Miracle-dz/UniFAR-FTUs huggingface dataset:
THERMAL BRIDGES : A TWO-DIMENSIONAL AND THREE-DIMENSIONAL TRANSIENT THERMAL ANALYSIS

Thermal bridges are parts of the building envelope where, due to the two-dimensional or three-dimensional character of the heat conduction, either the inside surface temperatures are rather low, which can cause condensation, or the heat losses are rather high. In this paper thermal bridges are analyzed by numerical methods, shortly described in the first section. They are based on energy balance techniques. Features of these models are their implementation on personal computers, the simple use and the graphical output by means of plots of isothermals and streamlines, permitting a direct evaluation of both aspects of thermal bridges. As shown in the next section, a two-dimensional steady-state analysis is sufficient to quantify both thermal bridge problems. The importance of thermal bridges in uninsulated or insulated cavity walls or massive walls is deduced. For example, for traditional insulated cavity wall constructions, the conductive heat losses exceed the onedimensionally calculated heat losses with 30% to 40%. The application of cavity insulation will not affect the condensation risk; it is also shown that with regard to this condensation risk, outer insulation of massive walls is much favorable than inner insulation. In a third section further study based on threedimensional and transient calculations shows some particular points of interest : the symmetry between an observed mold growth pattern and the calculated isothermal field, the influence of inside surface conductances, transient surface condensation, and the relativity of onedimensional heat loss calculations for complex-shaped buildings.
A Sequential Algorithm for Jerk Limited Speed Planning

In this article, we discuss a sequential algorithm for the computation of a minimum-time speed profile over a given path, under velocity, acceleration, and jerk constraints. Such a problem arises in industrial contexts, such as automated warehouses, where LGVs need to perform assigned tasks as fast as possible in order to increase productivity. It can be reformulated as an optimization problem with a convex objective function, linear velocity and acceleration constraints, and nonconvex jerk constraints, which, thus, represent the main source of the difficulty. While existing nonlinear programming (NLP) solvers can be employed for the solution of this problem, it turns out that the performance and robustness of such solvers can be enhanced by the sequential line-search algorithm proposed in this article. At each iteration, a feasible direction, with respect to the current feasible solution, is computed, and a step along such direction is taken in order to compute the next iterate. The computation of the feasible direction is based on the solution of a linearized version of the problem, and the solution of the linearized problem, through an approach that strongly exploits its special structure, represents the main contribution of this work. The efficiency of the proposed approach with respect to existing NLP solvers is proven through different computational experiments. Note to Practitioners—This article was motivated by the needs of LGV manufacturers. In particular, it presents an algorithm for computing the minimum-time speed law for an LGV along a preassigned path, respecting assigned velocity, acceleration, and jerk constraints. The solution algorithm should be: 1) fast, since speed planning is made continuously throughout the workday, not only when an LGV receives a new task but also during the execution of the task itself, since conditions may change, e.g., if the LGV has to be halted for security reasons and 2) reliable, i.e., it should return solutions of high quality, because a better speed profile allows to save time and even small percentage improvements, say a 5% improvement, has a considerable impact on the productivity of the warehouse, and, thus, determines a significant economic gain. The algorithm that we propose meets these two requirements, and we believe that it can be a useful tool for LGV manufacturers and users. It is obvious that the proposed method also applies to the speed planning problem for vehicles other than LGVs, e.g., road vehicles.
Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions

This work identifies major areas of knowledge and proposes a set of relevant dimensions by area that must be taken into account in the design and delivery of context-aware mobile applications for mental health interventions. We argue that much of the related research has focused only on a few dimensions, paying little or no attention to others and, most importantly, to potential relationships between them. Our belief is that the improvement of the effectiveness of mobile interventions to support mental health necessarily implies that developers and therapists comprehensively consider the interaction between the proposed dimensions. Taking as a starting point the three areas of knowledge (Technology, Context, and Mental Health), we re-examine each area to identify relevant dimensions, discuss the relationships between them and finally draw a series of considerations. The resulting considerations can help therapists and developers to devise, design, and generate custom mobile applications in a way that increases the motivation and engagement of patients and, therefore, the effectiveness of psychological treatments.
The economics of politically-connected firms

Political connections between firms and autocratic regimes are not secret and often even publicly displayed in many developing economies. We argue that tying a firm’s available rent to a regime’s survival acts as a credible commitment forcing entrepreneurs to support the government and to exert effort in its stabilization. In return, politically-connected firms get access to profitable markets and are exempted from the regime’s extortion. We show that such a gift exchange between government and politically-connected firms can only exist if certain institutional conditions are met. In particular, the stability of the regime has to be sufficiently low and the regime needs the power to exploit independent firms. We also show that building up a network of politically-connected firms acts as a substitute for investments in autonomous stability (such as spending on military and police force). The indirect strategy of stabilizing a regime via politically-connected firms gradually becomes inferior when a regime’s exploitative power rises.
Polyglycerolester as Sticker Penetrant Adjuvants

The effect of adjuvants on the biological efficacy of agrochemical sprays is related to improving the surface activity of pesticide formulations. By improving adhesion and retention of spray solution droplets the uptake of the active ingredients through the cuticular waxes is increased. Today new adjuvants also need to comply with eco-toxicological, safety and sustainability criteria. This paper presents a new class of benign adjuvants based on polyglycerolesters to improve the efficacy of pesticide formulations for tank-mix and in-can applications. Polyglycerolesters are natural derived and biodegradable. The performance of adjuvants based on polyglycerolester, hydrophilic and hydrophobic, and their influence on adhesion, retention, foliar uptake and drift control is presented. Field results confirm the enhancement of the biological efficacy of pesticides. The paper demonstrates that safe and environmentally friendly adjuvants are already available for formulator and consumer acceptance.
International Symposium on Power Electronics for Distributed Generation Systems ( PEDG ) , Charlotte , USA Synthesizing Distributed Energy Resources in Microgrids with Temporal Logic Specifications

Grid supportive (GS) modes integrated within distributed energy resources (DERs) can improve the frequency response. However, synthesis of GS modes for guaranteed performance is challenging. Moreover, a tool is needed to handle sophisticated specifications from grid codes and protection relays. This paper proposes a model predictive control (MPC)-based mode synthesis methodology, which can accommodate the temporal logic specifications (TLSs). The TLSs allow richer descriptions of control specifications addressing both magnitude and time at the same time. The proposed controller will compute a series of Boolean control signals to synthesize the GS mode of DERs by solving the MPC problem under the normal condition, where the frequency response predicted by a reduced-order model satisfies the defined specifications. Once a sizable disturbance is detected, the pre-calculated signals are applied to the DERs. The proposed synthesis methodology is verified on the full nonlinear model in Simulink. A robust factor is imposed on the specifications to compensate the response mismatch between the reduce-order model and nonlinear model so that the nonlinear response satisfies the required TLS.
A Multiple Legs Inverter with Real Time–Reflected Load Detection Used in the Dynamic Wireless Charging System of Electric Vehicles

Dynamic wireless power transfer is a potentially effective method to solve issues related to the range anxiety of electric vehicles (EVs) and reduce the cost of on-board batteries. A novel multiple legs inverter topology with a reflected load identification method for dynamic EV charging is proposed in this paper. In the proposed circuit topology, several inductor-capacitor-capacitor (LCC) reactive power compensation resonant networks and primary pads are selectively excited through a sole primary converter. Besides, a high-response and simple method for the reflected load identification is proposed to rapidly and precisely detect the EV’s position, providing accurate power regulation reference to the converter. With the proposed method, the system can realize high-response and closed-loop power control precisely without any additional wireless communication and position detection devices. Simulation and experimental results verified the efficiency of the proposed scheme. Additionally, the cost comparison results reveal that the proposed scheme could reduce costs by nearly 78% in comparison with the conventional scheme.
A Statistical Method for Detecting Move, Stop, and Noise: A Case Study with Bus Trajectories

The proliferation of devices with positioning capability has allowed new possibilities for studies and applications in the context of urban mobility. However, the process of analyzing raw trajectories poses several challenges. In this work, we investigate one of the main tasks in this process of trajectory analysis: detecting stops from GPS trajectories. Stops can reveal interesting behavior aspects of a moving object such as its daily routine, bottlenecks in traffic jams, or visiting times of touristic places. Although there are some efforts in this direction, most current methods ignore the presence of noise segments, which typically occur many times in trajectories. In this sense, we present a method that exploits gaps in time and space to identify episodes of movement, stop, and periods where some classification is inconclusive, which we define as noise. In addition, our method does not rely on contextual information as opposed to some current solutions, which make our proposal also suitable for trajectories recorded in free space. We compare our method to the state of the art highlighting its advantages in terms of manipulating noise, supporting spatial filtering and being independent of external resources. Moreover, we conduct an experimental evaluation using a large-scale bus dataset to show the effectiveness of our method in a real application scenario.
Formulation and Evaluation Of Metformin HCl Mouth Dissolving Tablet Using Sublimating Agent

The present study was focused on the development of orodispersible tablets of Metformin HCl for improving patient compliance, especially pediatric and geriatric categories by sublimation technique and comparing the super disintegrating property of benzoic acid and camphor . The other ingredients used in the formulations are crosspovidone, xylitol, magnesium stearate, talc and directly compressible mannitol to enhance the mouth feel. The total seven batches were prepared (F1–F6) and F7 (without superdisintegrant). Tablets were evaluated for weight variation, friability, hardness, drug content uniformity, in-vitro disintegration and dissolution studies. Among all the formulated tablets F3 which is based on Metformin Hcl with 40 mg benzoic acid was found to be the highest dissolution (92.56%) in 10 mins. From the dissolution result it is clear that the benzoic acid at different concentration showed better dissolution rate as a disintegrant as compare to camphor. Hence, benzoic acid was a good alternative as a disintegrant for the preparation of directly compressible mouth dissolving tablets of Metformin Hydrochloride.
Interaction of an Oscillating Flexible Plate and Nucleate Pool Boiling Vapor Bubble: Fluid-Structure Interaction in a Multimaterial Multiphase System

Many of the real world problems in engineering and science involve interactions of multiple materials and phases. The numerical modeling of such system should be able to handle the material and phase multitude to capture physics of problems. Here we present our initial results for introducing fluid-structure interaction for flexible geometries in our multimaterial multiphase fluid solver, which also captures phase change phenomena such as solidification and boiling. The multimaterial approach applies a hybrid moment of fluid and level set algorithm to handle material domains. The fluid-structure interaction method is a force-feedback algorithm developed for a flexible fiber geometry. Numerical simulations are performed to evaluate this approach, and as an application, study the effects of an oscillating flexible/rigid plate on a single-bubble nucleate pool boiling process.
A Novel Approach Canberra Measure Minimal Spanning Tree Using Fuzzy C-Means Based on Gaussian Function for Image Data Mining

Clustering analysis has been an emerging research issue in data mining due to its variety of applications. In recently, mathematical algorithm supported automatic segmentation system plays an important role in clustering of images. The fuzzy c-means clustering is a method of cluster analysis which aims to partition n data points into k-clusters. The conventional FCM-based algorithm considers no spatial content information, which means it sensitive to noise. Unsupervised techniques need to be employed, which can be based on minimal spanning tree generated by comparing spatial neighbourhood information, the MST based clustering algorithms have been widely used due to their ability to detect clusters with irregular boundaries. We propose an automatic fuzzy c-means initialization algorithm based on Canberra distance minimal spanning tree for the purpose of segmentation of medical images, where vertices and edges are labelled with multi-dimensional vectors. A Canberra distance measure based, construct the minimal spanning tree clustering algorithm. An efficient method for calculating membership and updating prototypes by minimizing the new objective function of Gaussian based fuzzy c-means. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the dataset in order to find the proper number of cluster at each level. In this algorithm to apply medical images to reduce the inhomogeneity and allow the labelling of a pixel to be influenced by the labels in its immediate neighbourhood and reduces the time complexity and better clustering results than the existing traditional minimal spanning tree algorithm. The performance of proposed algorithm has been shown with random data set, partition coefficient and validation function are used to evaluate the validity of clustering and then new cluster separation approach to optimal number of clustering. Also this paper compares the results of proposed method with the results of existing basic fuzzy c-means.
Instationary drift-diffusion problems with Gauss–Fermi statistics and field-dependent mobility for organic semiconductor devices

This paper deals with the analysis of an instationary drift-diffusion model for organic semiconductor devices including Gauss–Fermi statistics and application-specific mobility functions. The charge transport in organic materials is realized by hopping of carriers between adjacent energetic sites and is described by complicated mobility laws with a strong nonlinear dependence on temperature, carrier densities and the electric field strength. To prove the existence of global weak solutions, we consider a problem with (for small densities) regularized state equations on any arbitrarily chosen finite time interval. We ensure its solvability by time discretization and passage to the time-continuous limit. Positive lower a priori estimates for the densities of its solutions that are independent of the regularization level ensure the existence of solutions to the original problem. Furthermore, we derive for these solutions global positive lower and upper bounds strictly below the density of transport states for the densities. The estimates rely on Moser iteration techniques.
Requirement for preclinical prioritization of neuroprotective strategies in stroke: Incorporation of preconditioning.

Acute neuroprotection in numerous human clinical trials has been an abject failure. Major systemic-and procedural-based issues have subsequently been identified in both clinical trials and preclinical animal model experimentation. As well, issues related to the neuroprotective moiety itself have contributed to clinical trial failures, including late delivery, mono-targeting, low potency and poor tolerability. Conditioning (pre-or post-) strategies can potentially address these issues and are therefore gaining increasing attention as approaches to protect the brain from cerebral ischemia. In principle, conditioning can address concerns of timing (preconditioning could be pre-emptively applied in high-risk patients, and post-conditioning after patients experience an unannounced brain infarction) and signaling (multi-modal). However, acute neuroprotection and conditioning strategies face a common translational issue: a myriad of possibilities exist, but with no strategy to select optimal candidates. In this review, we argue that what is required is a neuroprotective framework to identify the "best" agent(s), at the earliest investigational stage possible. This may require switching mindsets from identifying how neuroprotection can be achieved to determining how neuroprotection can fail, for the vast majority of candidates. Understanding the basis for failure can in turn guide supplementary treatment, thereby forming an evidence-based rationale for selecting combinations of therapies. An appropriately designed in vitro (neuron culture, brain slices) approach, based on increasing the harshness of the ischemic-like insult, can be useful in identifying the "best" conditioner or acute neuroprotective therapy, as well as how the two modalities can be combined to overcome individual limitations. This would serve as a base from which to launch further investigation into therapies required to protect the neurovascular unit in in vivo animal models of cerebral ischemia. Based on these respective approaches, our laboratories suggest that there is merit in examining synaptic activity-and nutraceutical-based preconditioning / acute neuroprotection.
PHOTONIC CRYSTAL FIBRE LASER FOR ELECTRON BEAM

We present the successful development of a new fibre laser system for a laser-wire electron beam emittance diagnostic, suitable for intra-train scanning in an ILCspecification environment. The system is based on the amplification of a commercial Ytterbium (Yb) fibre laser with a repetition rate of 6.49MHz in rod type Yb doped photonic crystal fibre (PCF). We demonstrate world leading laser performance, with amplification of pulses from 1.5J to 268J and a beam quality factor M < 1.1, in 1ms bursts at 2Hz.
Motor activation in people with profound intellectual and multiple disabilities in daily practice

ABSTRACT Background People with profound intellectual and multiple disabilities (PIMD) are at risk of being motorically inactive. This study investigated the degree and type of motor activation in daily practice and its relationship to personal and contextual factors. Method A total of 58 participants with PIMD participated in the study. Data concerning the motor activation were collected by means of a diary over a period of 14 days. Relationships to personal and contextual factors were analysed using multilevel analyses. Results The mean number of transfers was 3.1 times per day (range: 0–9, SD = 1.4), the mean number of relocations was 7.7 times per day (range: 2–13, SD = 2.5), and the mean number of motor activities offered was 1.5 per day (range: 0–10, SD = 1.9). Relationships to age, gender, location, and day of week were found. Conclusion Motor activation seems to be a minor part of the support provided to people with PIMD.
Sustained Cognitive and Economic Benefits of Resistance Training Among Community- Dwelling Senior Women: A 1-Year Follow-up Study of the Brain Power Study

not have been systematically collected or reported, raising issues of ascertainment bias. This issue is even more challenging because all patients had cognitive impairment. Second, study discontinuation rates were high and there was not a planned, rigorously conducted period of off-drug follow-up. In addition, our analysis updates the published findings from study 078, since a subsequent investigation suggested that these were incomplete. Implications for individuals who used rofecoxib are unclear, since it was withdrawn from the market nearly 5 years ago. To our knowledge, no studies have been conducted to determine for how long the increased cardiovascular risk associated with rofecoxib use persists, to elucidate the mechanism, or to examine whether there are long-term risks after discontinuation of other nonsteroidal anti-inflammatory drugs, selective or nonselective. To improve drug safety evaluation within clinical trials, periods of off-drug surveillance should be used when appropriate to ensure observation of long-term effects.
112 Michalak1.qxp

Edyta Michalak, Maria Laurentowska, Barbara Pospieszna, Katarzyna Domaszewska, Rados3aw Rutkowski 1 Zak3ad Fizjologii, Akademia Wychowania Fizycznego, Poznañ 2 Zak3ad Fizykoterapii i Odnowy Biologicznej, Akademia Wychowania Fizycznego, Poznañ 1 Department of Physiology, The Eugeniusz Piasecki University School of Physical Education, Poznañ 2 Department of Physicotherapy and Biological Renewal, The Eugeniusz Piasecki University School of Physical Education, Poznañ
Unsupervised Approach towards Visual Saliency Modelling

Visual Salience refers to the most conspicuous regions of a visual scene. Visual salience modelling has gained enormous attention due to its wide range of applications in Robotics, Object segmentation and Object Tracking. Amongst the two variants of visual salience modelling, this work emphasises the bottom-up approach where the visual scenes or images are presented under free viewing conditions to track the salient regions. There have been several different approaches towards modelling visual salience starting from the earliest models based on feature extraction to the modern day deep learning. The recent deep learning models are supervised models which learn end-to-end from the input image to the saliency map. Though these models achieve reliable results, the major downside of these models is that they have very little or no relevance to the cognitive processes of the human brain. In this work, we propose an unsupervised learning model based on predictive processing [2] and information theory [1], to address the aforementioned drawback.