Distribution Of Activity Research Articles

Insect frass as a promising remedy of increasing the fertility of agricultural land in Siberia

One of the main tasks of modern organic crop production is the search for effective environmentally friendly fertilizers. A promising approach to this problem could be the study insect frass, a product of organic processing by insects. Insect frass is obtained as secondary products from the cultivation of such economic insects as, for example, housefly (Musca domestica) and black soldier fly (Hermetia illucens). Insect frass has a high concentration of nitrogen, which was processed within the digestive tract of insects, which makes it more accessible to plants and contributes to their active distribution and development. It also has insecticidal and fungicidal properties due to its own microbiome. Insect frass can also enhance the moisture-holding capacity of the environment and be used as sorbents for petroleum products. The actual study examines the use of insect frass as a stand-alone growing medium or as one component of a finished substrate. A study was carried out and the nitrogen content in ammonium and nitrate forms, total acidity and the phytotoxicity index of insect frass in mixtures with lowland peat were measured. The study showed that insect frass has acceptable acidity for use as a substrate, as well as a high nitrogen content in two forms. At high concentrations, insect frass is a source of phytotoxic properties, therefore, when using it, it is necessary to maintain the ratio with other components of the substrate. Thus, insect frass, with normal application, can become an effective fertilizer for all household producers engaged in organic farming, where the tools for increasing productivity are seriously limited, and all useful elements taken from the soil must be replenished without the use of chemical fertilizers.

Earth-Abundant CuWO4 as a Versatile Platform for Photoelectrochemical Valorization of Soluble Biomass Under Benign Conditions.

Here, a nanosheet-structured CuWO4 (nanoCuWO4) is demonstrated as a selective and stable photoanode for biomass valorization in neutral and near-neutral solutions. nanoCuWO4 can be readily prepared by a solid phase reaction using nanosheet-structured WO3 as the template. Several substrates, including glucose, fructose and glycerol, are investigated to reveal the wide applicability of nanoCuWO4. The activity and product distribution trends in biomass valorization are investigated at different pH by taking advantage of the promising stability of nanoCuWO4 in a wide pH range. Product analyses confirm that formate production with a Faradaic efficiency (FE) of 76 ± 5% and glycolate production with an FE of 61 ± 8% can be achieved by glucose and fructose valorization at pH 10.2, respectively. On the other hand, glyceraldehyde and dihydroxyacetone (DHA)are the main products in the glycerol valorization, accounting for an FE of over 85% at pH 7. Notably, nanoCuWO4 has the highest FE of DHA in photoelectrochemical (PEC)glycerol valorization compared to WO3 and BiVO4 at neutral pH. The oxidation routes and mechanism of glycerol valorization on nanoCuWO4 are also under investigation. The co-production of H2 and value-added chemicals is finally demonstrated using a photovoltaic cell connected to a nanoCuWO4-based PEC glycerol valorization system.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method

Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method

Business process simulation: Probabilistic modeling of intermittent resource availability and multitasking behavior

In business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday–Friday, 9:00–18:00. Resources are assumed to be always available during each time slot in their availability calendar. This assumption often becomes invalid due to interruptions, breaks, or time-sharing across processes. In other words, existing approaches fail to capture intermittent availability. Another limitation of existing approaches is that they either do not consider multitasking behavior, or if they do, they assume that resources always multitask (up to a maximum capacity) whenever available. However, studies have shown that the multitasking patterns vary across days. This paper introduces a probabilistic approach to model resource availability and multitasking behavior for business process simulation. In this approach, each time slot in a resource calendar has an associated availability probability and a multitasking probability per multitasking level. For example, a resource may be available on Fridays between 14:00–15:00 with 90% probability, and given that they are performing one task during this slot, they may take on a second concurrent task with 60% probability. We propose algorithms to discover probabilistic calendars and probabilistic multitasking capacities from event logs. An evaluation shows that, with these enhancements, simulation models discovered from event logs better replicate the distribution of activities and cycle times, relative to approaches with crisp calendars and monotasking assumptions.

Two-Stage Optimization Model Based on Neo4j-Dueling Deep Q Network

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

Self-supervised hybrid neural network to achieve quantitative bioluminescence tomography for cancer research

Bioluminescence tomography (BLT) improves upon commonly-used 2D bioluminescence imaging by reconstructing 3D distributions of bioluminescence activity within biological tissue, allowing tumor localization and volume estimation—critical for cancer therapy development. Conventional model-based BLT is computationally challenging due to the ill-posed nature of the problem and data noise. We introduce a self-supervised hybrid neural network (SHyNN) that integrates the strengths of both conventional model-based methods and machine learning (ML) techniques to address these challenges. The network structure and converging path of SHyNN are designed to mitigate the effects of ill-posedness for achieving accurate and robust solutions. Through simulated and in vivo data on different disease sites, it is demonstrated to outperform the conventional reconstruction approach, particularly under high noise, in tumor localization, volume estimation, and multi-tumor differentiation, highlighting the potential towards quantitative BLT for cancer research.

Determining the chemical ordering in nanoalloys by considering atomic coordination types.

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.

Corrigendum: Power management of harmonic polluted active distribution network in the presence of integrated unit of electric spring and electric vehicles parking lot

Corrigendum: Power management of harmonic polluted active distribution network in the presence of integrated unit of electric spring and electric vehicles parking lot

Rising global temperatures and its impact on sleep behavior of male redheaded bunting (Emberiza bruniceps).

Anthropogenic global warming is one of the most pervasive threats to nature and biodiversity. The magnitude with which earths' temperature is rising is affecting every lifeform uniquely; however, the studies highlighting the impacts of global warming on avian sleep are scarce. To this end, the present study was aimed at analyzing the impact of global warming on sleep behavior of a nocturnal migrant, Emberiza bruniceps. For this purpose, the birds were divided into two groups (N = 15 each), subjected to high (35 ± 1°C) and low (19 ± 1°C) temperature schedule with concurrent exposure to 8L:16D (short day; SD) photoperiod followed by 13L:11D (long day; LD). The experiment continued till 7 cycles of zugunruhe (LD) in birds. The results reveal significant impact of temperature treatment on initiation and quality of zugunruhe. Temporal distribution of activity and rest varied according to the temperature provided. Focusing on rest and specifically on sleep of birds, high ambient temperatures resulted in greater sleep fragmentation (evident by increased awakenings during night), whereas low temperature created a sleep conducive environment (evident by abundance of back sleep). Besides postural differences, high temperature resulted in reduced sleep duration, sleep onset latency and circulating plasma melatonin levels in comparison with low temperature suggesting the negative impact of high temperature on different sleep attributes. Not only sleep, seasonal physiology of birds such as hyperphagia, gain in body mass, and fat stores showed significant reduction in high temperature condition. Besides behavioral and physiological alterations, high ambient temperature led to elevated expression of temperature sensitive (trpv4, trpm8, hspa8, and hsp70) genes. Enhanced expression of chrm3 (responsible for wakefulness) also affirms sleep fragmentation in response to high temperature. Thus, the study highlights the negative impact of high temperature on birds' sleep behavior and seasonal physiology.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Alterations in magnitude and spatial distribution of erector spinae muscle activity in cyclists with a recent history of low back pain.

While cycling offers several health benefits, repetitive loading and maintenance of static postures for prolonged periods expose cyclists to low back pain (LBP). Despite high LBP prevalence in cyclists, underlying pathomechanics and specific lumbar region muscle activation patterns during cycling are unclear. Here, we compared lumbar erector spinae (ES) muscles activation and spatial distribution activity in cyclists with and without recent LBP history. Ten cyclists with recent LBP history (LBPG; Oswestry Disability Index score ~ 17.8%) and 11 healthy cyclists (CG) were recruited. After assessing the Functional Threshold Power (FTP), participants underwent an incremental cycling test with 4 × 3min steps at 70%, 80%, 90%, and 100% of their FTP. High-density surface electromyography (HDsEMG) signals were recorded from both lumbar ES using two 64-channel grids. Information about ES activation levels (root-mean-square, RMS), degree of homogeneity (entropy), and cranio-caudal displacement of muscle activity (Y-axis coordinate of the barycenter of RMS maps) was extracted from each grid separately and then grand-averaged across both grids. Repeated-measure 2-way ANOVAs showed a significant intensity by group interaction for RMS amplitude (p = 0.003), entropy (p = 0.038), and Y-bar displacement (p = 0.033). LBPG increased RMS amplitude between 70-100% (+ 19%, p = 0.010) and 80-100% FTP (+ 21%, p = 0.004) and decreased entropy between 70-100% FTP (-8.4%, p = 0.003) and 80-100% FTP (-8.5%, p = 0.002). Between-group differences emerged only at 100% FTP (+ 9.6%, p = 0.049) for RMS amplitude. Our findings suggest that cyclists with recent LBP history exhibit higher ES muscles activation and less homogeneous activity compared to healthy controls, suggesting potential inefficient muscle recruitment strategy. HEC-DSB/09-2023.

An enhanced algorithm for detection of HIAF in active distribution networks and real-time analysis

Identifying a downed conductor in distribution is challenging due to the insignificant variations in the current signal. Most of the distribution system faults are identified using overcurrent and earth fault protection. Fault path resistance decides the current magnitude while high resistance path may create timely issues in identifying a fault. Downed conductor and high resistance faults create arc in between the ground and conductor. Due to this the fault at the location is highly nonlinear with reduced magnitude. These faults are known as high impedance arcing fault (HIAF). Such faults can be identified effectively by conducting harmonic analysis of the signals. But in a modern distribution system apart from other renewable sources, the integration of electric vehicle (EV) may create HIAF detection challenges due to the inclusion of inverters and switching harmonics associated with EV. Considering the available advanced architecture of modern microgrid system, an effective solution is proposed in this work to securely detect HIAFs. The method is robust and highly reliable for HIAFs. The system stability and continuity can be unaffected with the help of the proposed method in case of various non-fault or transient/switching conditions. Comparison with other profound techniques is done to show the superiority of the proposed method.

A two-layer economic resilience model for distribution network restoration after natural disasters

Improving the resilience of energy networks and minimizing outages is crucial, making the development of self-healing strategies essential for ensuring a continuous power supply and boosting resilience. As decentralized prosumers become more integrated into smart grids, innovative coordination methods are required to fully leverage their potential and improve the system's self-healing capabilities. Hence, in this paper, we present a decentralized two-layer model for active distribution networks (ADNs) incorporating commercial energy hubs, Parking lots (PLs), hydrogen refueling stations, and mobile units (MUs). This model aims to maximize the flexible capacities of flexible prosumers under emergencies and includes a dynamic mechanism for transporting hydrogen to refueling stations via trucks. In the first layer of the model, the AND operator provides the required services to flexible prosumers, including commercial energy hubs, PLs and hydrogen refueling stations. During this layer, the movement paths of mobile units, such as battery-based mobile storage units (MSUs) and mobile hydrogen units (MHUs), are determined. In the second layer, prosumers independently plan and decide the extent of their service contributions. Subsequently, with the actual service capacities established, the AND operator re-optimizes the initial planning to finalize the system schedule and precise movement paths of the mobile units. The proposed model was tested on a modified 83-bus distribution network using the CPLEX solver in GAMS. Simulation results demonstrate a 54.9 % reduction in forced load shedding (FLS) due to the dispatch of mobile units and a further 84.4 %% reduction in FLS from leveraging the flexible capacities of commercial energy hubs and PLs.

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Role of the Kölliker-Fuse/Parabrachial Complex in generation of post-inspiratory vagal and sympathetic nerve activities.

In the rat, the activity of laryngeal adductor muscles, the crural diaphragm, and sympathetic vasomotor neurons is entrained to the post-inspiratory (post-I) phase of the respiratory cycle, a mechanism thought to enhance cardiorespiratory efficiency. The identity of the central neurons responsible for transmitting respiratory activity to these outputs remains unresolved. Here we explore the contribution of the Kölliker-Fuse/Parabrachial nuclei (KF-PBN) in the generation of post-I activity in vagal and sympathetic outputs under steady-state conditions and during acute hypoxemia, a condition that potently recruits post-I activity. In artificially ventilated, vagotomised and urethane-anesthetised rats, bilateral KF-PBN inhibition by microinjection of the GABAA receptor agonist isoguvacine evoked stereotypical responses on respiratory pattern, characterised by a reduction in phrenic nerve burst amplitude, a modest lengthening of inspiratory time, and an increase in breath-to-breath variability, while post-I vagal nerve activity was abolished and post-I sympathetic nerve activity diminished. During acute hypoxemia, KF-PBN inhibition attenuated tachypnoeic responses and completely abolished post-I vagal activity while preserving respiratory-sympathetic coupling. Furthermore, KF-PBN inhibition disrupted the decline in respiratory frequency that normally follows resumption of oxygenation. These findings suggest that the KF-PBN is a critical hub for the distribution of post-I activities to vagal and sympathetic outputs and is an important contributor to the dynamic adjustments to respiratory patterns that occur in response to acute hypoxia. While KF-PBN appears essential for post-I vagal activity, it only partially contributes to post-I sympathetic nerve activity, suggesting the contribution of multiple neural pathways to respiratory-sympathetic coupling.

Single-Molecule Analysis of Alkaline Phosphatase

Chemical studies usually consist of measurements made on large ensembles of molecules with data representing average values for the population. It has been shown that individual molecules of a given enzyme have different properties. Large-scale averaging has in the past masked these differences. Alkaline phosphatase has been used as a model to study this enzyme heterogeneity. The catalytic rates of the individual molecules have been found to differ by over 10-fold, and the activation energy of catalysis by more than two-fold. Differences in properties indicate that differences in structure must exist between the molecules. For alkaline phosphatase, the structural differences have been suggested to be differences in glycosylation, differences due to partial proteolysis, and due to some molecules containing mixtures of active and inactive subunits. The determination of the distribution of activities of populations of this enzyme within a sample has also been shown to be a useful tool in diagnostics. This review discusses the advent of single-molecule enzymology and summarizes its use in the study of alkaline phosphatase using capillary electrophoresis, microscopic well assays, and single-molecule tracking.