Minimization Variables Research Articles

Pengaruh Minimasi Pajak, Mekanisme Bonus dan Exchange Rate terhadap Transfer Pricing dengan Komite Audit sebagai Variabel Moderasi

Transfer pricing is a transaction of goods and services between several divisions in a business group at unreasonable prices, either by increasing or decreasing prices, mostly carried out by multinational companies that carry out cross-border activities. The aim, first, is to manipulate the amount of profit so that tax payments and dividend distributions are low. Second, inflating profits to polish financial reports. The country lost trillions of rupiah due to the transfer pricing practices of foreign companies in Indonesia. This research aims to obtain empirical evidence of the influence of Tax Minimization, Bonus Mechanisms, and Exchange Rates on Transfer Pricing. This research uses secondary data with a sample of mining companies for 2019-2022 on the IDX for 4 years. The result obtained is that Tax minimization variables have a significant effect on transfer pricing and the audit committee strengthens this effect, while the bonus mechanism and exchange rate have no effect on transfer pricing and the audit committee does not strengthen the effect.

Cystatin C vs creatinine eGFR in advanced CKD: an analysis of the STOP-ACEi trial

ABSTRACT Background In this secondary analysis of the STOP-ACEi trial, we explored the impact of discontinuing or continuing renin–angiotensin system inhibitor therapy in people with advanced chronic kidney disease on cystatin C estimated glomerular filtration rate (eGFR). Methods Cystatin C eGFRs were calculated at baseline, 12, 24 and 36 months using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) cystatin 2012, European Kidney Function Consortium and CKD-EPI Combined 2021 equations. We excluded samples obtained after the initiation of kidney replacement therapy. Primary analysis used complete case analysis and mixed-effects linear regression model, adjusting for minimization variables, baseline value, time-point and treatment by time interaction. Sensitivity analysis was conducted using a pattern mixture model to account for missing data that was not at random. To model the longitudinal cystatin C data with time-to-event data, a joint model was utilized which incorporated the cystatin C measurements at various time points and accounted for the occurrence of kidney replacement therapy. Results The mean cystatin C eGFRs (CKD-EPI 2012) at baseline were 17.8 mg/L [standard deviation (SD 6.3)] and 17.9 mL/min/1.73 m2 (SD 6.3) in the STOP and CONTINUE arms, respectively. The estimated least squares mean difference at 12 months between STOP and CONTINUE arm was –1.46 [95% confidence interval (CI) –2.39 to –0.52, P = .002]. The estimated least squares mean difference at 24 months was –2.27 (95% CI –3.48 to –1.06, P < .001). The estimated least squares mean difference at 36 months was –1.72 (95% CI –3.48 to 0.03, P = .05). Conclusion Our results are consistent with the primary study's analysis and sensitivity analyses support these findings and provide additional insights. Our findings demonstrate the similarity of creatinine and cystatin eGFR results and therefore support the use of cystatin C as an alternative marker of eGFR in advanced CKD, particularly in those in whom creatinine is likely to be less accurate.

Generalized Federated Learning via Gradient Norm-Aware Minimization and Control Variables

Federated Learning (FL) is a promising distributed machine learning framework that emphasizes privacy protection. However, inconsistencies between local optimization objectives and the global objective, commonly referred to as client drift, primarily arise due to non-independently and identically distributed (Non-IID) data, multiple local training steps, and partial client participation in training. The majority of current research tackling this challenge is mainly based on the empirical risk minimization (ERM) principle, while giving little consideration to the connection between the global loss landscape and generalization capability. This study proposes FedGAM, an innovative FL algorithm that incorporates Gradient Norm-Aware Minimization (GAM) to efficiently search for a local flat landscape. FedGAM specifically modifies the client model training objective to simultaneously minimize the loss value and first-order flatness, thereby seeking flat minima. To directly smooth the global flatness, we propose the more significant FedGAM-CV, which employs control variables to correct local updates, guiding each client to train models in a globally flat direction. Experiments on three datasets (CIFAR-10, MNIST, and FashionMNIST) demonstrate that our proposed algorithms outperform existing FL baselines, effectively finding flat minima and addressing the client drift problem.

Individual patient data meta-analysis of the effects of fluoxetine on functional outcomes after acute stroke.

Three large randomized controlled trials of fluoxetine for stroke recovery have been performed. We performed an individual patient data meta-analysis (IPDM) on the combined data. Fixed effects meta-analyses were performed on the combined data set, for the primary outcome (modified Rankin scale (mRS) at 6 months), and secondary outcomes common to the individual trials. As a sensitivity analysis, summary statistics from each trial were created and combined. The three trials recruited a combined total of 5907 people (mean age 69.5 years (SD 12.3), 2256 (38%) females, 2-15 days post-stroke) from Australia, New Zealand, United Kingdom, Sweden, and Vietnam; and randomized them to fluoxetine 20 mg daily or matching placebo for 6 months. Data on 5833 (98.75%) were available at 6 months. The adjusted ordinal comparison of mRS was similar in the two groups (common OR 0.96, 95% CI 0.87 to 1.05, p = 0.37). There were no statistically significant interactions between the minimization variables (baseline probability of being alive and independent at 6 months, time to treatment, motor deficit, or aphasia) and pre-specified subgroups (including age, pathological type, inability to assess mood, proxy or patient consent, baseline depression, country). Fluoxetine increased seizure risk (2.64% vs 1.8%, p = 0.03), falls with injury (6.26% vs 4.51%, p = 0.03), fractures (3.15% vs 1.39%, p < 0.0001) and hyponatremia (1.22% vs 0.61%, p = 0.01) but reduced new depression (10.05% vs 13.42%, p < 0.0001). At 12 months, there was no difference in adjusted mRS (n = 5760; common OR 0.98, 95% CI 0.89 to 1.07). Sensitivity analyses gave the same results. Fluoxetine 20 mg daily for 6 months did not improve functional recovery. It increased seizures, falls with injury, and bone fractures but reduced depression frequency at 6 months.

Health care overbooking cost minimization model

Challenges in the health care industry have confounded the provision of quality services for patients. Among many relevant concerns, the drive for cost-effectiveness and efficient care have placed considerable pressure on public health care systems and insurance coverage, amid existing barriers to restructuring entrenched systems. This study closely examines the factors impacting disruptions in health care scheduling systems using a structured case study in the health care industry. The study introduces a novel model to identify optimal overbooking capacity and minimize no-show costs. To address the complexity of this issue on a smaller scale, the model is implemented using a private hospital clinical platform in Jeddah, Saudi Arabia, instituting an overbooking reservation and queuing system in 14 departments to investigate the factors that influence disruptions and inefficacy of service provision, while also introducing a strategy for covering costs. The results identify the maximum amount of overbooking that can be made for each clinic. The cost-saving plan developed is expected to save each clinic a considerable sum, as opposed to randomly overbooking without any cost assumptions. Overall, if the clinics studied implemented this strategy, a total loss of no more than SAR. 2, 408 would be incurred from overbooking, in contrast to the exponentially growing amount of SAR. 10,000 that is currently lost on scheduling errors per year. The loss model developed has practical application as a tool for decision-making that includes no-show cost minimization variables.

SCORE: approximating curvature information under self-concordant regularization

Optimization problems that include regularization functions in their objectives are regularly solved in many applications. When one seeks second-order methods for such problems, it may be desirable to exploit specific properties of some of these regularization functions when accounting for curvature information in the solution steps to speed up convergence. In this paper, we propose the SCORE (self-concordant regularization) framework for unconstrained minimization problems which incorporates second-order information in the Newton-decrement framework for convex optimization. We propose the generalized Gauss–Newton with Self-Concordant Regularization (GGN-SCORE) algorithm that updates the minimization variables each time it receives a new input batch. The proposed algorithm exploits the structure of the second-order information in the Hessian matrix, thereby reducing computational overhead. GGN-SCORE demonstrates how to speed up convergence while also improving model generalization for problems that involve regularized minimization under the proposed SCORE framework. Numerical experiments show the efficiency of our method and its fast convergence, which compare favorably against baseline first-order and quasi-Newton methods. Additional experiments involving non-convex (overparameterized) neural network training problems show that the proposed method is promising for non-convex optimization.

Power minimization of gas transmission network in fully transient state using metaheuristic methods

Abstract In gas transmission networks, the pressure drop caused by friction is one of the main operation costs that is compensated through consuming energy in the compressors. In the competitive market of energy, considering the demand variation is inevitable. Hence, the power minimization should be carried out in transient state. Since the minimization problem is severely nonlinear and nonconvex subjected to nonlinear constraints, utilizing a powerful minimization tool with a straightforward procedure is very helpful. In this paper, a novel approach is proposed based on metaheuristic algorithms for power minimization of a gas transmission network in fully transient conditions. The metaheuristic algorithms, unlike the gradient dependent method, can solve the complicated minimization problem without simplification. In the proposed strategy, the cost function is not expressed explicitly as a function of minimization variables; therefore, the transient minimization can be as precise as possible. The minimization is carried out by a straightforward methodology in each time sample, which leads to more precise solutions as compared to the quasi transient minimization. The metaheuristic minimizer, called the particle swarm optimization gravitational search algorithm (PSOGSA), is utilized to find the optimum operating set points. The numerical results also confirm the accuracy and well efficiency of the proposed method.

Statistical analysis plan for Better Evidence for Selecting Transplant Fluids (BEST-Fluids): a randomised controlled trial of the effect of intravenous fluid therapy with balanced crystalloid versus saline on the incidence of delayed graft function in deceased donor kidney transplantation

BackgroundDelayed graft function, or the requirement for dialysis due to poor kidney function, is a frequent complication of deceased donor kidney transplantation that is associated with inferior outcomes. Intravenous fluids with a high chloride content, such as isotonic sodium chloride (0.9% saline), are widely used in transplantation but may increase the risk of poor kidney function. The primary objective of the BEST-Fluids trial is to compare the effect of a balanced low-chloride crystalloid, Plasma-Lyte 148 (Plasmalyte), versus 0.9% saline on the incidence of DGF in deceased donor kidney transplant recipients. This article describes the statistical analysis plan for the trial.Methods and designBEST-Fluids is an investigator-initiated, pragmatic, registry-based, multi-centre, double-blind, randomised controlled trial. Eight hundred patients (adults and children) in Australia and New Zealand with end-stage kidney disease admitted for a deceased donor kidney transplant were randomised to intravenous fluid therapy with Plasmalyte or 0.9% saline in a 1:1 ratio using minimization. The primary outcome is delayed graft function (dialysis within seven days post-transplant), which will be modelled using a log-binomial generalised linear mixed model with fixed effects for treatment group, minimization variables, and ischaemic time and a random intercept for study centre. Secondary outcomes including early kidney transplant function (a ranked composite of dialysis duration and the rate of graft function recovery), treatment for hyperkalaemia, and graft survival and will be analysed using a similar modelling approach appropriate for the type of outcome.DiscussionBEST-Fluids will determine whether Plasmalyte reduces the incidence of DGF and has a beneficial effect on early kidney transplant outcomes relative to 0.9% saline and will inform clinical guidelines on intravenous fluids for deceased donor kidney transplantation. The statistical analysis plan describes the analyses to be undertaken and specified before completion of follow-up and locking the trial databases.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12617000358347. Prospectively registered on 8 March 2017ClinicalTrials.gov identifier NCT03829488. Registered on 4 February 2019

Abstract P485: Predictors and Clinical Impact of Deep Grey Matter Infarction After Endovascular Treatment for Large Vessel Occlusion Stroke: Results From the Escape-NA1 Trial

Purpose: In this post-hoc analysis of the ESCAPE-NA1 trial, we investigated the prevalence of deep grey matter infarcts and their influence on clinical outcome. Methods: Infarcts on 24 hour follow up imaging (non contrast head CT or diffusion-weighted MRI) were categorized as predominantly deep grey matter infarcts (caudate and/or lentiform nucleus infarcts with sparing of the superficial grey matter and white matter) vs. other infarcts. Total infarct volume was manually segmented in all patients. When MRI follow-up was available, deep grey matter and grey matter infarct volumes were segmented separately. Multivariable logistic regression with adjustment for key minimization variables and by infarct volume was used to assess the association of predominantly deep grey matter infarcts and good outcome. Results: Of the 1026 included patients, 316 (30.8%) had predominantly deep grey matter infarcts. Cumulative proportions of good outcome for overall, grey matter, deep grey matter, and superficial grey matter infarct volumes are shown in the figure. Good outcomes were more frequently achieved in patients with predominantly deep grey matter infarcts (239/316 [75.6%] vs. 374/704 [53.1%]). Deep infarcts were tightly correlated with infarct volume (Pearson rho -0.35) and in multivariable analysis deep grey matter infarcts were predictive of outcome overall; when examined in volume percentiles, there was no effect of deep infarct location. Conclusion: Predominantly deep grey matter infarcts are associated with good outcomes. Deep grey matter infarct location favorable prognosis is associated with small overall infarct size.

Abstract P538: A Detailed Analysis of Intracranial Hemorrhage After Endovascular Treatment in Acute Ischemic Stroke Due to Large Vessel Occlusion in the Escape-NA1 Trial

Purpose: We investigated the prevalence and prognostic impact on outcome of any intracranial hemorrhage, hemorrhage morphology, type and volume in acute ischemic stroke patients undergoing mechanical thrombectomy. Methods: Prevalence of intracranial hemorrhage, hemorrhage type, morphology and volume was determined on 24h follow-up imaging (non contrast head CT or gradient-echo/susceptibility-weighted MRI). Proportions of good outcome (mRS 0-2 at 90 days) were reported for patients with vs. without any intracranial hemorrhage. Multivariable logistic regression with adjustment for key minimization variables and total infarct volume was performed to obtain adjusted effect size estimates for hemorrhage type and volume on good outcome. Results: Hemorrhage on follow up-imaging was seen in 372/1097 (33.9%) patients, among them 126 (33.9%) with hemorrhagic infarction (HI) type 1, 108 (29.0%) with HI-2, 72 /19.4%) with parenchymal hematoma (PH) type 1, 37 (10.0) with PH2, 8 (2.2%) with remote PH and 21 (5.7%) with extra-parenchymal/intraventricular hemorrhage. Good outcomes were less often achieved by patients with hemorrhage on follow-up imaging (164/369 [44.4%] vs. 500/720 [69.4%]). Any type of intracranial hemorrhage was strongly associated with decreased chances of good outcome ( adj OR 0.62 [CI 95 0.44 - 0.87]). The effect of hemorrhage was driven by both PH hemorrhage sub-type [PH-1 ( adj OR 0.39 [CI 95 0.21 - 0.72]), PH-2 ( adj OR 0.15 [CI 95 0.05 - 0.50])] and extra-parenchymal/intraventricular hemorrhage ( adj OR 0.60 (0.20-1.78) Petechial hemorrhages (HI-1 and HI-2) were not associated with poorer outcomes. Hemorrhage volume ( adj OR 0.97 [CI 95 0.05 - 0.99] per ml increase) was significantly associated with decreased chances of good outcome. Conclusion: Presence of any hemorrhage on follow-up imaging was seen in one third of patients and strongly associated with decreased chances of good outcome.

DETERMINANT DETECTION OF TRANSFER PRICING DECISIONS

This study aims to verify and analyze the effect of tax minimization, tunneling incentive and bonus mechanisms against the decision of transfer pricing. The population in this study are all companies registered in the Indonesia Stock Exchange unless the moving company financial sector during the period 2015-2017. While the sampling process using a purposive sampling method and the sample obtained as many as 132 companies. Tax minimization proxied using ETR (effective tax rate), tunneling incentive proxied using the percentage of foreign ownership above 20%, and a mechanism that is measured by the percentage of net profit in year t to the year t-1.This research is a quantitative research and data used in this research is secondary data obtained from the company's annual report in 2015-2017 sourced from www.idx.co.id. This study analyzes the data by using logistic regression with SPSS 23.0. The results of this study indicate that tax minimization variables influence the decision of transfer pricing. This study also shows that the tunneling incentive effect on transfer pricing decisions while the variable bonus mechanism does not affect the decision of transfer pricing.

DETEKSI DETERMINAN KEPUTUSAN TRANSFER PRICING

This study aims to verify and analyze the effect of tax minimization, tunneling incentive and bonus mechanisms against the decision of transfer pricing. The population in this study are all companies registered in the Indonesia Stock Exchange unless the moving company financial sector during the period 2013-2015. While the sampling process using purposive sampling method and sample obtained as many as 132 companies. Tax minimization proxied using ETR (effective tax rate), tunneling incentive proxied using the percentage of foreign ownership above 20%, and a mechanism that is measured by the percentage of net profit in year t to the year t-1.&#x0D; This research is a quantitative research and data used in this research is secondary data obtained from the company's annual report in 2013-2015 sourced from www.idx.co.id. This study data analysis using logistic regression with SPSS 21.0. The results of this study indicate that tax minimization variables influence the decision of transfer pricing. This study also shows that the tunneling incentive effect on transfer pricing decisions. While the variable bonus mechanism does not affect the decision of transfer pricing.

Left Ventricular Thrombus After Primary PCI for ST-Elevation Myocardial Infarction: 1-Year Clinical Outcomes

BackgroundLeft ventricular thrombus formation is a complication of acute myocardial infarction. However, the incidence and risk of systemic thromboembolism in the era of primary angioplasty for ST elevation myocardial infarction (STEMI) is unclear. This study aims to determine clinical outcomes in patients with STEMI treated with primary angioplasty and left ventricular thrombus at 1 year. MethodsPatients who underwent primary angioplasty for STEMI and had a transthoracic echocardiogram were recruited. The primary endpoint was a composite of all-cause mortality, stroke, and systemic thromboembolism at 1 year. For the primary endpoint, the difference between the presence and absence of left ventricular thrombus was compared using a logistic regression, adjusting for minimization variables including age, diabetes mellitus, hypertension, and previous stroke. ResultsOf 2608 patients who underwent primary angioplasty for STEMI, 1645 (63%) patients had a transthoracic echocardiogram performed during the index hospital admission. Forty patients (2.4%) had evidence of left ventricular thrombus on transthoracic echocardiography. Patients with left ventricular thrombus were more likely to develop atrial fibrillation in the immediate postinfarction period (6 [15%] vs 87 [5.4%], P = 0.025). At 1 year, the primary endpoint occurred in 4 (10%) patients with left ventricular thrombus and 146 (9.1%) who did not (logistic regression hazard ratio 0.79, 95% confidence interval 0.23-2.70). ConclusionsIn the contemporary era of mechanical reperfusion for STEMI, echocardiographic detection of left ventricular thrombus was observed in <3% patients. The presence of left ventricular thrombus was not associated with an increased risk of systemic thromboembolism.

Randomized feasibility trial of high-intensity interval training before elective abdominal aortic aneurysm repair.

This study assessed the feasibility of a preoperative high-intensity interval training (HIT) programme in patients awaiting elective abdominal aortic aneurysm repair. In this feasibility trial, participants were allocated by minimization to preoperative HIT or usual care. Patients in the HIT group were offered three exercise sessions per week for 4 weeks, and weekly maintenance sessions if surgery was delayed. Feasibility and acceptability outcomes were: rates of screening, eligibility, recruitment, retention, outcome completion, adverse events and adherence to exercise. Data on exercise enjoyment (Physical Activity Enjoyment Scale, PACES), cardiorespiratory fitness (anaerobic threshold and peak oxygen uptake), quality of life, postoperative morbidity and mortality, duration of hospital stay and healthcare utilization were also collected. Twenty-seven patients were allocated to HIT and 26 to usual care (controls). Screening, eligibility, recruitment, retention and outcome completion rates were 100 per cent (556 of 556), 43·2 per cent (240 of 556), 22·1 per cent (53 of 240), 91 per cent (48 of 53) and 79-92 per cent respectively. The overall exercise session attendance rate was 75·8 per cent (276 of 364), and the mean(s.d.) PACES score after the programme was 98(19) ('enjoyable'); however, the intensity of exercise was generally lower than intended. The mean anaerobic threshold after exercise training (adjusted for baseline score and minimization variables) was 11·7 ml per kg per min in the exercise group and 11·4 ml per kg per min in controls (difference 0·3 (95 per cent c.i. -0·4 to 1·1) ml per kg per min). There were trivial-to-small differences in postoperative clinical and patient-reported outcomes between the exercise and control groups. Despite the intensity of exercise being generally lower than intended, the findings support the feasibility and acceptability of both preoperative HIT and the trial procedures. A definitive trial is warranted. Registration number: ISRCTN09433624 ( https://www.isrctn.com/).

Economic Design of Reinforced Concrete Conical Shells

This work presents economic reinforced concrete conical shells dimensions and reinforcement in order to save time and efforts of the designers who aim to design conical shells economically from 5m to 20m spans. In addition to that, this work presents the economic shell raise (hec) vs. span (D) formula that gives lowest construction cost. The considered loads are the own weight of the shell body, external finishing by glazed brick and internal finishing layer of gypsum plaster (juss). As well as the dead load, live load is considered too. A computer program is written in the Visual Basic programming language. This computer program consists of two parts. The first part is the linear analysis using the membrane theory formulas to get the forces at the crown and at the support of the shell. The second part uses Nelder and Mead's minimization method to minimize the total cost formula (objective function). Three types of constrains confine this minimization regarding to strength, buckling and accuracy of the analysis method. The minimization variables are two; shell rise (h) and shell thickness (t). The two parts of the computer program are connected together in a force and cost convergence iterative loop in order to get the dimensions and reinforcement ratios that lead to the minimum cost.

On a Gradient-Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain

Sparse signals can be recovered from a reduced set of samples by using compressive sensing algorithms. In common compressive sensing methods the signal is recovered in the sparsity domain. A method for the reconstruction of sparse signals which reconstructs the missing/unavailable samples/measurements is recently proposed. This method can be efficiently used in signal processing applications where a complete set of signal samples exists. The missing samples are considered as the minimization variables, while the available samples are fixed. Reconstruction of the unavailable signal samples/measurements is preformed using a gradient-based algorithm in the time domain, with an adaptive step. Performance of this algorithm with respect to the step-size and convergence are analyzed and a criterion for the step-size adaptation is proposed in this paper. The step adaptation is based on the gradient direction angles. Illustrative examples and statistical study are presented. Computational efficiency of this algorithm is compared with other two commonly used gradient algorithms that reconstruct signal in the sparsity domain. Uniqueness of the recovered signal is checked using a recently introduced theorem. The algorithm application to the reconstruction of highly corrupted images is presented as well.

Optimal Sensor Configuration for Bridge Structures Following a Probabilistic Approach

In this paper, a statistical methodology is introduced for identifying the most effective way to install a limited number of sensors on a typical distributed-parameter system, i.e., a three-span continuous bridge model with two elastic supports to extract as much information as possible. This is performed by minimizing the uncertainties associated with the identified bridge modeling parameters. In the proposed methodology, the information entropy is employed as a measure to quantify the uncertainty of the identified structural modeling parameters. The problem of optimal sensor placement is then formulated as a continuous optimization problem, in which the information entropy is minimized, with the sensor configurations as the minimization variables. The generally used discrete-coordinate systems modeled by the finite element (FE) method can only approximate their actual dynamic behavior, which would in turn influence the sensor configuration results, and the sensors are confined to be only put on discrete nodes related to the coarse mesh scheme usually employed. For structures such as bridges belonging to typical distributed-parameter systems, it’s more reasonable and convenient to be modeled as continues-coordinate system with analytical formulation. It’s the main purpose of this paper to develop a sensor placement method for continues-coordinate systems following the Bayesian theorem and the information entropy method, with the binary-encoded genetic algorithm (GA) employed as the optimization technique.

Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating

A methodology is presented for the identification of the best locations to install a given number of sensors on a structure so as to extract as much information as possible for structural model updating. The information entropy measure is employed to quantify the uncertainties of the set of identified model parameters. The problem of optimal sensor placement is formulated as a discrete optimization problem in which the information entropy measure is minimized and the sensor configurations are taken as the minimization variables. This discrete optimization problem is computationally demanding especially for large-scale structures. An efficient genetic algorithm-based optimization method is developed to solve this minimization problem to make the entropy-based optimal sensor configuration approach applicable for large-scale structural systems. A typical transmission tower is first used as a numerical example to illustrate the proposed methodology. The performance of the optimal sensor placement technique and the proposed optimization method are then verified using the measured dynamic data from a 2.6 m high transmission tower model under laboratory conditions. The computational time of the proposed optimization method can be reduced in the future by making use of the parallel computing technology. Copyright © 2009 John Wiley & Sons, Ltd.

Improving health and productivity of depressed workers: a pilot randomized controlled trial of telephone cognitive behavioral therapy delivery in workplace settings

Objective To examine the feasibility of telephone-delivered cognitive behavioral therapy (T-CBT) in an occupational context, with reference to participant recruitment, treatment adherence, follow-up and effect. Method Eligible participants comprised all employees of a large communications company with authorized work absence due to mild/moderate mental health difficulties over a 10-month period. Fifty-three consenting participants were centrally randomized to 12 weeks T-CBT or usual care, with minimization on age, gender and illness severity. Primary (symptom severity) and secondary outcomes (self-rated work performance and productivity) were measured at baseline and 3-months via postal questionnaires. Intention-to-treat analysis comprised multiple regression modeling with adjustment for missing response predictors, minimization variables and baseline values. Results Twenty-three employees attended one or more T-CBT sessions. T-CBT was associated with medium–large effects sizes on clinical outcomes (0.63–0.77) and work productivity scores (0.75–0.88). Twenty-one patients failed to return 3-month primary outcome data. Non-respondents were more likely to be male and more severely ill. Conclusion Delivery of T-CBT in an occupational context is feasible with evidence of potential effect. Larger-scale trials are warranted. These studies demand assertive outreach or telephone-based assessment strategies in order to maximize participant recruitment and follow-up.

Convergence and application of a modified iteratively regularized Gauss–Newton algorithm

Theoretical convergence results for an iteratively regularized Gauss–Newton (IRGN) algorithm with a Tikhonov regularization term using a seminorm generated by a linear operator are established. The convergence theorem uses an a posteriori stopping rule and a modified source condition, without any restriction on the nonlinearity of the operator. The theoretical results are illustrated by simulations for a one-dimensional version of the exponentially ill-posed optical tomography inverse problem for the diffusion (D) and absorption (μ) coefficient spatial distributions. The modified Tikhonov regularization performs the mapping of the minimization variables, which are the coefficients of the spline expansions for D and μ, to physical space. This incorporates the inherently differing scales of these variables in the minimization, and also suggests relative weighting of the regularization terms with respect to each parameter space. The presented modification of the IRGN allows greater flexibility for implementations of IRGN solutions of ill-posed inverse problems in which differing scales in physical space hinder standard IRGN inversions.