Establishing links between organizational climate, employee well-being and historical patient outcomes

This research undertaken in collaboration with Queensland Health analysed the links between dimensions of workplace climate/employee well-being contained in a number of Queensland Health databases, including the Patient Satisfaction Survey, the Clinical Incident database, the compliments and complaints database, the Variable Life Adjusted Display (VLAD) Database and the Better Workplaces Staff Opinion Survey database. Queensland Health sought to identify in what ways workplace climate is related to patient outcomes using existing datasets collected within the Queensland Health Centre for Healthcare Improvement. The process of establishing links involved matching aggregated data for specific facilities (where possible), or failing that, larger facilities (e.g. Hospital), or the Health Service District. Once the datasets had been matched on location or facility, correlations were calculated between the aggregated scores. The results demonstrated links between the data sets. These links showed that a better workplace climate is associated with greater reported numbers of clinical incidents, especially “no harm” clinical incidents. There was also a link between workplace climate and patient compliments/complaints which show that unsolicited compliments received from patients and their families are clearly related to a number of positive aspects of workplace climate (workplace morale, role clarity, and appraisal and recognition) and individual morale. The results linking workplace climate and patient satisfaction showed that there is a strong positive relationship between overall patient satisfaction and role clarity, and a negative relationship between overall patient satisfaction and both workplace distress and excessive work demands. While these results relate to historical data and therefore should not be construed to reflect the current state of operation within Queensland Health, they are still indicative of some very important relationships. This is the first study to demonstrate that more positive clinical management practices, better perceptions of the workplace climate and better employee well-being are a reflection of a better incident reporting and learning culture in a health care organization, ultimately resulting in improved patient outcomes

ADHD-INTERNALIZING DISORDER CO-OCCURRENCE IN CHILDHOOD AND ADOLESCENCE: COMPARING NETWORK AND LATENT VARIABLE CONCEPTUALIZATIONS

Co-occurrence of attention-deficit/hyperactivity disorder (ADHD) with depression or anxiety (i.e., internalizing disorders) is a major route to poor outcomes, with temperament traits presenting as potential shared risk markers that underlie these disorders’ development and characterization. Prior work investigating the nature of ADHD-internalizing disorder co-occurrence using structural equation modeling has provided support for both temperament-based common cause (i.e., effortful control and negative affect as liabilities for multiple disorders) and direct causation (i.e., ADHD directly contributing to risk for internalizing disorders) effects separately. Using a network approach, the current study represented the first attempt to integrate these effects into one model while parsing heterogeneity in the trait-symptom and symptom-symptom relations within them. Participants were 799 children and adolescents aged 7-13 years at baseline (61.20% boys, 85.11% White; 59.57% diagnosed with ADHD). Across two measurement points approximately five years apart (i.e., Year 1, Year 6), parents/caregivers provided ratings of participants’ ADHD symptoms and temperament traits and participants provided ratings of depressive and anxiety symptoms. Pertaining to ADHD-depression networks, results suggested effortful control and, particularly, negative affect as transdiagnostic risk markers via relations with symptoms of both disorders. Simultaneously, depressive symptoms associated with reductions in perceived self-competence and difficulty making friends were uniquely related to several ADHD symptoms in Year 1, and ADHD inattentive symptoms (i.e., loses things; does not follow through; has difficulty sustaining attention) were uniquely related to depressive symptoms associated with reductions in perceived self-competence, distress/hopelessness, low self-worth, and difficulty making friends in Year 6. Examination of ADHD-anxiety networks suggested limited heterogeneity in symptom-symptom relations, although negative affect emerged as a core transdiagnostic risk marker via relations with inattentive and hyperactive/impulsive ADHD symptoms and anxiety symptoms associated with somatic problems and peer-related fears. Comparison of network findings with those of structural equation modeling approaches to conceptualizing common cause and direct causation effects suggested consistent and complementary results. No differences were identified in the structure of networks across Years 1 and 6, as well as gender. Continued clarification of specific and unique common cause and direct causation effects in the context of one another may help identify those most influential to the development and characterization of ADHD-internalizing disorder co-occurrence, with a focus on such effects potentially highlighting targets for screening tools and interventions that address and account for symptoms of multiple disorders

Fast S-parameter Convolution for Eye Diagram Simulations of High-speed Interconnects.

With the increase in signal frequency and the complexity of high-speed interconnects, signal integrity has become a prominent issue in modern electronic devices

Electronic Structure of LuRh2Si2: "Small" Fermi Surface Reference to YbRh2Si2

We present band structure calculations and quantum oscillation measurements on LuRh2Si2, which is an ideal reference to the intensively studied quantum critical heavy-fermion system YbRh2Si2. Our band structure calculations show a strong sensitivity of the Fermi surface on the position of the silicon atoms zSi within the unit cell. Single crystal structure refinement and comparison of predicted and observed quantum oscillation frequencies and masses yield zSi = 0.379c in good agreement with numerical lattice relaxation. This value of zSi is suggested for future band structure calculations on LuRh2Si2 and YbRh2Si2. LuRh2Si2 with a full f electron shell represents the "small" Fermi surface configuration of YbRh2Si2. Our experimentally and ab initio derived quantum oscillation frequencies of LuRh2Si2 show strong differences with earlier measurements on YbRh2Si2. Consequently, our results confirm the contribution of the f electrons to the Fermi surface of YbRh2Si2 at high magnetic fields. Yet the limited agreement with refined fully itinerant local density approximation calculations highlights the need for more elaborated models to describe the Fermi surface of YbRh2Si2.Comment: 12 pages 10 figure

Progress in neural network based techniques for signal integrity analysis–a survey

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data

Can rainmakers justify their pay? The role of investment banks in REIT M&As

This study explicitly rejects the prima facie proposition that the top-tier investment banks are capable of delivering supernormal value creation to the shareholders of a REIT acquirer in a corporate acquisition. Using the event study method, we find that REIT acquirers advised by market-leading investment banks suffer an average cumulative abnormal return of −4.41% following the M&A announcement, whereas REIT acquirers advised by non-top-tier investment banks only suffer an average cumulative abnormal return of −1.49%. The evidence shows that the contemporary practice of employing investment banks based on the prestige of the advisory firms could potentially result in value-destroying M&As for the REIT acquirers

Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process

This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the non-normalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a square-shaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models

Application of a Stable Latency Insertion Method for Simulations of Power Distribution Networks

This paper presents an application of a stable implementation of the latency insertion method for simulations of power distribution networks (PDN). Traditionally, simulations of PDNs poses a considerable challenge due to their large circuit sizes. While the latency insertion method can be applied to simulate these networks, the existence of low latency elements results in a more stringent stability criterion which reduces the efficiency of the method. Using the improved formulation, a latency insertion method that is free from the stability criteria is obtained, which results in no limitation on the size of the time step

Identifying the Molecular Edge Termination of Exfoliated Hexagonal Boron Nitride Nanosheets with Solid-State NMR Spectroscopy and Plane-Wave DFT Calculations

Hexagonal boron nitride nanosheets (h-BNNS), the isoelectronic analog to graphene, have received interest over the past decade due to their high thermal oxidative resistance, high bandgap, catalytic activity, and low cost. The functional groups that terminate boron and nitrogen zigzag and/or armchair edges directly affect their chemical, physical, and electronic properties. However, an understanding of the molecular edge termination present in h-BNNS is lacking. Here, high-resolution magic-angle spinning (MAS) solid-state NMR (SSNMR) spectroscopy, and plane-wave density-functional theory (DFT) calculations are used to determine the molecular edge termination in exfoliated h-BNNS. 1H → 11B cross-polarization MAS (CPMAS) SSNMR spectra of h-BNNS revealed multiple hydroxyl/oxygen coordinated boron edge sites that were not detectable in direct excitation experiments. A dynamic nuclear polarization (DNP)-enhanced 1H → 15N CPMAS spectrum of h-BNNS displayed four distinct 15N resonances while a 2D 1H{14N} dipolar-HMQC spectrum acquired with fast MAS revealed three distinct 14N environments. Plane-wave DFT calculations were used to construct model edge structures and predict the corresponding 11B, 14N and 15N SSNMR spectra. Comparison of the experimental and predicted SSNMR spectra confirms that zigzag and armchair edges with both amine and boron hydroxide/oxide termination are present. The detailed characterization of h-BNNS molecular edge termination will prove useful for many material science applications. The techniques outlined here should also be applicable to understand the molecular edge terminations in other 2D materials

EYE-HEIGHT/WIDTH PREDICTION USING ARTIFICIAL NEURAL NETWORKS FROM S-PARAMETERS WITH VECTOR FITTING

Artificial neural networks (ANNs) have been used to model microwave and RF devices over the years. Conventionally, S-parameters of microwave/RF designs are used as the inputs of neural network models to predict the electrical properties of the designs. However, using the S-parameters directly as inputs into the ANN results in a large number of inputs which slows down the training and configuration process. In this paper, a new method is proposed to first disassemble the S-parameters into poles and residues using vector fitting, and then the poles and residues are used as the input data during configuration and training of the neural networks. Test cases show that the ANN trained using the proposed method is able to predict the eye-heights and eye-widths of typical interconnect structures with minimal error, while showing significant speed improvement over the conventional method