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In this paper, we consider a shortest path network interdiction problem with incomplete information and multiple levels of interdiction intensity. The evader knows the attacker’s decision on the network arcs that have been interdicted. However, the extent of damage on each arc depends on the interdiction intensity and the amount of budget spent for interdiction. We consider two cases in which the evader has incomplete information about both the intensity of attack on the interdicted arcs and the additional cost imposed for traversing those arcs. In the first case, the evader’s perception of this cost falls in an interval of uncertainty. In the second case, it is assumed that the evader estimates a relative frequency for each level of interdiction intensity. This gives rise to multiple uncertainty sets for the evader’s estimates of the additional cost. To handle the uncertainty that arises in both cases, a robust optimization approach is employed to derive the mathematical formulation of underlying bilevel optimization problem. For each case, we first take the well-known duality-based approach to reformulate the problem as a single-level model. We show that this method does not always end up with an integer solution or fails in achieving a solution within the time limit. Therefore, we develop an alternative algorithm based on the decomposition approach. Computational results show that the proposed algorithm outperforms the duality-based method to obtain the optimal solution. Last, a real case study is presented to show the applicability of the studied problem.

Introduction: Lack of high-quality sleep causes severe side effects like anxiety and changes in plasma concentration of oxalate. The current study investigated the impact of local extremely low-frequency magnetic fields (ELF-MFs) on inducing sleep (sleepiness) and anxiety in male rats. Methods: In this experimental study, 40 male rats were divided into four groups (n=10 for each group). The ELF-MF exposure (0, 10, and 18 Hz) was applied with an intensity of 200µT for three days (10 min/d). The sham-treated animal did not receive ELF-MF. Serum levels of oxalic acid (OA) and sleepiness were measured before and after the last exposure to ELF-MF or sham. Anxiety, sleepiness, and OA were measured using the elevated plus maze, open-field test (OFT), and ELISA test. Results: A comparison of oxalate levels before and after exposure to ELF-MF revealed that ELF-MF (10 Hz) decreased the serum level of oxalate (P<0.05). Comparing open/closed arm entry (in an elevated plus maze) between before and after exposure to ELF-MF revealed significant differences. Also, frequency, velocity, and distance moved were decreased in the open-field test. Conclusion: Results of the present study demonstrated that ELF-MF with short-time exposure may modulate the metabolism of OA and may modulate anxiety-like behavior or kind of induction of sleepiness in male rats.

Traumatic brain injury (TBI) is strongly associated with coagulopathy that occurs in 25–35% of patients. This complication is linked to higher mortality and morbidity. Recent lines of evidance have supported administration of fibrinogen concentrate (FC) in patients with severe TBI, while its efficacy remains controversial. In this study we aim to evaluate the effectiveness of serum fibrinogen level correction from 1.5 and 2.0 g/l to more than 2.0 g/l in patients with severe TBI undergoing traumatic cranial surgery. This randomized, single-blind, placebo-controlled clinical trial included trauma patients who had abbreviated injury scale (AIS) more than 3 in head and below 3 in other organs. FC was administered intravenously to patients with severe TBI undergoing TBI to correct the fibrinogen level above 2 g/l. Patients were randomly assigned to FC and control groups. The amount of intra-operative blood loss, packed cell (PC) transfusion, formation of new intracranial hemorrhage, and hemovac drainage were compared between the two study groups. Forty-seven of 65 participants received the study intervention within 40–112 min of admission. Intra-operative PC transfusion was higher in FC group (80%) compared to control group (55.5%) while the differance was not statistically significant (p > 0.05). Intra-operative blood loss was significantly higher in control group than FC group (P = 0.036). Chance of re-operation and new intracranial hematoma were not significantly different between two study groups. Early delivery of FC, decreases intraoperative bleeding. Although based on our findings it has no other effect on other parameters, further multicenter studies are recommended to investigate the role of early FC administration in management of post traumatic coagulopathy. •Early delivery of Fibrinogen concentrate, decreases intraoperative bleeding.•Fibrinogen has no other effect on other blood parameters.•Early administration of fibrinogen does not affect the prognosis of the patients with TBI.

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

In this paper, we introduce a distributed secondary frequency control scheme for an islanded ac microgrid under event-triggered communication. An integral type event-triggered mechanism is proposed by which each distributed generator (DG) asynchronously and periodically checks its triggering condition and determines whether to update its control inputs and broadcast its states to neighboring DGs. In contrast to existing event-triggered strategies on secondary control of microgrids, under the proposed sampled-data based event-triggered mechanism, DGs need not be synchronized to a common clock and each individual DG checks its triggering condition periodically, relying on its own clock. Furthermore, the proposed method efficiently reduces communication and computation complexity. We provide sufficient conditions under which all DGs' frequencies asymptotically converge to the common reference frequency value. Finally, effectiveness of our proposed method is verified by simulating different scenarios on a well-established islanded ac microgrid benchmark in the MATLAB/Simulink environment.

Non-intrusive load monitoring (NILM) as the process of extracting the usage pattern of appliances from the aggregated power signal is among successful approaches aiding residential energy management. In recent years, high volume datasets on power profiles have become available, which has helped make classification methods employed for the NILM purpose more effective and more accurate. However, the presence of multi-mode appliances and appliances with close power values have remained influential in worsening the computational complexity and diminishing the accuracy of these algorithms. To tackle these challenges, we propose an event-based classification process, in the first phase of which the \\(K\\)-nearest neighbors method, as a fast classification technique, is employed to extract power signals of appliances with exclusive non-overlapping power values. Then, two deep learning models, which consider the water consumption of some appliances as a novel signature in the network, are utilized to distinguish between appliances with overlapping power values. In addition to power disaggregation, the proposed process as well extracts the water consumption profiles of specific appliances. To illustrate the proposed process and validate its efficiency, seven appliances of the AMPds are considered, with the numerical classification results showing marked improvement with respect to the existing classification-based NILM techniques.

A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient NILM methods are event-based algorithms in which events of the aggregated signal are detected and classified in accordance with the appliances causing them. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer's comfort and privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring (SILM), where appliances are partitioned into blocks and the consumption of each block is monitored via separate power meters. While a greater number of meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of \"disaggregation difficulty metric (DDM),\" which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer's usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off.

Non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power consumption signals and accurately detects all events, (ii) extracts specific features of appliances, such as operation modes and their respective power consumption intervals, from their power consumption signals in the training dataset, and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low frequency measured data by existing smart meters.

The mountainous Samian Watershed hosts important rivers recently, significantly triggered by fast and unplanned urbanization, population growth, environmentally hazardous industrialization, and inappropriate dam construction. Nonetheless, this watershed has not yet been evaluated through the lens of river restoration. Therefore, this study aims (1) to apply the River Restoration Index (RRI), (2) to assess the significance of each river restoration criterion and sub-index, and (3) to identify priority hotspots for immediate restoration efforts across 27 sub-watersheds in this case study. First, we built a database containing meteorological, hydrological, land use, physiographic, soil, and economic data. Then, we calculated the general state of the watershed (GSW), connectivity (Con), riverbank conditions (RbC), and hydraulic risk reduction (HRR) sub-indices to develop a multi-domain RRI. Finally, the MEREC-ORESTE hybrid method supported sustainable government planning. The findings reveal significant environmental issues, notably in sanitation conditions, transversal connectivity, and urban encroachment on riverbanks. Sanitation risks were high throughout the watershed, while other eco-environmental risks varied across regions. The weights of 0.36, 0.16, 0.32, and 0.16 were assigned for GSW, Con, RbC, and HRR, respectively, highlighting the importance of GSW and RbC in river restoration activities. Priority management areas (with RRI below 0.50) cover 78% of the watershed.

Low serum selenium (Se) levels have been shown in critical illness, which is associated with poor clinical outcomes and a higher mortality rate. Se plays an important role in inflammation and oxidative stress. Since the overproduction of inflammatory cytokines and increased oxidative stress is a major component of critical illnesses, its supplementation has been demonstrated to have promising effects on critically ill patients. This study aims to review the evidence regarding the effects of Se supplementation on inflammatory and oxidative markers in critically ill patients. The literature review highlights alterations of inflammatory markers, including procalcitonin, leukocyte count, albumin, prealbumin, C-reactive protein (CRP), inflammatory cytokines, and cholesterol following Se supplementation in critically ill patients. Besides, the antioxidant properties of Se due to its presence in the structure of several selenoenzymes have been reported. Article highlights Low serum Se level have been shown in critical illness, which is associated with poor clinical outcome and higher mortality rate. Se plays an important role in inflammation and oxidative stress. Se supplementation can have promising effects by alterations of inflammatory markers and its antioxidant properties for critically ill patients.