MAPM: MULTI-MODAL AUTO-AGILE PROJECT RISK MANAGEMENT AND PREDICTION FOR COLLABORATION PLATFORM USING AI/ML

Agile software development tools have been created to assist project management and enhance productivity. However, it may be challenging to properly employ those tools, especially in a hybrid work environment. Techniques are presented herein, which may be referred to herein as a MaPM system, that leverage a conferencing platform to offer a real-time agile project risk management and prediction framework that utilizes multi-modal collaboration data sources. Aspects of the presented techniques encompass an artificial intelligence (AI)-backed summarization model that may be utilized to extract project details and an auto-agile that model may consume those loggings and generate the predicted project sprint backlogs and their risks. Further aspects of the presented techniques support an optimization module that may jointly update the predicted sprint backlogs and the estimated task risks to realize the finalized backlog sequences. In summary, an MaPM system, according to the presented techniques, offers four novelties compared with conventional agile project management tools – a conferencing platform-centralized solution for automatic agile project risk prediction and management, a real-time multi-modal-based project risk monitoring and prediction system, the generation of sprint backlogs based on fully evaluated contexts that are collected from all of a project’s participants, and the liberation of project contributors from having to conduct manual project tracking and recording

Study on Preventive Maintenance Strategies of Filling Equipment Based on Reliability-Cantered Maintenance

In order to ensure normal operation of enterprise production activities and enhance the competitiveness of enterprise, equipment management and maintenance strategy formulation has always been one of important contents of daily management of enterprise. According to the actual requirement of a Chinese beer production enterprise, preventive maintenance strategy of filling equipment is put forward based on reliability-centred maintenance (RCM). Firstly, on the basis of analyzing RCM theory and equipment maintenance, the general process of failure analysis of beer production equipment is presented. Secondly, the general production process of bottled beer is analyzed, and the composition of major filling equipment is also introduced in the beer production line. With the help of key indicators of equipment reliability, such as mean time between failure (MTBF), mean time to repair restoration (MTTR) and availability Ai, the fault analysis of filling production line is carried out, and the relevant results are calculated. Then, process failure mode and effect analysis (PFMEA) of filling machine is implemented, and fault tree analysis (FTA) of potential failure modes with high risk priority numbers is also completed. Finally, preventive and maintenance strategies of filling equipment are established on the basis of RCM. Through the research in this paper, maintenance costs and unplanned breakdown hours can be significantly reduced

CROSS CUSTOMERS SMART NETWORK INVENTORY PLANNER (SNIP) AND OPTIMIZATIONS USING DEEP REINFORCEMENT LEARNING

Optimal inventory upgrade planning is one of the most challenging tasks in managing network assets. A Smart Network Inventory Planning (SNIP) architecture or framework is presented herein that leverages a deep reinforcement learning (DRL) framework to enable network inventory upgrade planning for different scenarios. As a foundation for the DRL framework, techniques herein provide for establishing a network inventory environment through which interaction with a supply chain can be used to allow the SNIP architecture to incrementally optimize upgrading sequences for multiple customers. To further optimize inventory upgrades via the DRL framework, the SNIP architecture may employ a multi-objective reward function. Additionally, a transformer can be utilized as a policy network to capture long-term correlations in the inventory upgrading sequence. By incorporating weighting coefficients into both the reward function and a multi-agent actor network, the SNIP architecture can provide customized inventory task scheduling within an optimal framework

DEEPSORTING: CUSTOMER-CENTRIC SEQUENCE-TO-SEQUENCE NOTIFICATION PRIORITIZATION

Network operators may control many thousands of devices and, as a consequence, they may be bombarded with notifications (e.g., posture assessments and exceptions for devices that do not comply with standards or which require remediation actions to circumvent security issues) and can be discouraged by unimportant or irrelevant information. To address the challenge that was described above, techniques are presented herein that support a process for intelligently filtering and prioritizing notifications to reduce noise and deliver recommendations that will provide the greatest impact to an environment. Aspects of the presented techniques encompass a smart recommendation notification system that prioritizes network actions based on multiple embedding spaces and dimensions; the use of business and financial logic, a persona, and a network operating state for reducing network actions into a prioritized output; the use of click-through and sequence mining to establish a ground truth of event prioritization of a network operator; an adaptive learning method for tracking proposed network recommendations to the final action that may be executed by a network operator; and a method for reducing multi-step recommendations based on an identification of the most efficient sequence of events based on a network operator implementation

An Empirical Examination of IPO Underpricing: Evidence from China

Much evidence suggests that the underpricing of initial public offerings is a common phenomenon in the stock markets of various countries. IPO underpricing reflects the efficiency of capital markets. Previous studies reported the level of IPO underpricing in Chinese A-share market is much higher than the average level of 60% in the emerging markets (Jenkinson and Ljungqvist, 2001). In this paper, the degree of IPO underpricing in Chinese stock market and its determinants are studied by empirical analysis. Using data in Shanghai and Shenzhen stock exchange from January 2008 to December 2016, this paper studied three main factors of Chinese underpriced IPOs, which are information asymmetry, reputation of underwriters and ownership structure. Our results showed that although the level of Chinese IPO underpricing is still higher, the reform of issuance mechanism leads Chinese underpricing level to bring down to emerging market level. Information asymmetry is the main reason for underpriced IPOs. Besides, the underwriters’ reputation and ownership structure have effects on the allocation of IPOs

Emergent Replica Conformal Symmetry in Non-Hermitian SYK2_2 Chains

Recently, the steady states of non-unitary free fermion dynamics are found to exhibit novel critical phases with power-law squared correlations and a logarithmic subsystem entanglement. In this work, we theoretically understand the underlying physics by constructing solvable static/Brownian quadratic Sachdev-Ye-Kitaev chains with non-Hermitian dynamics. We find the action of the replicated system generally shows (one or infinite copies of) ${O(2)\times O(2)}$ symmetries, which is broken to ${O(2)}$ by the saddle-point solution. This leads to an emergent conformal field theory of the Goldstone modes. We derive the effective action and obtain the universal critical behaviors of squared correlators. Furthermore, the entanglement entropy of a subsystem ${A}$ with length ${L_A}$ corresponds to the energy of the half-vortex pair ${S\sim \rho_s \log L_A}$, where ${\rho_s}$ is the total stiffness of the Goldstone modes. We also discuss special limits with more than one branch of Goldstone modes and comment on interaction effects.Comment: 16 pages, 4 figure

Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation

Free energy profile (FE Profile) is an essential quantity for the estimation of reaction rate and the validation of reaction mechanism. For chemical reactions in condensed phase or enzymatic reactions, the computation of FE profile at ab initio (ai) quantum mechanical/molecular mechanics (QM/MM) level is still far too expensive. Semiempirical (SE) method can be hundreds or thousands of times faster than the ai methods. However, the accuracy of SE methods is often unsatisfactory, due to the approximations that have been adopted in these methods. In this work, we proposed a new method termed MBAR+wTP, in which the ai QM/MM free energy profile is computed by a weighted thermodynamic perturbation (TP) correction to the SE profile generated by the multistate Bennett acceptance ratio (MBAR) analysis of the trajectories from umbrella samplings (US). The weight factors used in the TP calculations are a byproduct of the MBAR analysis in the post-processing of the US trajectories, which are often discarded after the free energy calculations. The results show that this approach can enhance the efficiency of ai FE profile calculations by several orders of magnitude