Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms

In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-choice maximality, i.e., assigning a maximal number of agents their top items, is often identified as an important efficiency criterion and measure of agents' satisfaction. In this paper, we propose a natural and intuitive efficiency property, favoring-eagerness-for-remaining-items (FERI), which requires that each item is allocated to an agent who ranks it highest among remaining items, thereby implying first-choice maximality. Using FERI as a heuristic, we design mechanisms that satisfy ex-post or ex-ante variants of FERI together with combinations of other desirable properties of efficiency (Pareto-efficiency), fairness (strong equal treatment of equals and sd-weak-envy-freeness), and strategyproofness (sd-weak-strategyproofness). We also explore the limits of FERI mechanisms in providing stronger efficiency, fairness, or strategyproofness guarantees through impossibility results

Multi-type Resource Allocation with Partial Preferences

We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well known PS and RP mechanisms to the multi-type resource allocation problem (MTRA) with partial preferences. In our setting, there are multiple types of divisible items, and a group of agents who have partial order preferences over bundles consisting of one item of each type. We show that for the unrestricted domain of partial order preferences, no mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies ex-post-efficiency, sd-strategy-proofness, and upper invariance, recovering the properties of PS and RP

Emergence of central recirculation zone in a V-shaped premixed swirling flame

This paper presents an experimental study on the emergence of the central recirculation zone (CRZ) in a V-shaped premixed swirling flame, using simultaneous measurement of particle image velocimetry (PIV) and CH* chemiluminescence. The results show that either increasing the Reynolds number (Re) or decreasing the equivalence ratio ({\phi}) would facilitate the emergence of CRZ, and the inner shear layer (ISL) plays an essential role in governing the characteristics of CRZ. Further analysis demonstrates that the CRZ emergence can be promoted by higher ISL intensity but suppressed by enhanced viscous diffusion owing to higher flame temperature. As such, the CRZ formation can be interpreted as the outcome of a competition between the ISL intensity, i.e., circulation, and the vorticity consumption due to viscous diffusion. This competition physically corresponds to a special Reynolds number, Re_s, defined as the ratio between the ISL circulation ({\Gamma}) and the ISL effective viscosity ({\nu}_s), with a simplified heat loss model proposed for the temperature and viscosity estimations of the ISL. The outputting {\Gamma}-{\nu}_s plot yields a single boundary line separating the cases with and without CRZ, which points to a common critical Re_s of about 637, justifying the generality of the present criterion for lean-premixed V-shaped swirling flames of various operating conditions. Unlike most previous works which study the CRZ of a swirling flame from the point of vortex breakdown, the present work reveals the importance of enhanced viscous diffusion, caused by flame heating, in suppressing the CRZ emergence

Descending motor circuitry required for NT-3 mediated locomotor recovery after spinal cord injury in mice

Locomotor function, mediated by lumbar neural circuitry, is modulated by descending spinal pathways. Spinal cord injury (SCI) interrupts descending projections and denervates lumbar motor neurons (MNs). We previously reported that retrogradely transported neurotrophin-3 (NT-3) to lumbar MNs attenuated SCI-induced lumbar MN dendritic atrophy and enabled functional recovery after a rostral thoracic contusion. Here we functionally dissected the role of descending neural pathways in response to NT-3-mediated recovery after a T9 contusive SCI in mice. We find that residual projections to lumbar MNs are required to produce leg movements after SCI. Next, we show that the spared descending propriospinal pathway, rather than other pathways (including the corticospinal, rubrospinal, serotonergic, and dopaminergic pathways), accounts for NT-3-enhanced recovery. Lastly, we show that NT-3 induced propriospino-MN circuit reorganization after the T9 contusion via promotion of dendritic regrowth rather than prevention of dendritic atrophy

Development of a Quartz Crystal Microbalance Biosensor with Aptamers as Bio-recognition Element

The ultimate goal in any biosensor development project is its use for actual sample detection. Recently, there has been an interest in biosensors with aptamers as bio-recognition elements, but reported examples all deal with standards, not human serum. In order to verify the differences of aptamer-based biosensor and antibody-based biosensor in clinical detection, a comparison of the performance of aptamer-based and antibody-based quartz crystal microbalance (QCM) biosensors for the detection of immunoglobulin E (IgE) in human serum was carried out. Aptamers (or antibodies) specific to IgE were immobilized on the gold surface of a quartz crystal. The frequency shifts of the QCM were measured. The linear range with the antibody (10–240 μg/L) compared to that of the aptamer (2.5–200 μg/L), but a lower detection limit could be observed in the aptamer-based biosensor. The reproducibility of the two biosensors was comparable. The aptamers were equivalent or superior to antibodies in terms of specificity and sensitivity. In addition, the aptamer receptors could tolerate repeated affine layer regeneration after ligand binding and recycling of the biosensor with little loss of sensitivity. When stored for three weeks, the frequency shifts of the aptamer-coated crystals were all greater than 90% of those on the response at the first day

Trading Off Voting Axioms for Privacy

In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet consistency, and Condorcet loser criterion. We provide upper and lower bounds on the two-way tradeoffs between DP and each axiom. We also provide upper and lower bounds on three-way tradeoffs among DP and every pairwise combination of all the axioms, showing that, while the axioms are compatible without DP, their upper bounds cannot be achieved simultaneously under DP. Our results illustrate the effect of DP on the satisfaction and compatibility of voting axioms

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor

An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points

With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm

A Unified Active Frequency Regulating and Maximum Power Point Tracking Strategy for Photovoltaic Sources

In order to optimize the extraction of solar energy, photovoltaic sources are commonly operated under the control of the so-called maximum power point (MPPT) strategy. However, as the rate of PV installations increases explosively, traditional MPPT algorithms may cause problems such as frequency deviation and power fluctuations, making system frequency stability a challenge due to the inherent intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control is expected for PV sources to provide ancillary services for the system, especially for weak systems such as microgrids. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. In conjunction, an f–P droop mechanism is designed to provide a frequency regulating (FR) service for the AC microgrid. The comprehensive control strategy unifies the FR function with the traditional MPPT function in a single control structure, allowing the PV source to operate either in the MPPT mode when the system frequency is nominal or in FR mode when the frequency exceeds it. The transition between MPPT and FR is autonomous and fully decentralized, which improves the PV generation efficiency as well as ensuring generation fairness among different parallel PV sources. Importantly, the proposed control strategy does not require any internal bundled energy within the PV generation system to achieve FR capability, but it effectively collaborates with the system-level energy storage system, thus reducing the necessary battery capacity. A detailed dynamic model of a PV generation system is constructed to validate the feasibility and effectiveness of the proposed control strategy