Forecasting Employee Turnover in Large Organizations

Researchers and human resource departments have focused on employee turnover for decades. This study developed a methodology forecasting employee turnover at organizational and departmental levels to shorten lead time for hiring employees. Various time series modeling techniques were used to identify optimal models for effective employee-turnover prediction based on a large U.S organization\u27s 11-year monthly turnover data. A dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator efficiently predicted turnover. Another turnover model predicted both retirement and quitting, including early retirement incentives, demographics, and external economic indicators using the Cox proportional hazard model. A variety of biases in employee-turnover databases along with modeling strategies and factors were discussed. A simulation demonstrated sampling biases\u27 potential impact on predictions. A key factor in the retirement was achieving full vesting, but employees who did not retire immediately maintain a reduced hazard after qualifying for retirement. Also, the model showed that external economic indicators related to S&P 500 real earnings were beneficial in predicting retirement while dividends were most associated with quitting behavior. The third model examined voluntary turnover factors using logistic regression and forecasted employee tenure using a decision tree for four research and development departments. Company job title, gender, ethnicity, age and years of service affected voluntary turnover behavior. However, employees with higher salaries and more work experience were more likely to quit than those with lower salaries and less experience. The result also showed that college major and education level were not associated with R&D employees\u27 decision to quit

Evaluation and Recommendations for Preservation Practices in Historic Districts in China: The Case of Dashilan Area, Beijing

Driven by economic and social pressures, large-scale urban renewal movements have taken place in China since 1970s. Many of these projects were carried out in a radical way, ignorant of history, culture and existing physical fabric. Dashilan Area in Beijing is a typical example. In the name of historic preservation, renewal projects occurred in 2003. Although there were two historic districts designated in the area and the master plan required different levels of tolerance of change, the reality demonstrated its failure to accomplish the scheme. Random demolition and pseudo-historic reconstructions occurred, which not only eliminated some of the character-defining elements but also partially destroyed the historic fabric of the area. The thesis focuses on the renewal practices completed in a sub-area, namely Block C, within the Dashilan Historic District. The Advantages and disadvantages of these practices are analyzed and urban design guidelines are established for this Block. Historic elements of these jade shops and Baroque-style buildings draw out physical features and urban qualities that should be maintained in Block C, which formed the philosophy to create design guidelines at the end. The overall approach can be applied to the whole Dashilan Area as well as other historic areas in China

RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding

Knowledge graph (KG) reasoning is an important problem for knowledge graphs. It predicts missing links by reasoning on existing facts. Knowledge graph embedding (KGE) is one of the most popular methods to address this problem. It embeds entities and relations into low-dimensional vectors and uses the learned entity/relation embeddings to predict missing facts. However, KGE only uses zeroth-order (propositional) logic to encode existing triplets (e.g., ``Alice is Bob's wife."); it is unable to leverage first-order (predicate) logic to represent generally applicable logical \textbf{rules} (e.g., ``$\forall x,y \colon x ~\text{is}~ y\text{'s wife} \rightarrow y ~\text{is}~ x\text{'s husband}$''). On the other hand, traditional rule-based KG reasoning methods usually rely on hard logical rule inference, making it brittle and hardly competitive with KGE. In this paper, we propose RulE, a novel and principled framework to represent and model logical rules and triplets. RulE jointly represents entities, relations and logical rules in a unified embedding space. By learning an embedding for each logical rule, RulE can perform logical rule inference in a soft way and give a confidence score to each grounded rule, similar to how KGE gives each triplet a confidence score. Compared to KGE alone, RulE allows injecting prior logical rule information into the embedding space, which improves the generalization of knowledge graph embedding. Besides, the learned confidence scores of rules improve the logical rule inference process by softly controlling the contribution of each rule, which alleviates the brittleness of logic. We evaluate our method with link prediction tasks. Experimental results on multiple benchmark KGs demonstrate the effectiveness of RulE

Comparison of the calculated method to the driving voltage applied across the lay in single and double layers of piezoelectric material of active sound absorption

Piezoelectric material can be used as a main component of devices, such as transducers, energy exchangers and arresters. Due to its excellent mechanics and electric coupling performances, piezoelectric material can also be utilized in control system of sound and vibration. However, there have not been any publications outlining the basic equations of reflection or transmission coefficients of driving voltage applied across the layers (single or double) of piezoelectric material. In this paper, two methods – the theoretical method and the electro-acoustic analogy method – are used in order to compare the driving voltage applied across the single and the double layer of active sound surfaces of piezoelectric material. Computational results indicate that the proposed theoretical models are correct and applicable in practical implementations

Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews

People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise the awareness of the self-selection bias by making three types of information concerning user ratings and reviews transparent. We distill these three pieces of information (reviewers experience, the extremity of emotion, and reported aspects) from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess the perceptions of the usefulness of such information and identify the exact facets people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases the awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content

Effects of godet wheel position on compact siro-spun core yarn characteristics

Cotton-spandex compact siro-spun core yarns (29.2tex/44.4dtex and 14.6tex/44.4dtex) have been prepared on two kinds of compact spinning, viz complete condensing spinning system (CCSS) and lattice apron compact spinning system (LACSS) respectively. Three godet wheel positions on two kinds of compact system have been selected and corresponding yarn covering effect is studied respectively. Especially, the surface morphology and cross-sections of the core yarns are observed. Then, the covering effects are compared and affecting factors are analyzed. Moreover, other yarn properties including yarn hairiness, strength and evenness are also tested and compared. The results indicate that the covering effect of staple fibres is the most even when the godet wheel position is set on left side for both CCSS and LACSS