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Extended Abstract Background: One of the major goals of watershed management operations is to reduce soil erosion and waste, and in the next step, to prevent the exit of eroded particles from the watershed. For this purpose, sediment control structures are usually built throughout watersheds. Check dams also play a special and important role in reducing the sediment load of rivers. Sediment dams are constructed to prevent the entry of sediment particles caused by the erosion of upstream lands into the main river. Particle size distribution is one of the key physical characteristics of sediment materials that is used as an important factor in sediment management in watersheds. Many quantitative and qualitative characteristics of sediments, such as porosity, permeability, transferability, chemical reactivity, and erodibility, are influenced by the size of particles and their mode of distribution. To achieve better results in watersheds and especially sediment trapping behind check dams, knowing the type of sediment and its characteristics helps in the better management of watersheds and water and soil resources. Comparing check dams with other water and soil conservation measures to reduce sedimentation, it seems that check dams are the most effective way to quickly reduce the entry of coarse sediments into rivers. Considering the role of sediment traps in trapping sediment particles, it is very important to study the sediments behind these dams in terms of particle size distribution characteristics. This research aims to investigate the size distribution of sediment particles behind check dams and to investigate the efficiency of sediment trapping dams in trapping sediments in the Lashkaran Salmas watershed. Methods: The Lashkaran watershed is located in the northwest of Salmas city in West Azerbaijan province. According to the country division, it is located in the Urmia Lake River basin, and its geographical coordinates are in the range of 44°38'‌ to 44°40'‌ east longitude and 38°17'‌ to 38°18 '‌north latitude, with an area of of 363 hectares. First, the location and dimensions of the structures and the height of the accumulated sediment behind the structures were examined and measured through field monitoring. To determine the size distribution of sediment particles, the sediments behind each check dam were sampled at two depths of 0-25 and 25-50 cm. In total, 32 sediment samples were collected from eight dams. The samples were transferred to the laboratory, air dried, and then passed through a 2 mm sieve. The particle size distribution of sediment samples was determined by the hydrometric method. To quantitatively investigate sediment particle size distribution, an optimal model was selected from several particle size distribution models, and particle size distribution indices, such as D50, were calculated with the optimal model. Results: In the studied watershed, check dams have been built on the streams with three orders, including order one, three, and four. Seven dams have been built on the third-order streams, one dam on the fourth-order stream, and one dam on the first-order stream. The results show that the particle size distribution does not follow a specific and regular pattern in order three streams, and the average percentages of sand, silt, and clay are 65.7%, 28.2%, and 6.1%, respectively. In order four streams, the average percentages of sand,silt, and clay are 80.4%, 16.5%, and 3.2%, respectively. The respective percentages are 82.7%, 9.8%, and 7.6% in the first order. In general, the average percentage of sand particles, followed by silt, is the largest volume of particles making up sediments. The sediment size distribution of the third-order steams shows that the amount of changes in sediment particles does not have a regular trend, but in general, the percentages of clay and silt increase with increasing distance from the outlet of the watershed, with a decrease in the amount of sand particles. The fourth-order stream has high flow intensity and assumedly carries larger particles. Along with approaching the outlet, the amount of coarse particles decreases due to the presence of check dams and the accumulation of sediments behind the dams. As the distance from the outlet increases, the amount of clay and silt tends to decrease while sand particles tend to increase. The first-order stream has the lowest flow intensity. The amount of clay is constant with increasing distance, with increased silt and decreased sand percentages. Three efficiency coefficients were used to assess the efficiency of the models. The results of the sediment granulation distribution showed that check dams were most efficient in trapping sediments such as sand with a share of 69.5%. The results of examining the efficiency coefficients of particle size distribution models show that the Fredlund model with the highest coefficient of determination (0.98), the lowest RMSE index (0.03), and Akaike's statistic (59.54) is the most efficient model in measuring the distribution of sediment particle size compared to the other seven models. Conclusion: The average particles of sand, silt, and clay (69.5%, 24.7%, and 5.8%, respectively) indicate the performance of check dams in controlling coarse-grained sediments such as sand. The results show that the dams upstream the watershed have restrained larger particles, but this trend is not seen in other check dams. It seems that the type and sequence of the dam are more effective in sediment changes. Moreover, the runoff carries fine to coarse particles, and regular diameter differentiation cannot be obtained due to the short distance between the dams.

Abstract Thanks to reinforcement learning (RL), decision‐making is more convenient and more economical in different situations with high uncertainty. In line with the same fact, it is proposed that prosumers can apply RL to earn more profit in the transactive energy market (TEM). In this article, an environment that represents a novel framework of TEM is designed, where all participants send their bids to this framework and receive their profit from it. Also, new state‐action spaces are designed for sellers and buyers so that they can apply the Soft Actor‐Critic (SAC) algorithm to converge to the best policy. A brief of this algorithm, which is for continuous state‐action space, is described. First, this algorithm is implemented for a single agent (a seller and a buyer). Then we consider all players including sellers and buyers who can apply this algorithm as Multi‐Agent. In this situation, there is a comprehensive game between participants that is investigated, and it is analyzed whether the players converge to the Nash equilibrium (NE) in this game. Finally, numerical results for the IEEE 33‐bus distribution power system illustrate the effectiveness of the new framework for TEM, increasing sellers' and buyers' profits by applying SAC with the new state‐action spaces. SAC is implemented as a Multi‐Agent, demonstrating that players converge to a singular or one of the multiple NEs in this game. The results demonstrate that buyers converge to their optimal policies within 80 days, while sellers achieve optimality after 150 days in the games created between all participants.

Abstract Inertial navigation on a chip has long been constrained by the noise and stability issues of micromechanical Coriolis gyroscopes, as silicon, the dominant material for microelectromechanical system devices, has reached the physical limits of its material properties. To address these challenges, this study explores silicon carbide, specifically its monocrystalline 4H polytype, as a substrate to improve gyroscope performance due to its low phonon Akhiezer dissipation and its isotropic hexagonal crystal lattice. We report on low-noise electrostatic acoustic resonant gyroscopes with mechanical quality factors exceeding several millions, fabricated on bonded 4H silicon carbide-on-insulator wafers. These gyroscopes operate using megahertz frequency bulk acoustic wave modes for large open-loop bandwidth and are tuned electrostatically using capacitive transducers created by wafer-level deep reactive ion etching. Experimental results show these gyroscopes achieve superior performance under various conditions and demonstrate higher quality factors at increased temperatures, enabling enhanced performance in an ovenized or high-temperature stabilized configuration.

Abstract Unbalanced operation of distribution networks, as a crucial but inevitable aspect of power quality, brings about economic and technical issues. The increase in active power loss is more concerning due to the economic and technical implications. Economic sustainability of liberalized distribution networks is hugely tied to the availability of transparent and effective approaches to determine the stakeholders of this sort of phenomenon. This is done by awarding/penalizing them accordingly. Studies in the literature have not considered the incremental losses that unbalanced operations cause to distribution systems. Here, a mathematical approach based on vector analysis in the symmetrical components space is introduced. This approach quantifies the contribution of each unbalanced load to unbalanced operation and increased power losses. This technique discriminates between the mutual effects of different unbalanced loads on the power losses of the entire network. By means of the proposed approach, the best candidate unbalanced load(s)/location(s) to perform the corrective balancing actions and their ranking are precisely specified. Vector representation of the developed method simplifies its understanding, utilization, and expansion. For validation, the proposed method is applied to the IEEE 37‐bus test distribution network under various scenarios. The results confirm the sound results and practical merits of the developed method.

The reuse of wastewater (WW) for crop irrigation is increasingly recognized as an alternative to freshwater irrigation in arid and semi-arid regions. However, there is a significant gap in knowledge regarding the soil health index (SHI) and factors influencing topsoil and subsoil in cropland under long-term WW irrigation. This study aimed to comparatively assess soil health attributes between topsoil and subsoil in smallholder farmlands that have been irrigated with WW for over 50 years. This assessment utilized a combination of soil physico-chemical and fertility attributes, along with heavy metal concentrations. The soil health index (SHI) was developed using linear (SHI - L) and nonlinear (SHI - NL) models, based on the Total Data Set (TDS) and Minimum Data Set (MDS). Statistically significant differences (P ≤ 0.05) were observed between topsoil and subsoil for the soil stability index (SSI), organic matter (SOM), calcium carbonate equivalent (CCE), electrical conductivity (EC), sodium adsorption ratio (SAR), macro- and micronutrients, and heavy metals (Zn, Cu, Cd, Pb, and Ni). In contrast, soil bulk density (BD), pH, and cation exchange capacity (CEC) did not show significant differences. The mean SHI - L and SHI - NL values ranged from 0.68 to 0.77 and 0.46–0.53 for topsoil, and from 0.66 to 0.74 and 0.45–0.51 for subsoil, respectively. The SHI values were higher in the topsoil, with increases ranging from 2.3 % to 7.1 % for SHI - L and 0.65–11.3 % for SHI - NL compared to the subsoil. The regression coefficients between SHIs and corn yield were higher in the topsoil (0.46–0.49) than in the subsoil (0.20–0.22). Furthermore, the SHI - NL model demonstrated greater precision than the SHI - L model in predicting corn yield in both soil depths. These findings highlight the effectiveness of SHI assessments, particularly the SHI - NL model, in analyzing changes in soil health indices with depth and their relationship with crop performance in long-term WW-irrigated smallholder farmlands. This research provides valuable insights into addressing soil health challenges in similar agricultural systems.

Resilience of the power system against natural disasters is a vital need for sustainable energy supply. As a result of global warming, lakes and rivers have dried out, resulting in dust hubs that threaten the normal operation of outdoor power system equipment. Unlike other events like hurricanes and blizzards, the impact of extreme salt dust on power system insulator failures and network resilience in affected areas remains unexamined. In this paper, to avoid power curtailment caused by insulators breakdown in electricity distribution networks, the resiliency assessment and enhancement of these networks against salt dust is investigated. Failure mechanism analysis of insulators and fragility curves extraction of them in face of salt pollution and relative humidity are done using mathematical modelling and experimental tests to extract the breakdown probability; Experimental tests are conducted in the High Voltage Laboratory, University of Tehran (HVLUT) and a novel method is proposed to extract 3-dimensional fragility curves of insulators. A Monte Carlo-based resiliency assessment method is then employed to obtain resiliency curve against the salt dust. Some suitable indicators are introduced for this purpose. In addition, several resiliency enhancement measures are proposed and ranked using a benefit to cost ratio (BCR) index. Numerical simulations are conducted on two real distribution feeders in a distribution grid around Urmia Salt Lake, Iran. Numerical results confirm the effectiveness and applicability of the proposed method.

Abstract Emergence of different stakeholders with contrary objectives in active distribution network (ADN) requires for a fair network expansion planning that considers all of the contrary objectives. This paper provides an expansion planning model for ADN strategic visioning from electric distribution stakeholders' organization (EDSO) perspective, which takes into account the objectives of all stakeholders. The stakeholders are network owner, active customers (ACs), consumers, and environmental regulatory body (ERB). The proposed model is multistage bi‐level, where the upper‐level minimizes the present value of the total costs imposed to all ADN stakeholders and the lower‐level minimizes the total generation costs within the distribution‐level electricity market. The Karush–Kuhn–Tucker optimality conditions and the linearization techniques are employed through which the model is converted to mixed integer linear programming which can be tackled effectively. The model provides a visionary expansion plan that policymaker must legislate to realize it. Effectiveness of the model in providing a visionary expansion plan considering EDSO's perspective in making cost effective planning decisions per social welfare goals is verified through several case studies on 33‐bus standard test system. According to the simulation results, transition from DSO to EDSO in the presence of low‐capacity ACs improves social welfare by 5%. But this improvement is up to 70% in the presence of high‐capacity ACs.

Abstract We investigated the individual and synergistic impact of gypsum, elemental sulfur, vermicompost, biochar, and microbial inoculation on soil health improvement in degrading calcareous saline-sodic soils. We developed Linear and nonlinear soil health quantification frameworks to assess the efficacy of remedial practices. The combined inoculated chemical and organic treatments; gypsum + vermicompost and elemental sulfur + vermicompost with 134% (0.29 versus 0.68) and 116% (0.29 versus 0.62) increases in nonlinear index, significantly increased the efficacy of amendments compared with control. An increase in the overall soil health index ranged between 12 to 134%. Microbial inoculation further enhanced the impact of treatments on soil health. Soil health properties included in the indexes explained 29 to 87% of the variance in wheat growth. The findings bring insight into the cost-effective and environmentally sustainable practices to recover degraded saline-sodic soils. Furthermore, the introduced soil health indexes offer a quantitative evaluation of soil remediation strategies.

Abstract In this paper, a modification to the eigenmode operation of resonant gyroscopes is introduced. The multi-coefficient eigenmode operation can improve cross-mode isolation due to electrode misalignments and imperfections, which is one of the causes of residual quadrature errors in conventional eigenmode operations. A 1400 µm annulus aluminum nitride (AlN) on a silicon bulk acoustic wave (BAW) resonator with gyroscopic in-plane bending modes at 2.98 MHz achieves a nearly 60 dB cross-mode isolation when operated as a gyroscope using a multi-coefficient eigenmode architecture. The as-born frequency mismatches in multiple devices are compensated by physical laser trimming. The demonstrated AlN piezoelectric BAW gyroscope shows a large open-loop bandwidth of 150 Hz and a high scale factor of 9.5 nA/°/s on a test board with a vacuum chamber. The measured angle random walk is 0.145°/√h, and the bias instability is 8.6°/h, showing significant improvement compared to the previous eigenmode AlN BAW gyroscope. The results from this paper prove that with multi-coefficient eigenmode operations, piezoelectric AlN BAW gyroscopes can achieve a noise performance comparable to that of their capacitive counterpart while having the unique advantage of a large open-loop bandwidth and not requiring large DC polarization voltages.

Abstract The anthropogenic heavy metal dissemination in the natural environment through riverine sediments is a major ecological and public health concern around the world. This study gives insight into the source apportionment and potential ecological and health risks of heavy metals in river sediments of the Urmia Lake basin, a natural world heritage located in northwestern Iran. A comprehensive sediment sampling was conducted in seven major rivers feeding the basin during the summer and winter of 2021. Samples were analyzed for zinc (Zn), copper (Cu), cadmium (Cd), lead (Pb), and nickel (Ni) contents and a suite of chemical and physical properties. Subsequently, Pollution Index (PI), Pollution Load Index (PLI), Ecological Risk (ER), Hazard Quotients (HQ), Hazard Index (HI), and Carcinogenic Risk (CR) indices were determined. The mean concentration of heavy metals in all rivers’ sediments exhibited the descending order of Ni > Zn > Pb > Cu > Cd during both summer and winter. Multivariate analysis suggested that Zn was primarily initiated from natural processes, Cd and Pb were affected by human activities, and Cu along Ni were derived from natural and anthropogenic factors. The PI unveiled that most sediment samples were unpolluted to slightly polluted by Zn, Cu, and Pb, and slightly to moderately polluted by Cd. PLI and ER indices demonstrated that the sediment poses non to moderate pollution and low to moderate ecological risk, respectively. Using a human health risk approach, we found that the HI values of all heavy metals and THI were less than one for children and adults implying non-carcinogenic risk in the analyzed sediments. Carcinogenic effects of Cd and Pb at all rivers sediments via ingestion, inhalation, and dermal contact were almost within tolerable risks (1 × 10−6 to 1 × 10−4) for children and adults. PI, PLI, ER, HQ, HI, and CR index values of sediment samples during the summer were higher than those during the winter. This is attributed to the greater heavy metal concentrations and the lower water flow during summer. Our results provide practical information for better management and control of heavy metal pollution in aquatic-sedimentary ecosystems.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient’s chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time–frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set—outperforming the deterministic time–frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Introduction Sand mines are one of the most important active mines. One of the key properties of sediments is their particle size distribution, which affects many physical and chemical properties such as hydraulic and electrical properties and characteristics related to the transportability by erosive agents. Determining the size of sediment particles in different environments can be used in reclaiming sediments from washing river and mountain sand mines for physical and chemical remediation of soil. By determining the size of sediment particles in different environments, it is possible to measure the reuse of sediments from washing river and mountain sand mines for physical and chemical remediation of soil.Materials and Methods The main aim of this study was to compare the capability of some mathematical models in describing particle size distribution of sediments of 26 sand mines in Urmia. For this purpose, sampling was performed from mine sedimentation ponds. By examining each of the mines, in the field surveyes of sediment ponds, sediment sampling was done. In sampling of each mine, three samples were taken from three different points of stilling pond (entrance, middle and end of the pond) in depth (approximate depth of 20 cm) and composed of sediment accumulation profiles with approximately one kilogram weight. The particle size distribution was determined by hydrometric method. In order to describe the sediments, 4 mathematical models of sediment size distribution including Weibull, Fredlund, Van genuchten and Jacky models were used. Different aspects of models performance were evaluated by some efficiency criteria.Results and Discussion The samples taken from mountain and river mines were in the sandy loam and loamy texture class, respectively, so they have fine to medium grain texture. The results showed the difference between the amount of particles forming river and mountain sediments and there is small difference between river and mountain sediments in terms of the amount of component particles. In mountain sediments, the amount of clay, silt and sand is much more than river sediments and the amount of sand in river sediments is more than mountain sediments. Based on the results, most of the sediments are sand, silt and very fine sand and clay, respectively. Six efficiency coefficients were used to evaluate the accuracy of sediment particle grading models. The results showed that Fredlund model had better performance in describing sediment size distribution than other models.Conclusion Analysis of daily, monthly, seasonal and annual trends of precipitation and minimum and maximum temperatures in the period Fredlund model has the lowest error compared to other models and increasing the number of model parameters is not a reason to increase the accuracy of the model.It is suggested that the physical and chemical properties of sediments of mountain and river sand mines be studied separately so that according to different origins and different processes involved in their formation, the feasibility of using these sediments for soil optimization can be investigated.

Abstract The huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data‐driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv‐AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv‐AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine‐tuned for the problem at hand. It uses an ensemble learning method to augment the fully‐labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34‐node and IEEE 123‐node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real‐world DPMU dataset is utilized to evaluate performance of the Conv‐AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.

Abstract An effective robust fuzzy model predictive control (RFMPC) method for secondary voltage control in islanded microgrids (μGs) is presented here. In contrast to the existing techniques, which require a detailed model of μG and an ideal communication network between μG central controller and primary local controllers, RFMPC is synthesized for a non‐linear model of the μG with various time delays, uncertainties, and bounded disturbances. The famous Takagi‐Sugeno fuzzy approach is adopted to approximate the inherently non‐linear model of μG by locally linear dynamics. The Lyapunov–Razumikhin functional method is exploited to deal with time delays. In this regard, sufficient conditions are provided in the form of linear matrix inequalities (LMIs). Then, a sequence of control laws corresponding to a set of terminal constraints is computed offline. Doing so, the online stage is reduced to solving a convex problem with LMI constraints considering the sequence of constraint sets obtained in the offline stage, thereby reducing the computational burden significantly. Robust positive invariance and input‐to‐state stability property concerning communication network deficiency are then speculated. The effectiveness of the proposed RFMPC is verified via a comprehensive suite of simulations in the MATLAB/SimPowerSystems environment.

Background and Objective: With the industrialization of communities, population increase and use of surface water, river pollution has been increased by agricultural, industrial pollutants and urban wastewater. Therefore, investigation of river pollution for regional and environmental planning is of great importance. To evaluate surface water pollution, a number of surface water quality indices have been investigated. Materials and Methods: Iran water quality index for surface water resources (IRWQISC) approach was used to evaluate the water quality of the Mahabadchai river, Iran. Sampling was carried out along the Mahabadchai river based on some criteria such as approximately to drainage areas for landfills/domestic and agricultural effluents. Results: Pearson correlation coefficient between physical, chemical and microbial parameters of water showed that fecal coliform, BOD, COD, nitrate, ammonium, phosphate, turbidity and total hardness had a significant positive relationship with each other at 99% confidence. According to this index, upstream samples of the river are classified as good, mid-stations relatively good to moderate category, and downstream samples of the river are classified as relatively bad. Conclusion: Based on the relationship between each variable, fecal coliform, BOD, COD, ammonium, and turbidity were more effective in determining the IRWQISC. Most of the examined variables showed low concentrations in upstream areas of the river while their concentration gradually increased along the river to downstream areas, especially close to urban and industrial districts.

Abstract Natural disasters threaten the sustainability of electric power supply. This fact highlights the importance of enhancing technological resourcefulness to handle the upcoming events. Once a natural disaster occurs, the most urgent undertaking is to rapidly pinpoint and assess component damages and dispatch the repair crews towards the most critical impaired elements. Practical efforts confirm that utilising a drone, which is an unmanned aerial vehicle, can notably reduce the duration of post‐disaster distribution system damage assessment (DA) and increase the resilience of power systems. This study presents an optimisation model to determine the optimal number and type of drones required for DA. The main goal is to minimise the time duration of the DA mission, which includes scanning the grid and identifying the damaged components. As a matter of fact, the financial resources are limited, so the problem is subject to the available budget constraint. Besides, the technical capacity of purchased drones for inspecting the grid is considered. The optimisation problem is formulated as a mixed‐integer programme with a guaranteed optimum solution. Case studies confirm the applicability of the model and the optimality of the results. As the major conclusion, the best allocation is a compromise between the speed and price of drones.

This prospective phase II clinical trial (Side Out 2) explored the clinical benefits of treatment selection informed by multi‐omic molecular profiling (MoMP) in refractory metastatic breast cancers (MBCs). Core needle biopsies were collected from 32 patients with MBC at trial enrollment. Patients had received an average of 3.94 previous lines of treatment in the metastatic setting before enrollment in this study. Samples underwent MoMP, including exome sequencing, RNA sequencing (RNA‐Seq), immunohistochemistry, and quantitative protein pathway activation mapping by Reverse Phase Protein Microarray (RPPA). Clinical benefit was assessed using the previously published growth modulation index (GMI) under the hypothesis that MoMP‐selected therapy would warrant further investigation for GMI ≥ 1.3 in ≥ 35% of the patients. Of the 32 patients enrolled, 29 received treatment based on their MoMP and 25 met the follow‐up criteria established by the trial protocol. Molecular information was delivered to the tumor board in a median time frame of 14 days (11–22 days), and targetable alterations for commercially available agents were found in 23/25 patients (92%). Of the 25 patients, 14 (56%) reached GMI ≥ 1.3. A high level of DNA topoisomerase I (TOPO1) led to the selection of irinotecan‐based treatments in 48% (12/25) of the patients. A pooled analysis suggested clinical benefit in patients with high TOPO1 expression receiving irinotecan‐based regimens (GMI ≥ 1.3 in 66.7% of cases). These results confirmed previous observations that MoMP increases the frequency of identifiable actionable alterations (92% of patients). The MoMP proposed allows the identification of biomarkers that are frequently expressed in MBCs and the evaluation of their role as predictors of response to commercially available agents. Lastly, this study confirmed the role of MoMP for informing treatment selection in refractory MBC patients: more than half of the enrolled patients reached a GMI ≥ 1.3 even after multiple lines of previous therapies for metastatic disease.

Abstract Having a robust and accurate grid synchronization unit for distributed generators (DGs) has been accentuated due to rapidly growing integration of inverter‐based DGs to the autonomous microgrids. Although most of the synchronization methods are based on the phased locked loops (PLL) technique, digital filters could result in more salient dynamic performance in non‐ideal sinusoidal situations. This paper presents a novel digital filter as a grid synchronization method that overcomes the flaws of conventional PLLs under harmonic distorted and unbalanced cases as well as large delay of window‐based digital filters. For the sake of computational advantages, the proposed method is executed in two distinct offline and online stages. In the offline stage, the second‐order Taylor series expansion approximation of the bandpass oscillatory signal calculates the state‐transition matrices and state vectors. Then, by applying the concept of Kalman filter, the state vectors are updated in the online calculation section much easier and faster compared to all‐online approach. Compared to the existing methods, the proposed method generates a dynamic response with less settling time, more accuracy, and immunity against harmonic distortion, imbalances, and noise.

Abstract Unexpected imbalances between supply and demand during real‐time operation in the wake of high penetration of renewable energy resources necessitate system operators to improve flexibility of the system. Moreover, increasing growth in flexible distributed resources installation in distribution networks with multiple MicroGrids (MGs) such as fuel‐based distributed generators, storage units, and controllable loads makes distribution system operators be able to manage energy imbalances locally through exploiting potentials of aforementioned resources in flexibility enhancement. This paper proposes a receding horizon data‐driven chance‐constrained energy flexibility trading framework for active distribution networks with multiple MGs. Specifically, a distance‐based confidence set is constructed in a data‐driven manner to apply net load uncertainty into this modelling. According to this proposed data‐driven approach, the energy flexibility is modelled using flexibility envelope notion to demonstrate its dynamics in different scenarios in an imminent horizon. Furthermore, MGs' flexibility trading problem is modelled as an exact potential game that possesses a pure strategy Nash Equilibrium (NE). An iterative decentralized algorithm based on alternating direction method of multipliers is developed to achieve the NE strategy of MGs. Numerical results are also represented to show the performance efficiency of the proposed energy flexibility trading framework.

Abstract The optimal controlled islanding of power systems is a practical solution to prevent system blackouts if the boundary of islands and corrective control actions in each island are carefully specified. This paper establishes a model for the controlled islanding problem by a proposed mixed integer linear program (MILP). The frequency‐arresting (FA) and frequency‐stabilising (FS) constraints are linearised and incorporated in our FA‐FS‐constrained MILP model to prevent triggering of load shedding (LS) relays. This achievement makes our model capable of handling low‐inertia networks. Intentional LS and stepwise generation curtailment are corrective actions accommodated for frequency control and power mismatch considerations. These corrective actions increase the degree of freedom and broaden the feasible space of the MILP model under the envisaged tough operating conditions. Our model's computational efficiency is improved using a proposed graph theory‐based network reduction technique. The basic groups of coherent generators, determined in the offline mode, are aggregated as equivalent buses using an extended Steiner tree method. A graph‐path determination technique is also proposed to generate disconnection constraints (between equivalent buses of incoherent areas) and bus‐allocation constraints. Simulation results on the IEEE 39‐bus test system and a 76‐bus case study verify the proposed network reduction technique's effectiveness and the MILP model.

Abstract Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

Abstract Background In real-world pragmatic administrative databases, patient reported remission is often missing. Objective We evaluate if, in administrative data, five features of antidepressant use patterns can replace patient-reported symptom remission. Method We re-examined data from Sequence Treatment Alternatives to Relieve Depression (STAR*D) study. Remission was measured using 50% reduction in Hamilton index. Pattern of antidepressant use was examined through five variables: (a) number of prior ineffective antidepressants, (b) duration of taking current antidepressant, (c) receiving therapeutic dose of the medication, and (d) switching to another medication, or (e) augmenting with another antidepressant. The likelihood ratio (LR) associated with each of these predictors was assessed in 90% of data (3329 cases) and evaluated in 10% of data (350 cases) set-aside for evaluation. The accuracy of predictions was calculated using Area under the Receiver Operating Curve (AROC). Results Patients who took antidepressants for 14 weeks (LR = 2.007) were more likely to have symptom remission. Prior use of 3 antidepressants reduced the odds of remission (LR = 0.771). Patients who received antidepressants below therapeutic dose were 5 times less likely to experience remission (LR = 0.204). Antidepressant that were augment or switched, almost never led to remission (LR = 0.008, LR = 0.002 respectively). Patterns of antidepressant use accurately (AROC = 0.93) predicted symptom remission. Conclusion Within the first 100 days, antidepressants use patterns could serve as a surrogate measure for patient-reported remission of symptoms.

Abstract High penetration of renewable distributed generations is one of the most current challenges of electric distribution networks due to resources uncertainties, and hosting capacity limitation of the networks. The challenges may be more critical when uncertainties are highly correlated. The effect of correlated uncertainties of wind speed and load on the distributed generation hosting capacity of distribution networks is evaluated. A combination of point estimation method, and inverse Nataf transformation is proposed for correlated uncertainties modelling. The efficiency of some ANM schemes on the hosting capacity improvement is also studied in the presence of correlated uncertainties. To do so, an optimisation framework with the objective of maximising the installed capacity of distributed generation subject to network operational constraints such as voltage deviation is proposed. The proposed optimisation problem is formulated in a mixed‐integer quadratically constrained program form, and is solved via CPLEX solver. The proposed method is implemented on the 33‐bus standard test system. The results demonstrate the significant effect of the correlation between uncertainties on the distributed generation hosting capacity of network.

Abstract Background Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history. Methods The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression. Results The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT. Conclusion Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.

Although synchrophasor measurement technology (SMT) has become a somewhat mature and operational tool used in transmission-level power networks, its use in active distribution networks (ADNs) is still in very early stages. Due to distinct features (such as unbalanced operation, very low phase angle differences, greater harmonics pollution, and lower signal-to-noise ratios in distribution networks), transmission-level devices and applications may not be fully effective at the distribution level. Extensive research and development efforts are in progress to bring distribution-level phasor measurement units (DPMUs) to an operational stage in ADNs. This essential work is made even more urgent by changing ADNs that are widely integrating distributed energy resources and enabling demand-side entities as active network players, in some cases considering authorizing them for peer-to-peer energy trading. This paper focuses on SMT/DPMU applications in ADNs and microgrids. We specifically elaborate on how SMT/DPMUs are enabling 1) effective situational awareness, 2) enhanced protection and fault location functions, 3) novel control algorithms and augmented energy management applications, and 4) long-term baselining and analysis. Various barriers and challenges that come with rapid and broad deployment of SMT/DPMUs and their associated applications are also discussed.

Background: Many algorithms exist for learning network structure and parameters from data. Some of these algorithms include Grow Shrink, Incremental Association Markov Blanket, IAMB, Fast IAMB, and Interleaved IAMB, Hill Climbing, Restricted Maximization and Maximum-Minimum Hill Climbing. These algorithms optimize the fit to the data, while ignoring the order of occurrences of the variables in the network structure. Objective: This paper examines if sequence information (i.e., one variable occurs before another) can make algorithms for learning directed acyclical graph networks more accurate. Methods: A 13- variable network was simulated, where information on sequence of occurrence of some of the variables was assumed to be known. In each simulation 10,000 observations were generated. These observations were used by 4 conditional dependency and 4 search and score algorithms to discover the network from the simulated data. Partial sequence was used to prohibit a directed arc from a later variable to an earlier one. The Area under the Receiver Operating Curve (AROC) was used to compare the accuracy of the sequence-constrained and unconstrained algorithms in predicting the last node in the network. In addition, we examined the performance of sequence constrained algorithms in a real data set. We analyzed 1.3 million disability assessments done on 296,051 residents in Veterans Affairs nursing homes; where the sequence of occurrence of variables was inferred from the average age of occurrence of disabilities. We constructed three networks using Grow-Shrink algorithm, one without and the other two use two permutation of the observed sequence. The fit of these three models to data was examined using Bayesian Information Criterion (BIC). Results: In simulated data, algorithms that used sequenced constraints (AROC = 0.94, confidence intervals, C.I. = 0.86 to 1) were significantly more accurate than the same algorithm without use of sequence constraints (AROC = 0.74, C.I. = 0.65 to 0.83). The agreement between discovered and observed networks improved from range of 0.54 to 0.97 to range of 0.88 to 1. In the real data set, the Bayesian network constructed with use of sequence had 6% lower BIC scores. Conclusions: Sequence information improved accuracy of all eight learning algorithms and should be routinely examined in learning network structure from data.

Abstract This paper proposes a novel algorithm for optimizing decision variables with respect to an outcome variable of interest in complex problems, such as those arising from big data. The proposed algorithm builds on the notion of Markov blankets in Bayesian networks to alleviate the computational challenges associated with optimization tasks in complex datasets. Through a case study, we apply the algorithm to optimize medication prescriptions for diabetic patients, who have different characteristics, suffer from multiple comorbidities, and take multiple medications concurrently. In particular, we demonstrate how the optimal combination of diabetic medications can be found by examining the comparative effectiveness of the medications among similar patients. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems. The procedure can be applied to any complex Bayesian network with numerous features, multiple decision variables, and a target variable.

Abstract Mechano-acoustic signals emanating from the heart and lungs contain valuable information about the cardiopulmonary system. Unobtrusive wearable sensors capable of monitoring these signals longitudinally can detect early pathological signatures and titrate care accordingly. Here, we present a wearable, hermetically-sealed high-precision vibration sensor that combines the characteristics of an accelerometer and a contact microphone to acquire wideband mechano-acoustic physiological signals, and enable simultaneous monitoring of multiple health factors associated with the cardiopulmonary system including heart and respiratory rate, heart sounds, lung sounds, and body motion and position of an individual. The encapsulated accelerometer contact microphone (ACM) utilizes nano-gap transducers to achieve extraordinary sensitivity in a wide bandwidth (DC-12 kHz) with high dynamic range. The sensors were used to obtain health factors of six control subjects with varying body mass index, and their feasibility in detection of weak mechano-acoustic signals such as pathological heart sounds and shallow breathing patterns is evaluated on patients with preexisting conditions.

This study uses two existing data sources to examine how patients' symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.

Bringing atomic beam technology to the chip scale is challenging due to the long distance required to filter the velocity distribution. Here, the authors report an engineering strategy for on-chip filtering of the velocity profile of atomic beams by fabricating planar, etched microchannel arrays.

Power systems are evolving to the networks with proliferated penetration of renewable energy resources to leverage their environmental and economic advantages. However, due to the stochastic nature of renewables, the management of the rapidly increasing uncertainty and variability in power system planning and operation is of crucial significance. This paper represents a comprehensive overview of power system flexibility as an effective way to maintain the power balance at every moment. Definitions of power system flexibility from various aspects are explained to reach the reliable and economic planning and operation of the power system. The effects of the high penetration of variable energy resources on power systems and the evolution of flexibility in response to renewables are studied. A variety of resources during the flexibility evolutionary transition are introduced and discussed. As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.

With the increasing interdependence of various energy carriers, the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures. This paper presents a mixed-integer linear programming (MILP) model for the microgrid (MG) optimal scheduling considering technical and economic ties between electricity and natural gas (NG) systems. In the proposed methodology, different energy converters and storages, including combined heat and power (CHP) units, electricity/heat storage units, and distributed energy resources (DERs) are considered. The proposed model allows the MG operator to minimize the operation cost of the MG while different operational limitations on the energy hub are satisfied. The model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints. The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG.

Purpose The veterans administration diabetes risk (VADR) cohort facilitates studies on temporal and geographic patterns of pre-diabetes and diabetes, as well as targeted studies of their predictors. The cohort provides an infrastructure for examination of novel individual and community-level risk factors for diabetes and their consequences among veterans. This cohort also establishes a baseline against which to assess the impact of national or regional strategies to prevent diabetes in veterans.Participants The VADR cohort includes all 6 082 018 veterans in the USA enrolled in the veteran administration (VA) for primary care who were diabetes-free as of 1 January 2008 and who had at least two diabetes-free visits to a VA primary care service at least 30 days apart within any 5-year period since 1 January 2003, or veterans subsequently enrolled and were diabetes-free at cohort entry through 31 December 2016. Cohort subjects were followed from the date of cohort entry until censure defined as date of incident diabetes, loss to follow-up of 2 years, death or until 31 December 2018.Findings to date The incidence rate of type 2 diabetes in this cohort of over 6 million veterans followed for a median of 5.5 years (over 35 million person-years (PY)) was 26 per 1000 PY. During the study period, 8.5% of the cohort were lost to follow-up and 17.7% died. Many demographic, comorbidity and other clinical variables were more prevalent among patients with incident diabetes.Future plans This cohort will be used to study community-level risk factors for diabetes, such as attributes of the food environment and neighbourhood socioeconomic status via geospatial linkage to residence address information.

Heavy metal wastes generated from mining activities are a major concern in developing countries such as Iran. Increasing concentrations of these metals in the soil make up a severe health hazard due to their non-degradability and toxicity. In this study, batch washing experiments were conducted in order to investigate the removal efficiency of zinc by biodegradable chelates, tartaric acid. For this purpose, soil samples were collected from the zinc contaminated soil in the region of the Angouran, Zanjan, Iran. Hence, optimization of batch washing conditions followed using a three-level central composite design approach based on the response surface methodology. The results demonstrated that the effects of pH, tartaric acid concentration, and interaction between selective factors on the zinc removal efficiency were all positive and significant (P

This study proposes a single-leader-multi follower game to model a bilevel retail market among an aggregator and multiple microgrids to determine the optimal demand scheduling of the consumer, as well as price-power bidding strategies of microgrids in an interactive scheme. In the lower level, microgrids which include several distributed energy resources and energy storage units, compete with each other and offer the optimal energy-price bids such that their individual profit is maximised, while energy dispatch among their energy resources is also determined. Then, in the upper-level problem, the aggregator leads the competition taking advantages of demand-side management including interruptible and shiftable loads to minimise its energy payment for real-time pricing of generation units. By means of Karush–Kuhn–Tucker optimality condition, the bilevel optimisation of Stackelberg game is reduced to a single-level mixed-integer linear programming problem. Moreover, impact of microgrids’ integration on the retail market clearance mechanism, as well as required incentives for such integration has been discussed in a separate scenario.