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Objective This study was performed to evaluate the effect of two different methods of controlling glycemic variability (GV) in patients with severe traumatic brain injury (STBI) undergoing surgery. Methods Patients with STBI were randomly grouped into a conventional adjustment process (CAP) group and modified Leuven’s adjustment process (mLAP) group. Each group included 50 patients. Blood glucose levels were continuously monitored and data were recorded and analyzed. Results The mean blood glucose level was stable in both groups for 5 days postoperatively with no significant difference. The standard deviation of the blood glucose level, mean amplitude of glycemic excursions, and glycemic lability index were significantly higher in the CAP than mLAP group for the first 2 days. In the final 3 days, no significant differences were observed between the two groups. The incidence of hypoglycemia was significantly higher in the CAP than mLAP group on the first day. This value gradually declined during the following 4 days, but the difference between the two groups was not significant. Conclusion The mLAP produced more favorable results than the CAP for GV control in the early stage after surgery for STBI.

The social adjustment process is a prominent factor in the literature on the transition to higher education (HE). This vast body of research, however, has predominantly focussed on academically oriented first-year higher education (FYHE) contexts, leaving professionally oriented programs (providing vocational education that prepares students for a particular occupation) rather underexamined. Therefore, this study set out to explore what first-year students in professional HE contexts perceive to be the most important social experiences associated with their adjustment process in the first semester of FYHE. Furthermore, this study examines the extent to which the uncovered key experiences associated with social adjustment are reported at different times. Hereto, drawing on the critical incidents technique, qualitative data were collected from 104 purposively selected freshmen, enrolled in a Flemish (Dutch-speaking part of Belgium) university college which offers professional HE programs, such as social work and nursing. At the start of the second semester of FYHE, these respondents completed ‘reflective logs’ wherein they reflected on three critical social experiences during their first semester in HE. Firstly, a qualitative content analysis revealed that students report on five overarching themes: (1) dealing with the unknown, (2) establishing a first connection, (3) establishing a deeper connection, (4) support from peers and others, and (5) loneliness. Secondly, a quantitative analysis of code occurrence suggests that these five uncovered themes seem to be more significant at different times in the first semester of FYHE. Implications of the findings for the development of guidance and coaching initiatives are discussed.

This paper extends the framework of “rational behavior adjustment process” (RBAP) to incorporating the “boundedly rational user equilibrium” (BRUE). The proportional-switch adjustment process (PSAP) and the network tatonnement process (NTP) are extended to the BRUE case, and their dynamical equations are shown to be Lipschitz continuous, which guarantees the global uniqueness of the classical solutions. A special group of the BRUE-RBAP is proposed, for which the path flows would increase if the paths are in an acceptable path set, and would decrease otherwise. Classical solutions to this special group of models may not exist. Stability of the BRUE-RBAP with classical solutions is proved with separable link travel cost functions. For nonseparable link travel cost functions, the stability of the BRUE-PSAP is proved. Numerical examples are presented to demonstrate the evolution processes of BRUE-PSAP and BRUE-NTP under various bounded rationality thresholds and different initial states. The applicability of BRUE-PSAP in larger networks with asymmetric link travel cost functions is also illustrated.

This paper reviews recent advances in the circulation dynamics of the Kuroshio and its interaction with shelf currents in the East China Sea (ECS). The annually averaged Kuroshio volume transport varies between 19 and 24 Sv, based on different observations, but there is no consensus on which season its volume transport peaks. The Kuroshio is intensified over the central slope of the ECS from that off the northeast of Taiwan. The total Kuroshio intrusion into the ECS shelf is estimated to be 1.3–1.4 Sv, deduced from the observed volume transport of exchange flow in the Taiwan and Tsushima Straits, based on the assumption of volume conversation over the shelf. However, the uncertainty regarding this estimation remains due to the absence of sufficient observations and understanding of the Kuroshio dynamics. The Kuroshio intrusions over the shelf off the northeast of Taiwan and southwest of Kyushu are stimulated by planetary or topographic β -effect associated with the alongshore variations in the ECS slope topography and altered by variations in the Kuroshio intensity, shear stress, and baroclinicity. Multilayered exchanges between the Kuroshio and shelf currents were found between 100- and 200-m isobaths along the central ECS slope. The spatial variations in these exchanges are governed by cross-isobath transport by geostrophy, whereas bottom Ekman transport may play a predominant role in altering the integrated exchange flow along the slope. Although the intrusion is greatly modulated along the path of the Kuroshio in the ECS by variable slope topography, there are few observations on the spatial variations of these exchange flows. The characteristics and variations in the circulation and hydrographic properties of waters between 100- and 200-m isobaths significantly determine the general ECS circulation, about which consensus has still not been attained.

Purpose: Existing productivity improvements activities such as inventory buffer, overall equipment effectiveness (OEE) and total productive maintenance (TPM) do not analytically associate the throughput shortage with process parameters. The paper aims to develop and test an integrated model to recover the throughput shortage through process parameters adjustment in a semiconductor assembly. Design/methodology/approach: The mathematical model of planned throughput is developed as a function of input parameters in an integrated multiple process line. When the planned throughput does not meet the real-time throughput, the throughput shortage occurs. The planned throughput for the next day is summed with the throughput shortage from previous day and mathematical programming is used to find the optimum process parameter values. Findings: The throughput shortage is restored using mathematical programming for the subsequent day of planning. If there still exist throughput shortage, the additional throughput shortage will be carried forward for the subsequent day of planning where mathematical programming is used again to find the adjusted process parameters. The proposed optimization model in essentially a parametric model using input parameters of real-time data based on a normal distribution that is translated into a range of minimum and maximum using 95% confidence interval. Research limitations/implications: The adjusted input parameters in this model is based on the cycle time of three processes which are Die Attach, Wire Bond and Pre-Cap Inspection. Downtime and setup time are not subjected to adjustments. Practical implications: The mathematical programming computes optimum process parameters values to restore the throughput shortage where it correlates the process parameters and throughput shortage quantitatively rather than conventional method of throughput shortage recovery. Originality/value: The research addresses the process parameter adjustment to recover the throughput shortage in integrated multiple processes.

The adjustment problem of a process, which creates a scatter and a drift on the characteristics of its products, is extremely common in industry. Although old, it still has no satisfactory solution. To solve it, this problem is first formulated on a direct variational model, which it is always possible to establish for such a process. Control limits and a target are to be chosen for each of the product’s quality characteristics. An adjustment is sought if, at least, one characteristic of the last product is outside its limits. It is calculated by optimizing a problem of contracting the limits containing the characteristics towards their targets. Applied to a simulated production of one hundred and fifty workpieces, the “boundary contraction adjustment” results in two times less adjustments than the method of “minimum mean squared error” and, especially, makes it possible to control, automatically, the quality of all the characteristics.

As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

Numerous reversible data hiding schemes have been proposed with the goal of ensuring both security and availability of the original image. However, in the case of medical images, there is a risk of information leakage and image distortion, which must be avoided. To address this issue, an innovative reversible data hiding scheme based on circular distance interpolation utilizing optimal pixel adjustment with error substitution has been proposed in this paper. This approach aims to conceal confidential information within an image while preserving its visual quality, enhancing capacity, improve security and achieve reversibility. Before embedding, the secret data has been scrambled using Arnold transform technique to enhance security of the proposed scheme. In circular distance interpolation, a circle has been drawn taking interpolated pixel as center with different radius. Data hiding capacity has been increased by extending the reference pixels of a circular area specially in complex regions. Then identify all reference pixels from the circular region and embed secret data bits into the interpolated pixels. Finally, substitution error, optimal pixel adjustment process and input value replacement strategies are applied on intermediate stego image to get a high imperceptible stego image. The proposed data hiding scheme has been measured using standard evaluation metrics such as PSNR(dB), payload (bpp) and SSIM using different circular areas with higher values than existing interpolation based reversible data hiding schemes. The proposed phenomenon highlighted the significant and valuable features of hidden data communication research, which includes the ability to ensure hidden communication, which are crucial in our modern high-tech lifestyle. This approach has many advantages and benefits for various sectors such as private organizations, government agencies, and the public.

To generate advanced properties for the wear resistance and fatigue life of components and allow for an improved, application-oriented development of part specifications, a precisely tailored initial machining or manufacturing process is necessary. In addition, it is important to know how subsequent machining steps or operational loads affect the components’ condition. Residual stresses are a meaningful measurand for evaluating the modifications that a machining process induces into the material. The desired modifications should be specified regarding the final state for the required operational behavior. Thus, the stability of the modifications can be considered so that they can be beneficial in service. This investigation is part of fundamental research in the field of the Collaborative Research Center (CRC) “Process Signatures”. By applying defined selected loads, the effects on machined surface layers are investigated since machined components are exposed to further loads during use. For this reason, experimental process chains are applied in this work to grind-strengthened specimens as possible application cases and corresponding loads. These experimental process chains consist of defined mechanical and thermal loads, which are applied to the specimens using a thermal and mechanical testing system. Furthermore, it is investigated how these additional loads affect the modifications previously introduced by the grinding process. The influence of the additional loads is evaluated by using radiographic and electron microscopic examinations. It can be observed that the sequence, as well as the type of the applied loads, play a significant role in the development of the modifications.