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Bureaucracies often post staff to better or worse locations, ostensibly to provide incentives. Yet we know little about whether this works, with heterogeneity in preferences over postings impacting effectiveness. We propose a performance-ranked serial dictatorship mechanism, whereby bureaucrats sequentially choose desired locations in order of performance. We evaluate this using a two-year field experiment with 525 property tax inspectors in Pakistan. The mechanism increases annual tax revenue growth by 30–41 percent. Inspectors whom our model predicts face high equilibrium incentives under the scheme indeed increase performance more. Our results highlight the potential of periodic merit-based postings in enhancing bureaucratic performance.

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.

With the development of distributed generation and the corresponding importance of the P.V. (photovoltaic) system, it is desired to operate a P.V. system efficiently and reliably. To ensure such an operation, a monitoring system is required to diagnose the health of the system. This paper aims to analyze a P.V. system under various operating conditions to identify parameters–derived from the I-V (current-voltage) characteristics of the P.V. system–that could serve as electrical signatures to various faulty operations and facilitate in devising a monitoring algorithm for the system. A model-based approach has been adopted to represent a P.V. system, using a one-diode model of a practical P.V. cell, developed in MATLAB/Simulink. The modelled system comprises two arrays, while each array has two panels in series. It was simulated for various operating conditions: healthy condition represented by STC (Standard Testing Condition), O.C. (open-circuited), soiling, P.S. (partial-shading), H.S. (panels hotspots) and P.D. (panels degradation) conditions. For the analysis of I-V curves under these conditions, six derived parameters were selected: Vte (equivalent thermal voltage), MCPF (maximum current point factor), Ri (currents ratio), S (slope), and Dv and Di (voltages and currents differences, respectively). Using these parameters, data of the actual system under various conditions were compared with its model-generated data for healthy operating conditions. Thresholds were set for each parameter’s value to mark normal operation range. It was observed that almost each considered fault creates a unique combination of sensitive parameters whose values exceeds the pre-defined thresholds, creating an electrical signature that will appear only when the corresponding conditions on the system are achieved. Based on these signatures, an algorithm has been proposed in this study which aims to identify and classify the considered faults. In comparison to other such studies, this work has been focused on those sensitive parameters for faults identification which shows greater sensitivity and contribute more to creation of unique sets of sensitive parameters for considered faults.

Mathematical models can be very useful in determining efficient and successful antibiotic dosing regimens. In this study, we consider the problem of determining optimal antibiotic dosing when bacteria resistant to antibiotics are present in addition to susceptible bacteria. We consider two different models of resistance acquisition, both involve the horizontal transfer (HGT) of resistant genes from a resistant to a susceptible strain. Modeling studies on HGT and study of optimal antibiotic dosing protocols in the literature, have been mostly focused on transfer of resistant genes via conjugation, with few studies on HGT via transformation. We propose a deterministic ODE based model of resistance acquisition via transformation, followed by a model that takes into account resistance acquisition through conjugation. Using a numerical optimization algorithm to determine the ‘best’ antibiotic dosing strategy. To illustrate our optimization method, we first consider optimal dosing when all the bacteria are susceptible to the antibiotic. We then consider the case where resistant strains are present. We note that constant periodic dosing may not always succeed in eradicating the bacteria while an optimal dosing protocol is successful. We determine the optimal dosing strategy in two different scenarios: one where the total bacterial population is to be minimized, and the next where we want to minimize the bacterial population at the end of the dosing period. We observe that the optimal strategy in the first case involves high initial dosing with dose tapering as time goes on, while in the second case, the optimal dosing strategy is to increase the dosing at the beginning of the dose cycles followed by a possible dose tapering. As a follow up study we intend to look at models where ‘persistent’ bacteria may be present in additional to resistant and susceptible strain and determine the optimal dosing protocols in this case.

The data on infective endocarditis after transcatheter aortic valve implantation (TAVI) is scarce and limited to case reports and case series in the literature. It is the need of the hour to analyze the available data on post-TAVI infective endocarditis from the available literature. The objectives of this systematic review were to evaluate the incidence of infective endocarditis after transcatheter aortic valve implantation, its microbiological profile and clinical outcomes. It will help us to improve the antibiotic prophylaxis strategies and treatment options for infective endocarditis in the context of TAVI. EMBASE, Medline and the CENTRAL trials registry of the Cochrane Collaboration were searched for articles on infective endocarditis in post-TAVI patients till October 2018. Eleven articles were included in the systematic review. The outcomes assessed werethe incidence of infective endocarditis, its microbiological profile andclinical outcomes including major adverse cardiac event (MACE), net adverse clinical event (NACE), surgical intervention and valve-in-valve procedure. The incidence of infective endocarditis varied from 0%-14.3% in the included studies, the mean was3.25%. The average duration of follow-up was 474 days (1.3 years). Enterococci were the most common causative organism isolated from 25.9% of cases followed by Staphylococcus aureus (16.1%) and coagulase-negative Staphylococcus species (14.7%). The mean in-hospital mortality and mortality at follow-up was 29.5% and 29.9%, respectively. The cumulative incidence of heart failure, stroke and major bleeding were 37.1%, 5.3% and 11.3%,respectively. Only a single study by Martinez-Selles et al. reported arrhythmias in 20% cases. The septic shock occurred in 10% and 27.7% post-TAVI infective endocarditis patients according to 2 studies. The surgical intervention and valve-in-valve procedure were reported in 11.4% and 6.4% cases, respectively. The incidence of post-TAVI infective endocarditis is low being 3.25% but it is associated with high mortality and complications. The most common complication is heart failure with a cumulative incidence of 37.1%. Enterococciare the most common causative organism isolated from 25.9% of cases followed by Staphylococcus aureus in 16.1% of cases. Appropriate measures should be taken to prevent infective endocarditis in post-TAVI patients including adequate antibiotics prophylaxis directed specifically against these organisms. PROSPERO registration number CRD42018115943.

Specific B-cell subsets can regulate T-cell immune responses, and are termed regulatory B cells (Breg). The majority of Breg cells described in mouse and man have been identified by IL-10 production and are known to suppress allergy and autoimmunity. However, Breg cell mediated immune suppression, independent of IL-10, also occurs. Here we show that Breg cells play a critical role in regulating humoral immunity mediated by CD4 + CXCR5 + PD-1 + follicular helper T cells, and can suppress inflammation in autoimmune disease through elevated expression of PD-L1. We have also identified that these B cells are resistant to αCD20 B-cell depletion. This work describes how Breg cells are critical in humoral homoeostasis and may have implications for the regulation of autoimmune diseases. Follicular helper T cells promote antibody production by B cells, and regulatory B cells, in turn, can restrain T cell activation. Here, Khan et al . show that PD-L1 plays a critical role in regulatory B cell function, curbing excessive immune responses by engaging the PD-1 receptor on follicular helper T cells.

Performance pay for tax collectors has the potential to raise revenues, but might come at a cost if it increases the bargaining power of tax collectors vis-à-vis taxpayers. We report the first large-scale field experiment on these issues, where we experimentally allocated 482 property tax units in Punjab, Pakistan, into one of three performance pay schemes or a control. After two years, incentivized units had 9.4 log points higher revenue than controls, which translates to a 46% higher growth rate. The scheme that rewarded purely on revenue did best, increasing revenue by 12.9 log points (64% higher growth rate), with little penalty for customer satisfaction and assessment accuracy compared to the two other schemes that explicitly also rewarded these dimensions. The revenue gains accrue from a small number of properties becoming taxed at their true value, which is substantially more than they had been taxed at previously. The majority of properties in incentivized areas in fact pay no more taxes, but instead report higher bribes. The results are consistent with a collusive setting in which performance pay increases collectors’ bargaining power over taxpayers, who have to either pay higher bribes to avoid being reassessed or pay substantially higher taxes if collusion breaks down.

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hindered by high computational costs, particularly due to the need for fine spatial and temporal discretization, complex boundary conditions, and the resolution of non-linearities inherent in the governing equations. In this study, we investigate the use of Physics-Informed Neural Networks (PINNs) to solve 1D and 2D SWEs in dam-break scenarios. The proposed PINN framework incorporates the governing partial differential equations along with the initial and boundary conditions directly within the training process of the network, ensuring physically consistent solutions. We conduct a systematic comparison of the solutions of SWE using the classical numerical scheme (Lax-Wendroff) with estimates of physics informed neural networks. For 1D SWE, a neural network is trained and validated on a dam-break problem, revealing that physics-informed models produce smoother but still acceptable estimates of wave propagation compared to standard numerical results. For 2D SWE, we consider various configurations of dam geometries along with varying initial profiles for water heights. Across all scenarios, reproduce the numerical baselines, albeit with limited accuracy, while avoiding spurious oscillations and numerical artifacts. Further tuning, achieved by incorporating numerical solutions into the PINN training, improved accuracy. This proof of concept demonstrates the potential of hybridized PINNs as a mesh-free, scalable, and generalizable framework for approximating solutions to nonlinear hyperbolic systems. Our results indicate that pre-trained, physics-informed models could serve as a viable alternative for real-time flood forecasting in complex domains.

Mastitis, the inflammatory condition of mammary glands, has been closely associated with immune suppression and imbalances between antioxidants and free radicals in cattle. During the periparturient period, dairy cows experience negative energy balance (NEB) due to metabolic stress, leading to elevated oxidative stress and compromised immunity. The resulting abnormal regulation of reactive oxygen species (ROS) and reactive nitrogen species (RNS), along with increased non-esterified fatty acids (NEFA) and β-hydroxybutyric acid (BHBA) are the key factors associated with suppressed immunity thereby increases susceptibility of dairy cattle to infections, including mastitis. Metabolic diseases such as ketosis and hypocalcemia indirectly contribute to mastitis vulnerability, exacerbated by compromised immune function and exposure to physical injuries. Oxidative stress, arising from disrupted balance between ROS generation and antioxidant availability during pregnancy and calving, further contributes to mastitis susceptibility. Metabolic stress, marked by excessive lipid mobilization, exacerbates immune depression and oxidative stress. These factors collectively compromise animal health, productive efficiency, and udder health during periparturient phases. Numerous studies have investigated nutrition-based strategies to counter these challenges. Specifically, amino acids, trace minerals, and vitamins have emerged as crucial contributors to udder health. This review comprehensively examines their roles in promoting udder health during the periparturient phase. Trace minerals like copper, selenium, and calcium, as well as vitamins; have demonstrated significant impacts on immune regulation and antioxidant defense. Vitamin B12 and vitamin E have shown promise in improving metabolic function and reducing oxidative stress followed by enhanced immunity. Additionally, amino acids play a pivotal role in maintaining cellular oxidative balance through their involvement in vital biosynthesis pathways. In conclusion, addressing periparturient mastitis requires a holistic understanding of the interplay between metabolic stress, immune regulation, and oxidative balance. The supplementation of essential amino acids, trace minerals, and vitamins emerges as a promising avenue to enhance udder health and overall productivity during this critical phase. This comprehensive review underscores the potential of nutritional interventions in mitigating periparturient bovine mastitis and lays the foundation for future research in this domain.