Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Consequences Of The “fit” Between Management Accounting And Control Systems And The Environment

We examine the performance consequences of the fit between environmental factors surrounding publicly-listed firms in China's emerging economy, and the firms' use of, and emphasis on management accounting and control systems (MCS): formal planning and budget controls, participative budgeting and performance evaluation, strategic performance measures and rewards, and socialization practices. We use archival data and survey data collected from senior- and middle-level managers in 158 publicly-listed Chinese firms. We find robust and consistent evidence that more extensive use of MCS is associated with higher concurrent and lagged accounting (return on assets) and market (market return) performance, and lower market beta. More importantly, we find robust and consistent evidence for the alignment hypothesis that less - but not more - extensive use of MCS than predicted by the firms' market competition and growth opportunities adversely affects performance, both subjective-based (assessed by senior-level managers) and accounting- and market-based. Our results suggest that a misfit between the firms' environmental characteristics and their MCS practices hurts the firms' performance. We discuss the implications of our findings, and provide some directions for future research.
A biomechanical comparison between cement packing combined with extra xation and three- dimensional printed strut-type prosthetic reconstruction for giant cell tumor of bone in distal femur

Li Min (  jacky-min@163.com ) West China School of Medicine: Sichuan University West China Hospital Xin Hu West China School of Medicine: Sichuan University West China Hospital Minxun Lu West China School of Medicine: Sichuan University West China Hospital Yuqi Zhang West China School of Medicine: Sichuan University West China Hospital Yitian Wang West China School of Medicine: Sichuan University West China Hospital Chongqi Tu West China School of Medicine: Sichuan University West China Hospital
Lagrange-Euler Closed Dynamics Model of the Baxter Robot

The dynamic model of a robot is important to find the relation between the joint actuator torques and the resulting motion. There are two common methods to do this: The Lagrange formulation, which gives a closed form of the dynamic equations, and the Newton-Euler method, which uses a recursive form. Presented in this paper is a formulation of the Lagrange-Euler (L-E) equations representing the dynamics of the Baxter manipulator. These equations are then verified against torque trajectories recorded from the Baxter robot. Experimental studies show that torques generated using the L-E method closely match recorded actuator torques. All of Baxter’s kinematic and dynamic parameters are presented here for easy future reference, and the full symbolic dynamics are made available online for closed loop analysis by the community.
E�cient Prediction of Bridge Conditions using Modi�ed Convolutional Neural Network

Artificial Intelligence (AI) technology has proved itself as a proficient substitute for classical techniques of modeling. AI is a branch of computer science with the help of which machines and software with intelligence similar to humans can be developed. Many problems related to structural as well as civil engineering are exaggerated with uncertainties that are difficult to be solved using traditional techniques. AI proves advantageous in solving these complex problems. Presently, a comprehensive model based on the Convolutional Neural Network technique of Artificial Intelligence is developed. This model is advantageous in accurately predicting the structure of a bridge without the need for actual testing. The firefly algorithm is used as a technique for accurate feature selection. The database is taken from national bridge inventory (NBI) using internet sources. Different performance measures like accuracy, recall, precision, and F1 score are considered for accurate prediction of the bridge structure and also provide
Designing Investing Apps to Promote Healthier Trading Habits in Retail Investors

Retail investors often make poor investing decisions due to behavioral factors. In recent years, smart-phone apps such as Robinhood that promise to “democratize investing” have risen in popularity. These apps have allowed retail investors to trade stocks, options, and other securities easily and inexpensively, often commission-free. It seems plausible that the design patterns of these new apps may significantly influence trading behaviors of their users. For instance, since underperformance is caused by behavioral, rather than informational, factors, making trading easier or increasing access to information can be coun-terproductive. But so far, there is little formal design guidance on how such apps should be designed. This thesis starts to fill this gap in design guidance by making two overarching contributions. First, it introduces a set of design guidelines for trading platforms based on fundamental results from related fields that may encourage healthier investing behaviors. Second, it presents a prototype for Robinhood’s Forest , an “interpassive” trading application based on these principles. The thesis also discusses design implications and opportunities for future design.
Signature Scheme with Access Control

A wide variety of digital signature schemes currently exist, from RSA to El-Gamal to Schnorr. More recently, multi-party signature schemes have been developed, including distributed signature schemes and threshold signature schemes. In particular, threshold signature schemes provide useful functionality, in that they require the number of participating parties to pass a threshold in order to generate a valid signature. However, they are limited in their complexity, as they can only model a threshold function. The proposed signature scheme (monotonic signature scheme) allows for the modeling of complex functions, so long as they are monotonic. This would allow for a much greater degree of access control, all while security and correctness are preserved.
The Nuts and Bolts of Building FlowLens

The recent adoption of programmable switching devices has enabled the deployment of efficient packet processing primitives in large-scale, high-speed networks. These capabilities have sparked a consistent effort from the research community to perform network security tasks in such switches with the goal of decreasing reaction times to threats and reducing costs associated with equivalent centralized server-based infrastructures. Unfortunately, existing solutions [20, 23, 41] target specific security-driven tasks and cannot accommodate ML tasks that perform targeted flow classification based on packet size or inter-packet frequency distributions.
Platform Embedded Security Technology Revealed: Safeguarding the Future of Computing with Intel Embedded Security and Management Engine

Platform Embedded Security Technology Revealed is an in-depth introduction to Intels platform embedded solution: the security and management engine. The engine is shipped inside most Intel platforms for servers, personal computers, tablets, and smartphones. The engine realizes advanced security and management functionalities and protects applications secrets and users privacy in a secure, light-weight, and inexpensive way. Besides native built-in features, it allows third-party software vendors to develop applications that take advantage of the security infrastructures offered by the engine. Intels security and management engine is technologically unique and significant, but is largely unknown to many members of the tech communities who could potentially benefit from it. Platform Embedded Security Technology Revealed reveals technical details of the engine. The engine provides a new way for the computer security industry to resolve critical problems resulting from booming mobile technologies, such as increasing threats against confidentiality and privacy. This book describes how this advanced level of protection is made possible by the engine, how it can improve users security experience, and how third-party vendors can make use of it. It's written for computer security professionals and researchers; embedded system engineers; and software engineers and vendors who are interested in developing new security applications on top of Intels security and management engine. Its also written for advanced users who are interested in understanding how the security features of Intels platforms work. What youll learn The cyber security challenges behind the creation of the embedded security and management engine, and the solutions it presents The pros and cons of enforcing security in the embedded engine Basic cryptography and security infrastructure of the engine Security-hardening features of the engine Handling dynamically loaded applications How anonymous authentication works with enhanced privacy protection Content protection at the hardware level Secure boot with a hardware root of trust Firmware-based TPM Identity protection with a hardware-based, one-time password Who this book is for Computer security professionals and researchers; embedded system engineers; software engineers and vendors who are interested in developing new security applications on top of Intels security and management engine; OEM (such as Lenovo, HP, etc.) marketing and R&D staff.
Encoding Dictionary Feature for Deep Learning-based Named Entity Recognition

: Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.
Control Circuit Overhead for Stateful Memristor Logic

—Memristors are considered as a potential replacement of CMOS transistors in light of their low switching energy, high density and non-volatility. These two-terminal devices have been shown to be versatile and efficient for implementing logical, neuromorphic, and in-memory computing systems. However, memristor-based computing circuits require complex sequential operations that are controlled by overhead circuity, and these control circuits have not been considered in previous efficiency analyses of memristor logic. Here we thoroughly analyze the overhead circuitry for memristor logic and evaluate the actual area, power, and delay consumptions for complete memristor logic systems. This evaluation includes a horizontal comparison between the original IMPLY-based memristor logic to pure-CMOS logic in implementing standard logic gates and a one-bit full adder. The results show that when the overhead control circuitry is considered, memristor logic is ten orders of magnitude less efficient than conventional CMOS logic.
Cell Culture-Based Assessment of Toxicity and Therapeutics of Phytochemical Antioxidants

Plant-derived natural products are significant resources for drug discovery and development including appreciable potentials in preventing and managing oxidative stress, making them promising candidates in cancer and other disease therapeutics. Their effects have been linked to phytochemicals such as phenolic compounds and their antioxidant activities. The abundance and complexity of these bio-constituents highlight the need for well-defined in vitro characterization and quantification of the plant extracts/preparations that can translate to in vivo effects and hopefully to clinical use. This review article seeks to provide relevant information about the applicability of cell-based assays in assessing anti-cytotoxicity of phytochemicals considering several traditional and current methods.
Regional thermal optimization of Astrangia poculata and Oculina arbuscula

Climate change poses an imminent threat to the marine environment and is predicted to cause thermal extremes. We aim to understand if there is a correlation between adaptations of Astrangia poculata and Oculina arbuscula populations and their thermal performances under cold temperature stressors. This study wants to determine how colonies vary in thermal performance based on different geographic locations. The dark respiration and net photosynthesis of the corals were measured in a cold ramp using oxygen probes in respiration chambers, and we used photo analysis to assess coral status before and after the experiment. Our results showed that different populations of the corals had different respiration and net photosynthesis at different temperatures, and that the thermal performance curve of each population did not have a significantly different overall shape. Specifically, corals at higher latitudes had higher net photosynthesis at lower temperatures. By better understanding the regional differences in thermal performance, we can better focus our conservational efforts on coral communities in less variable climates in order to mitigate the effects of climate change in these more vulnerable populations.
On Phase Transitions in the Approximation Ratio for MAX 2-SAT

In the Boolean Maximum 2-Satisﬁability (MAX 2-SAT) problem, we consider a Boolean formula F in conjunctive normal form with clauses that contain only two literals each, and ask for the maximum number of clauses that can be satisﬁed in F . MAX 2-SAT is an NP-complete problem, and it has been identiﬁed that it undergoes a phase transition where the hardness shifts from underconstrained to overconstrained. The hardness of MAX 2-SAT depends on the clause to variable ratio c , such that MAX 2-SAT undergoes a phase transition when c = 1. Meanwhile, there exist approximation algorithms to estimate solutions to MAX 2-SAT in polynomial time using relaxations. One such relaxation involves the use of semideﬁnite programs (SDP), in which the algorithm optimizes over the cone of positive semideﬁnite matrices, or matrices with nonnegative eigenvalues. Semideﬁnite programming was ﬁrst incorporated into an approximation algorithm for MAX 2-SAT by Goemans and Williamson, which produces approximations such that the exact solution is at least 0.87856 times the SDP upper bound. Yet, despite our knowledge of MAX 2-SAT’s behavior over diﬀerent regions of constrainedness, less can be said about how the ratio between the exact solution and the approximated solution, or the approximation ratio, changes over these regions. Thus, we studied how the approximation ratio changes over c to identify if there exists a phase transition for the approximation ratio. Ultimately, we found that the approximation ratio approaches 1 as MAX 2-SAT becomes more constrained. In addition, when the SDP upper bound is restricted such that it cannot produce solutions greater than the number of clauses in an instance, there is an easy-hard-easy pattern for the approximation ratio. We also found that when the negation of variables in MAX 2-SAT instances are “unbalanced,” such that variables are negated with a probability of 32%, the rate of convergence between the exact solution and upper bound is faster and the approximation ratio is greater for larger c .
Prediction of some geotechnical properties of deltaic clays: using regression analysis models

Received 24 November 2021 Accepted 02 January 2022 Establishment of any engineering structure on a compressible clay leads to settlement and/or failure through this clay. The amount of this settlement is controlled mainly by the compressibility parameters, while the failure by the undrained strength. The measuring of compressibility parameters and undrained strength are expensive, cumbersome, and time-consuming; therefore several attempts have been made to predict these geotechnical parameters by using simple, cheap, and time-saving index properties of the soil. In this study, the performance of applying empirical equations for the estimation of these critical geotechnical parameters was assessed by using a database including 551 undisturbed samples of the Nile Delta clay. The regression analysis results depict statistically moderate to strong correlations exist between both compression index and recompression index and the bulk density, Atterberg limits, initial void ratio, and effective overburden pressure. The precompression stress, overconsolidation ratio, and undrained shear strength parameters have no remarkable correlations with these index properties, except the effective overburden pressure parameter that has moderate to strong correlations with these properties. The compressibility and strength parameters depend on the in-situ effective stress, stress during geological history, diagenesis and cementation process, mineralogy, microstructure, and texture of clay, therefore they provide meaningful relationships. The developed empirical equations in this study, which derived from various linear and multi-variables regression models, are reliable and capable to predict the geotechnical design parameters with high prediction performance. Keywords
Prevalence of DSM-III-R Psychiatric Disorder in a Sample of Canadian Women

This paper presents the initial fmdings from the East York Health Needs Study, an in-depth study of the incidence and correlates of psychiatric disorder in a community sample of Canadian women. Our main objectives were to determine prevalence rates of psychiatric disorder, to examine patterns of use of general and speciality health care services, and to assess the match between needs for psychiatric care and available services. We derived the prevalence figures for psychiatric disorder using threshold scores from two standard screening instruments calibrated to a structured clinical interview. A secondary goal was to determine which of the two instruments, the General Health Questionnaire (GHQ; Goldberg, 1972) or the Center for Epidemiological Studies Depression scale (CESâ€”D;Radloff, 1977), is best suited for screening psychiatric disorder in a Canadian com munity sample. The GHQ was developed in a British population, and has since been applied in a variety of European and Commonwealth countries, but has been less popular in North America. European (but not North American) researchers have invested considerable effort in establishing the utility of the GHQ in specific populations. Researchers in North America have tended to adopt the cut-off values recommended by Goldberg (1972) or others as their criterion for caseness, without validating the GHQ within their own populations. As the sensitivity and specificity estimates derived in one population do not auto matically translate to another, the accuracy of prevalence rates based on criteriaderived from other cultures may be questionable. It is essential, therefore, to assess the suitability of the GHQ as a psychiatric screening instrument in our population. The CESâ€”Dwas developed for a general study of depressive symptoms (Comstock & Helsing, 1976; Radloff, 1977), and has been used successfully with community samples across the US (Myers & Weissman, 1980; Roberts & Vernon, 1983; Schulberg eta! , in press). Unlike the GHQ, the CES-D has not become established internationally, but has generally been used in North American epidemiological studies of depressiveillness. This may be because the CES-D has a narrow focus on symptoms of depression, and therefore lends itself readily to specific DSMâ€”IIIâ€”R criteria. The GHQ, in contrast, has a broader scope and assesses symptoms of anxiety and social! somatic distress as well as depression. However, the CESâ€”D has been found in one study (Breslau, 1985)to detect symptoms associated with generalised anxiety disorder equally as well as depression. This finding raises the possibility that the CESâ€”Dmay be a more broadly useful instrument than previously thought.
An extended formulation for the Constraint Routing and Spectrum Assignment Problem in Elastic Optical Networks *

The emergence of Elastic Optical Networks allowed a more ﬂexible spectrum allocation for routing traﬃc demands within telecommunication networks. From this context arises the Routing and Spectrum Assignment problem, which consists of routing a given set of origin-destination traﬃc demands and assigning them to contiguous spectrum frequencies such that no frequency slot is assigned to more than one demand within a network link. This work deals with the variant where each demand route must additionally satisfy a maximal-length constraint. In this paper we propose a compact extended formulation for the Constrained Routing and Spectrum Assignment Problem. We show that our extended formulation is theoretically stronger than formulations known in the literature. Experimental results demonstrate the eﬃciency of our approach.
The Function of the Hypothalamicâ€”Pituitary-Adrenal Axis in Alzheimer's Disease Response to Insulin Hypoglycaemia

Background.To investigatean associationbetween HPA axis dysfunction,depressionand cognitive impairment,we assessedsubjectswith mild Alzheimer'sdisease (AD). Method. Sixteen non-depressed subjects with ADaccording to NINCDS/ADRDAcriteria and 18 normalcontrolsunderwentthe insulinhypoglycaemia(IH) test and the dexamethasone suppressiontest (DST). Results.TheAD subjectsshoweda bluntedresponseof adrenocorticotrophic hormone(ACTH) to lH compared with controls (P= 0.019). ACTH response (area under curve) correlated with a scorefor cognitiveability (CAMCOG) (r=0.64, P< 0.01). AD subjectshad a shortertime to peak cortisol level than controls (P= 0.004), although total cortisol response was normal. Conclusions. The AD subjects show evidence of adrenal hyper-responsiveness and normal immediate (rate-sensitive) glucocorticoid feedback. An association between HPA axis dysfunctionand organicbrainpathologyin AD subjectsmay be mediatedby cell lossin the hippocampus.