record of the Near East provides an opportunity to develop a comprehensive archaeomagnetic dataset for exploring the behavior of the geomagnetic field with high precision. The Levantine archaeomagnetic curve (LAC) project is an ongoing effort to develop a continuous high‐resolution geomagnetic intensity curve for the Levant and Mesopotamia. The first version of the LAC covered the period between 3000 and 550 BCE. Here, we report archaeointensity data from 169 samples compiled into 32 groups dating between the 7th and the 1st centuries BCE aiming at extending the LAC up to the end of the first millennium BCE. Twenty‐two groups are assembled from storage jar handles bearing different types of royal seal impressions, which were used in Judah as part of a taxation administrative system. These groups are combined with 10 other groups of pottery assemblages, three of which are from Hellenistic destruction layers dated using radiocarbon and coins. The new curve shows that the Levantine Iron Age Anomaly (LIAA) spanned 550 years (1100 ‐ 550 BCE) and that the rate of decline during the last spike around 600 BCE could have reached ∼0.6 μT/year. During the 6th century, the virtual axial dipole moment (VADM) dropped from 160 ZAm2 to 125 ZAm2 after which field intensity only slightly increased to 135 ZAm2, until another considerable decline to ∼90 ZAm2 during the 3rd to the 1st centuries BCE. We highlight the archaeomagnetic implication of the new curve in inferring the relative chronological relationship between different stamp types. Plain Language Summary The Earth's magnetic field is continuously changing both in time and space in an unpredictable manner. A detailed description of how the magnetic field has changed throughout Earth's history offers constraints on our understanding of the mechanism generating the field in Earth's core. In this study, we reconstruct the intensity of the past field using an assemblage of well‐dated archaeological materials from Israel, dated to the Assyrian, Babylonian, Persian, Hellenistic and Hasmonean periods. This work is part of an ongoing effort to procure a high‐resolution curve describing the changes in field intensity for the Levant and Mesopotamia over the past several millennia. With the new data, we calculate the curve for the first three millennia BCE. The curve provides further details on an anomalous behavior of the field between 1100 BCE and 550 BCE, termed the Levantine Iron Age Anomaly (LIAA), during which the field intensity and its rate of change were significantly higher than today's. In addition, the curve forms the basis for an archaeomagnetic dating tool, which can be especially useful for periods when traditional archaeological dating methods fail to provide precise ages due to large uncertainties in radiocarbon dates. Key Points Archaeomagnetic intensity data from 32 groups of pottery in Israel dated between the 7th and the 1st centuries BCE The second generation of the Levantine Archaeomagnetic Curve (LAC.v.2.0) covering the last three millennia BCE The new data constrain the duration of the Levantine Iron Age Anomaly (LIAA) from 1100 BCE to 550 BCE

Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.

In this paper, we utilise a two-stage approach for addressing the post enrolment course timetabling (PE-CTT) problem. We attempt to find a feasible solution in the first stage. The solution is further improved in terms of soft constraint violations in the second stage. We present an enhanced variant of the Simulated Annealing with Reheating (SAR) algorithm, which we term Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning-based methodology to obtain a suitable neighbourhood structure for the search to operate effectively. We incorporate the average cost changes into the reheating temperature function. The proposed enhancements are tested on three widely studied benchmark data-sets. Our algorithm eliminates the need for tuning parameters in conventional SA as well as neighbourhood structure composition in SAR. The results are highly competitive with SAR and other state of the art methods. In addition, SAIRL is scalable when the runtime is extended. The algorithm achieves new best results for 6 instances and new mean results for 14 instances.

Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

In this work we investigate a new graph coloring constructive hyper-heuristic for solving examination timetabling problems. We utilize the hierarchical hybridizations of four low level graph coloring heuristics, these being largest degree, saturation degree, largest colored degree and largest enrollment. These are hybridized to produce four ordered lists. For each list, the difficulty index of scheduling the first exam is calculated by considering its order in all lists to obtain a combined evaluation of its difficulty. The most difficult exam to be scheduled is scheduled first (i.e. the one with the minimum difficulty index). To improve the effectiveness of timeslot selection, a roulette wheel selection mechanism is included in the algorithm to probabilistically select an appropriate timeslot for the chosen exam. We test our proposed approach on the most widely used un-capacitated Carter benchmarks and also on the recently introduced examination timetable dataset from the 2007 International Timetabling Competition. Compared against other methodologies, our results demonstrate that the graph coloring constructive hyper-heuristic produces good results and outperforms other approaches on some of the benchmark instances.

Palm oil is one of the plants that can be used to produce cooking oil, industrial oil, and fuel. Indonesia itself was one of the largest palm oil-producing countries in the world. OPEFB includes lignocellulose biomass, whose main content is 46.5% cellulose, 33.8% hemicellulose, and 32.5% lignin. This research method used 3 stages, namely microwave pretreatment, Fungal Treatment 1 (FT 1), and Fungal Treatment 2 (FT 2). The raw materials used are OPEFB, while the fungi used are Phanaerochaete chrysosporium , Tricodherma harzianum , Aspergillus niger , and Tricodherma viride . The first step to be prepared in this study was to prepare OPEFB powder measuring 20 mesh. OPEFB powder was then put in the microwave for 20, 40, and 60 minutes. OPEFB that had been heated in the microwave was then converted into a slurry with the addition of water with a ratio of 3:5 (w/w). The second and third phases were Fungal Treatment for ±10 days. The parameters analyzed were the introduction of OPEFB, product of microwave pretreatment, product of fungal treatment 1, and product of fungal treatment 2. The content to be analyzed were lignin, cellulose, hemicellulose, and glucose. The result of pretreatment revealed that microwave-based pretreatment with the addition of alkaline solution could eliminate lignin by 83.45% and increase holocellulose levels by up to 41.907% at 300 W power and 60 minutes. As for the result for fungal treatment, the best treatment for FT 1 was treatment ratio TH:PC 1:2, which could eliminate lignin for 47.55% and FT 2 with treatment ratio TV:AN 1:2 that could increase glucose up to 84.9%.

A standard honey-bee mating optimization algorithm (HBMO) utilizes the steepest descent local search algorithm as a worker. The steepest descent algorithm has the advantage of being simple to understand, fast and is easy to implement. However, it can easily trapped in a local optimum and subsequently restrict the performance of HBMO. Furthermore, the type of neighborhood structures that are used within the local search algorithm might impact on the performance of algorithm. This work aimed to enhance the performance of HBMO by using an adaptive guided variable neighborhood search (AGVNS) as a worker. The AGVNS algorithm is a variant of variable neighborhood search algorithm that incorporates some problem-specific knowledge and utilizes an adaptive learning mechanism to find the most suitable neighborhood structure during the searching process. In order to evaluate the effectiveness of the proposed algorithm, the Socha course timetabling dataset has been chosen as the tested domain problem. The results demonstrated that the performance of the proposed algorithm is comparable to other approaches in the literature. Indeed, the proposed algorithm obtained the best results as compared to other approaches on some instances. These results indicate the effectiveness of combining HBMO and AGVNS for solving course timetabling problems, hence demonstrated that the AGVNS can enhance the performance of HBMO.

In this paper, a PSO-based algorithm that hybridized Particle Swarm Optimization (PSO) and Hill Climbing (HC) is applied to high school timetabling problem. This hybrid has two features, a novel solution transformation and particle elimination. The proposed methodologies are tested on the XHSTT-2014 dataset (which is relatively new for the school timetabling problem) plus other additional instances. The experimental results show that the proposed algorithm is effective in solving small and medium instances compared to standalone HC and better than the conventional PSO for most instances. In a comparison to the state of the art methods, it achieved the lowest mean of soft constraint violations for 7 instances and the lowest mean of hard constraint violations for 1 instance.

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time.