Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Entrepreneurial Orientation and Total Quality Management on Organizational Performance of Pharmaceutical SMEs in Thailand with moderating role of Organizational Learning

Performance is a serious concern for manufacturing SMEs, especially for pharmaceutical SMEs in Thailand. Specifically, pharmaceutical SMEs face challenges regarding the enhancing performance by application of total quality management and entrepreneurial orientation. This study envisioned to assess the entrepreneurial orientation and total quality management on organizational performance of pharmaceutical organizations in Thailand. This study also aimed to test the moderating role of organizational learning. To study the proposed association of variables, the data was gathered from the owners and managers of Thai pharmaceutical small and medium firms. This study concluded that entrepreneurial orientation and total quality management are the factors that significantly enhance the performance of pharmaceutical organizations in Thailand. Findings of the study demonstrate that organizational learning significantly moderate the relationship of TQM with performance of pharmaceutical organizations but it does not moderate the link of entrepreneurial orientation. The theoretical contribution of the present study lies in its use of organizational learning as a moderator of the relationships of entrepreneurial orientation, total quality management, and pharmaceutical SMEs’ performance.From the practical perspective, the key contribution of this study is that pharmaceutical SMEs in Thailand may clearly appreciate the benefits of devoting greater attention to the implementation of entrepreneurial orientation, total quality management, and organisational learning to attain a sustained competitive advantage
The Implications of Social Trust on Corporate Social Responsibility: International Evidence

This study examines how social trust influences corporate social responsibility (CSR) around the world. Finance theory predicts that an important determinant of firm performance stems from corporate culture or social trust originating from shared norms and beliefs within the organization. Within this line of research, we expect that a higher level of social trust will lessen the likelihood of harmful misunderstandings while facilitating a reduction in the agency problem, thereby increasing the probability of cooperation and meaningful CSR activities. Using a sample of firms from 26 countries over the period of 2002-2018, we find a positive relationship between a firm’s CSR engagement and societal trust. This finding is robust to accounting for various CSR measures, cross-sectional analyses, and difference-in-difference tests. We also demonstrate economic mechanisms that can explain how trust affects CSR activities. Our findings imply that trust enables greater stakeholder cooperation therefore enhancing CSR performance. JEL Classification: G18; G31; G32
Toxicological and Mutagenic Effects of Particulate Matter from Domestic Activities

People spend most of their time indoors, particularly in their houses where daily activities are carried out, enhancing particulate matter (PM) emissions with consequent adverse health impacts. This study intended to appraise the toxicological and mutagenic responses of particulate matter with a diameter less than 10 μm (PM10) released from cooking and ironing activities under different conditions. The cytotoxicity of the PM10 total organic extracts was tested in A549 cells using the WST-8 and the lactate dehydrogenase (LDH) assays, while the interference in cell cycle dynamics and reactive oxygen species (ROS) production was analysed by flow cytometry. The S. typhimurium TA98 and TA100 Ames tester strains with and without metabolic activation were employed to determine the mutagenic potential of the PM10-bound polycyclic aromatic hydrocarbons (PAHs). PM10 organic extracts decreased the metabolic activity of A549 cells; however, no effects in the LDH release were observed. An increase in ROS levels was registered only for cells treated with PM10 at IC20 from steam ironing, in low ventilation conditions, while cell cycle dynamics was only affected by exposure to PM10 at IC20 from frying horse mackerel and grilling boneless pork strips. No mutagenic effects were observed for all the PM10-bound PAHs samples.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

Virtual and augmented reality (VR/AR) are immersive and potentially multimodal sensory experiences that augment or completely replace real-world sensory input with artificial content. Outside commercial applications in entertainment, VR and AR can be used to create controlled, yet ecologically valid experimental paradigms for research in cognitive science or related fields. Here, we review how brain–computer interfaces (BCIs) are being integrated with VR/AR to rehabilitate and assist the injured and disabled, improve interaction between human and computer, and provide us more insight into how our brains process and evaluate complex environments and events. We also describe a concrete example architecture for conducting BCI/VR experiments and conclude our review with a discussion on limitations and potential future developments on BCI-based interaction with VR and AR.
Diverse Impacts of the Indian Ocean Dipole on El Ni ñ o–Southern Oscillation

: Understanding the impact of the Indian Ocean dipole (IOD) on El Ni ñ o–Southern Oscillation (ENSO) is important for climate prediction. By analyzing observational data and performing Indian and Paciﬁc Ocean pacemaker experiments using a state-of-the-art climate model, we ﬁnd that a positive IOD (pIOD) can favor both cold and warm sea surface temperature anomalies (SSTA) in the tropical Paciﬁc, in contrast to the previously identiﬁed pIOD–El Ni ñ o connection. The diverse impacts of the pIOD on ENSO are related to SSTA in the Seychelles–Chagos thermocline ridge (SCTR; 60 8 –85 8 E, 7 8 –15 8 S) as part of the warm pole of the pIOD. Speciﬁcally, a pIOD with SCTR warming can cause warm SSTA in the southeastern Indian Ocean, which induces La Ni ñ a–like conditions in the tropical Paciﬁc through interbasin interaction processes associated with a recently identiﬁed climate phenomenon dubbed the ‘‘warm pool dipole. ’’ This study identiﬁes a new pIOD–ENSO relationship and examines the associated mechanisms.
The Informativeness of Mandatory Cash Flow Forecasts in Financially Distressed Firms

We study the informativeness of mandatory disclosure of cash flow forecasts to bond holders of financially distressed firms. Exploiting a unique data set from Israel, spanning the period from 2010 to 2017, we utilize a propensity score matching approach to estimate the bond price reaction to mandatory cash flow forecasts in treated vs. non-treated firms. We find that bond holders of distressed firms perceive the cash flow forecasts as credible and informative, and react positively to higher forecasts. These results are robust to different specifications of the matching procedure, to first time cash flow forecasts and to the inclusion of various fixed effects. These results are in contrast with prior findings regarding voluntary forecasts, which were found to be less reliable in financially distressed firms (Rogers and Stocken, 2005). Consistent with prior literature, we find that the results are driven by non-investment grade bonds, and that the bond reaction is not reversed in subsequent periods. Moreover, forecasts estimated from a naïve model do not yield any bond reaction, suggesting that management forecasts convey relevant information that cannot be easily obtained from other sources. Other components of the cash flow forecast do not seem to convey additional information to bond holders. Finally, conditioning on firms undergoing reorganization, we find that firms with cash-flow forecast disclosure, in the year in which reorganization commenced, were associated with between 11 to 15 percent higher recovery rates relative to reorganizations that commenced in years with no forecast disclosure. which is consistent with the regulation objectives of signaling early warnings and preceding onset of bankruptcy procedures. Overall, our paper contributes to the literature on the provision of financial information to bond holders by showing that forward - looking information that is regulated and disclosed mandatorily provides useful information to bond holders of companies that are in financial distress.
Criminal court fees, earnings, and expenditures: A multi-state RD analysis of survey and administrative data*

Millions in the United States face financial sanctions in the criminal court system each year, totaling over $27 billion in overall criminal debt. In this study, we leverage five distinct natural experiments across multiple states using regression discontinuity designs to evaluate the causal impact of these sanctions. We consider a range of long-term outcomes including employment, recidivism, household expenditures, and other self-reported measures of well-being, measured using a combination of administrative records on earnings and employment, the Criminal Justice Administrative Records System, and household surveys. We find consistent evidence across the range of natural experiments and subgroup analyses of precise null effects on the population, ruling out long-run impacts larger than +/-3.6% on total earnings and +/4.7% on total recidivism. These results are inconsistent with theories of criminal debt poverty traps but also do not justify using financial sanctions for local revenue or as a crime control tool.
A Mixed Intrusion Detection System utilizing K-means and Extreme Gradient Boosting

The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed model called K-means-XGBoost consisting of K-means and (Extreme Gradient Boosting, XGBoost) algorithms. The distributed computation of our method is achieved in Spark platform to rapidly separate normal events and anomaly events. In phrases of accuracy, DR, F1-score, recall, precision, and error indices FAR, the proposed model’s performance is measured via the well-known dataset of NSL-KDD. The experimental outcomes indicate that our method is outstandingly better among accuracy, DR, F1-score, training time, and processing speed, compared to other models which are recently created. In particular, the accuracy, F1-score, and DR of the proposed model can achieve as high as 93.28%, 94.39%, and 99.22% in the NSL-KDD dataset, respectively.
Nonstochastic Contextual Combinatorial Bandits

We study a contextual version of online combinatorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round. As a result of its action, the learner incurs a loss that is a bi-linear function of the context vector and the vector representation of the chosen action. We consider two natural versions of the problem: semi-bandit where the losses are revealed for each component appearing in the learner’s combinatorial action, and full-bandit where only the total loss is observed. We design computationally efficient algorithms based on a new loss estimator that takes advantage of the special structure of the problem, and show regret bounds order √ T with respect to the time horizon. The bounds demonstrate polynomial scaling with the relevant problem parameters which is shown to be nearly optimal. The theoretical results are complemented by a set of experiments on simulated data.
Social bricolage and business model innovation: a framework for social entrepreneurship organizations

PurposeThe last two decades have witnessed a surge of interest in social entrepreneurship organizations (SEOs). Understanding their business models is crucial for sustaining their long-term growth. This paper analyses how SEOs that use the approach of social bricolage adapt their business model to develop social innovation.Design/methodology/approachThis study used in-depth multiple comparative case studies and narrative analysis to focus on the South of Italy, where these ventures play a crucial role in the entrepreneurial process of minor and abandoned cultural heritage sites, generating economic and social value and employment opportunities.FindingsBy developing a conceptual framework, this paper enhances current understanding of the social dimensions of SEOs’ business model. These ventures using the approach of social bricolage can produce social innovation, reinventing and innovating their business model. The business model innovation of the cases revealed a strong social mark and identified peculiar strategies that both respond to social needs and long-term sustainability in complex contexts.Practical implicationsThis study connects previous knowledge on social bricolage with the business model innovation, highlighting routines and processes used by ventures, and provides a starting point for social entrepreneurs and innovators in the complex and often uncertain cultural domain of the Third Sector in Italy.Originality/valueThe paper aims to contribute to the literature on SEOs by exploring their main features and social dimensions. By combining social bricolage and business model innovation, it offers a novel conceptual framework for developing social innovation and for the study of SEOs.
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness

Adversarial robustness is a critical property of classiﬁers in applications as they are increasingly deployed in complex real-world systems. Yet, achieving accurate adversarial robustness in machine learning remains a persistent challenge and the choice of the surrogate loss function used for training a key factor. We present a family of new loss functions for adversarial robustness, smooth adversarial losses , which we show can be derived in a general way from broad families of loss functions used in multi-class classiﬁcation. We prove strong H -consistency theoretical guarantees for these loss functions, including multi-class H -consistency bounds for sum losses in the adversarial setting. We design new regularized algorithms based on the minimization of these principled smooth adversarial losses ( PSAL ). We further show through a series of extensive experiments with the CIFAR-10, CIFAR-100 and SVHN datasets that our PSAL algo-rithm consistently outperforms the current state-of-the-art technique, TRADES , for both robust accuracy against (cid:96) ∞ -norm bounded perturbations and, even more signiﬁcantly, for clean accuracy. Finally, we prove that, unlike PSAL , the TRADES loss in general does not admit an H -consistency property.
Vibrational and thermodynamic properties of GeSe in the quasiharmonic approximation

Finite-temperature properties such as thermodynamic state functions can be obtained for a range of crystalline materials by combining density functional theory (DFT) with lattice-dynamics approaches. Despite the usefulness of such first-principles predictions, their results must be carefully checked for accuracy, especially in cases where the DFT description of the material itself is nontrivial. Here, we investigate a prototypical layered semiconductor, namely, germanium selenide (GeSe) by dispersion-corrected DFT, lattice-dynamics computations, and a thermodynamic framework that relies on the quasiharmonic approximation (QHA). We study phonon band structures, their evolution under pressure, and finite-temperature thermodynamic state functions. Besides the layered orthorhombic structure, this analysis includes the high-temperature cubic (rocksalt-type) polymorph of GeSe, which is shown to exhibit imaginary vibrational modes but emerging dynamic stability under increasing external pressure. The effect of these imaginary vibrational modes on the QHA and hence on computed thermochemical properties is critically evaluated. First-principles thermodynamics correctly predict a high-temperature transition from the orthorhombic to the cubic structure, albeit the transition temperature is severely underestimated. This simple compound allows us to address important methodological questions regarding the QHA treatment of crystalline solids. © 2014 American Physical Society.
Saving and Attitudes to the Future Saving and Attitudes to the Future

Abstract How are variations in inter-temporal preferences or planning behavior linked to inequality among households in long-term outcomes, such as savings, marriage or health? We use a simple regression method to develop an index of "future-orientedness" based on how an individual’s reported attitudes in the 1970s predict wealth many years later. Our results suggest that variation in planning matters more for future net worth than discount rates. We find that this index also has statistically significant effects on offspring savings and on non-financial choices such as the timing of children.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

Play, that is, self-motivated activities for enjoyment, is a significant aspect for human development and essential to learning and skill acquisition. Games, the structured form of play, are increasingly being used in brain–computer interface (BCI) and neurofeedback (NF) applications. In BCI and NF applications, patterns of the users’ brain activation are assessed in real time and fed back to the users. When users become successful in modulating their own brain activation, improvements in behavior, cognition, or motor function follow or they are able to control external devices such as a computer, wheelchair, or neuroprosthe-sis. In electroencephalogram-based applications, however, a large number of users cannot attain control over their own brain signals. Current approaches to attaining control require lengthy repetitive trainings. The use of games and game-like feedback aims at keeping user motivation and engagement high over time. This chapter provides an overview of existing game-like feedback modalities and critically discusses their potential value and also possible drawbacks in BCI and NF applications.
The relationship between isokinetic knee extensor and flexor muscle strength and vertical jump performance in university rugby union players of the North-West University

Introduction: Rugby is a professional sport which places emphasises on strength, power, speed and endurance. Therefore the accurate assessment of rugby performance is very important for sports and exercise therapists to enable peak performance for the players. Lower limb strength is often tested by means of isokinetic testing, whilst functional power is tested by means of the vertical jump test. Sophisticated equipment used in the measurement of performance indicators, are often not available in smaller communities and rural areas. A good correlation between laboratory testing and functional testing could be of great value for determining performance in less fortunate communities. Aim of Study: To determine the relationship between isokinetic strength testing of the quadriceps and hamstring muscle groups and vertical jump performance in rugby players. Methods: Fifty one male, rugby players who were part of the Varsity Cup Tournament in (2011) participated in this study. Ethical approval was given by the Human Research Ethics Committee (HREC) of the University of Witwatersrand. Height and weight were measured. Isokinetic knee extensor and flexor strength was tested (Biodex system 4 dynamometer TM ) at 60 /sec (5 repetitions), 180 /sec (10 repetitions) and 300 /sec (15 repetitions). A single leg vertical jump was done using the Vertec and the performance was assessed as maximal height jump in centimetres. Descriptive statistics (mean and standard deviation) are reported for all outcomes. The SPSS software (IBM. SPSS version 21) was used for analysis. A Pearson's Product Moment Correlation coefficient was calculated between the isokinetic parameters of quadriceps and hamstring muscles and the vertical jump height and power (watts). An independent t-test and a paired t-test were used to calculate the differences between the forward and backline players and the dominant (Dom) and non-dominant (ND) legs. Results: The mean age of the participants was 21.4 ± 1.2 years; they were 1.83 ± 7.4m tall and weighed 99.3 ± 13.8kg. There were 26 forward players (mean height of 1.88 ± 7.2m and mean weight of 109.9 ± 10.3kg) and 25 backline players (mean height of 1.80 ± 5.5m and mean weight of 88.3 ± 6.1kg). The forwards were significantly taller and heavier than the backline players (p = 0.0001). The forwards produced significant greater peak power in the vertical jump than the backline players in both the dominant and non-dominant legs (p=0.0001). The PT (Nm) decreased as the angular velocity increased from 60 /sec to 300 /sec. A significant negative correlation (r = 0.313; p = 0.025) between peak torque (PT) concentric strength from the hamstrings at 60o/sec and the vertical jump height on the non-dominant side were found. Quadriceps PT to vertical power at the higher velocities showed significant correlations at 180o/sec (dominant: r = 0.294; p = 0.011)
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling

Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution in problems involving complicated PDEs. This is reflected in several recent studies on characterizing the"failure modes"of PINNs, although a thorough understanding of the connection between PINN failure modes and sampling strategies is missing. In this paper, we provide a novel perspective of failure modes of PINNs by hypothesizing that training PINNs relies on successful"propagation"of solution from initial and/or boundary condition points to interior points. We show that PINNs with poor sampling strategies can get stuck at trivial solutions if there are propagation failures, characterized by highly imbalanced PDE residual fields. To mitigate propagation failures, we propose a novel Retain-Resample-Release sampling (R3) algorithm that can incrementally accumulate collocation points in regions of high PDE residuals with little to no computational overhead. We provide an extension of R3 sampling to respect the principle of causality while solving time-dependent PDEs. We theoretically analyze the behavior of R3 sampling and empirically demonstrate its efficacy and efficiency in comparison with baselines on a variety of PDE problems.