Miracle-dz/UniFAR-FTUs huggingface dataset:
Málaga Costa del Sol airport and its new conceptualization of hinterland

The concept of hinterland is changing with a globalized economy, new needs between airports, stakeholders and the tourist destination challenge new dimensions of operation in the territory. Identifying new factors and actors in the influence zone of the hinterland will allow us to stage the importance of airports in the regional economy and the positive effects derived from these. The aim of this paper is to analyse the hinterland of Málaga Costa del Sol airport and its territorial and economic dimensions. Moreover, to provide an updated and clearer definition of hinterland, assuming future implications for airport operators, management of tourist destination by destination marketing organizations and scholars and practitioners interested in this topic. The results revealed that Málaga’s airport is modifying the hinterland of airport and its area of influence in economic and urban development terms.
Online Developmental Research: Observations from Moderated Studies

The use of online tools for data collection has become increasingly prevalent in developmental research in recent years. While remote developmental studies offer significant advantages, adapting lab-based studies to remote conditions is a challenging process and requires researchers to consider various ethical, organizational, methodological and technical issues. The aim of this article is to facilitate this process by offering suggestions about some of these issues that arise when conducting online developmental research, focusing mainly on studies involving moderated sessions with infants and children. Different aspects of conducting remote developmental studies including ethics, participant recruitment, appointment scheduling, preparation of the materials and the set-up and execution of the live sessions are covered. Potential challenges associated with different stages are discussed with suggested solutions.
Study on Microstructure, Mechanical Properties and Erosion Characteristics of Al-Si Alloy Manufactured by Continuous Casting Direct Rolling Process

Al-Si alloys exhibit promising wear resistance, thus being mainly employed to weld Al alloy parts and processed into components of equipment. During the new continuous casting direct rolling (CCDR) process, the raw material gradually cools and solidifies, simultaneously plastically deformed. Hence, the materials manufactured through the CCDR process presented an unparalleled microstructure. The experimental results indicated that the strength of the CCDR Al-Si alloy can be increased through cold rolling. A two-stage heat treatment (solid solution and aging treatment) was introduced to improve the ductility and satisfy the industrial application. Furthermore, the erosion wear characteristics and fracture mechanism of the CCDR Al-Si alloy dominated by the ductility were confirmed. Both cold rolling specimens (FR) and those with heat treatment (FRH) showed greater wear resistance than as-manufactured (F). The FR specimens exhibited greater wear resistance owing to a higher Al matrix strength at a lower impact angle; on the other hand, at a higher impact angle, the FRH specimens with a softer Al matrix presented better wear resistance due to the formation of a lip structure to reduce material removal. The TEM results confirmed that the nanoscale grains formation was induced in the erosion-affected region and affected the Si concentration. Conclusively, the heat-treated CCDR Al-Si alloy possessed excellent erosion resistance and workability, which can serve as a reference processed as wear-resistant mechanical parts.
Heritage Management: Analytical Study of Tourism Impacts on the Archaeological Site of Umm Qais—Jordan

The archaeological site of Umm Qais is a popular tourist destination for both local and foreign tourists who come to appreciate the site’s archaeological history, scenic landscape, and panoramic perspective. The site was the focus of tourism planning, which included the construction of amenities and infrastructure, the creation of tourist circuits, and archaeological management. This development was linked to a rise in visitor numbers as well as the provision of a high level of service, such as parking, tickets, kiosks, restaurants, and cafés, to welcome visitors. The purpose of this study was to examine the impacts of on-site tourist services and infrastructures, as well as those of visitors, and their geographical and temporal scope on the site. The study used a qualitative approach based on case study fieldwork as a research method to achieve this goal. Personal observation, interviews with site-related stakeholders, and a checklist were used to collect data during the fieldwork. Both tourism infrastructure and visitors were proven to have a detrimental influence on tourist attractions. The site’s aesthetic pollution and structural deterioration were caused by tourism services and infrastructure. Graffiti, vandalism, and trash left by visitors exerted strong negative impacts. Furthermore, spatial and temporal negative impacts were determined by the patterns of seasonal movement of visitors and the location of infrastructure. Thus, most of the impacts were concentrated in a small portion of the site, among the western theater, the panoramic view, and the traditional Ottoman village. This research sheds light on these challenges and makes recommendations in the areas of heritage management, tourism, and visitor impact management that may be of interest to on-the-ground decision makers as well as academics.
Observational Analysis on the Early Evolution of a CME Flux Rope: Preflare Reconnection and Flux Rope’s Footpoint Drift

We study the early evolution of a hot-channel-like magnetic flux rope (MFR) toward eruption. Combining with imaging observation and magnetic field extrapolation, we find that the hot channel possibly originated from a preexisting seed MFR with a hyperbolic flux tube (HFT). In the precursor phase, three-dimensional tether-cutting reconnection at the HFT is most likely resulting in the heating and buildup of the hot channel. In this process, the forming hot channel was rapidly enlarged at its spatial size and slipped its feet to two remote positions. Afterward, it instantly erupted outwards with an exponential acceleration, leaving two core dimmings near its feet. We suggest that preflare reconnection at the HFT played a crucial role in enlarging the seed MFR and facilitating the onset of its final solar eruption. Moreover, a recently predicted drifting of MFR’s footpoints was detected at both core dimmings. In particular, we find that MFR’s west footpoint drift was induced by a new reconnection geometry among the erupting MFR’s leg and thereby inclined arcades. As MFR’s west footpoints gradually drifted to a new position, a set of newborn atypical flare loops connected into the west core dimming, causing a rapid decrease of dimmed area inside this core dimming and also generating a secondary flare ribbon at their remote feet. This reveals that core dimmings may suffer a pronounced diminishment due to the eruptive MFR’s footpoint drift, implying that mapping the real footpoints of the erupting MFR down to the Sun’s surface is more difficult than previously thought.
Approximate Representer Theorems in Non-reflexive Banach Spaces

The representer theorem is one of the most important mathematical foundations for regularised learning and kernel methods. Classical formulations of the theorem state sufficient conditions under which a regularisation problem on a Hilbert space admits a solution in the subspace spanned by the representers of the data points. This turns the problem into an equivalent optimisation problem in a finite dimensional space, making it computationally tractable. Moreover, Banach space methods for learning have been receiving more and more attention. Considering the representer theorem in Banach spaces is hence of increasing importance. Recently the question of the necessary condition for a representer theorem to hold in Hilbert spaces and certain Banach spaces has been considered. It has been shown that a classical representer theorem cannot exist in general in non-reflexive Banach spaces. In this paper we propose a notion of approximate solutions and approximate representer theorem to overcome this problem. We show that for these notions we can indeed extend the previous results to obtain a unified theory for the existence of representer theorems in any general Banach spaces, in particular including $l_1$-type spaces. We give a precise characterisation when a regulariser admits a classical representer theorem and when only an approximate representer theorem is possible.
Unique episodic groundwater recharge event in a South American sedimentary aquifer and its long‐term impact on baseflow

The anomalous entrance of water into groundwater systems can affect storage throughout long periods and normally relies on infrequent and irregular pulses of groundwater recharge defined by the term episodic recharge. Recently there was a groundwater recharge of large magnitude with unknown circumstances in the Caiuá aquifer. This unique event was explored in detail here and allowed to better understand the occurrence of such events in humid subtropical climates in South America. For this study, groundwater monitoring daily data from the Integrated Groundwater Monitoring Network was used combined with a specific yield obtained from geophysical wireline logging to obtain groundwater recharge rates. To improve the investigation, we also used a baseflow separation method to obtain the groundwater contribution into local rivers. The groundwater storage variations were also assessed by remote sensing with the GRACE data. Results showed the importance of high soil moisture storage on the occurrence of large episodic recharge events. We estimated that the groundwater recharger volumes derived from 1 year that included the unique episodic recharge observed (total of 866 mm for April 2015–March 2016) were comparable with the sum of 7 years of groundwater recharge (total of 867 mm). Atypical rainfall in winter periods were responsible for the increase in soil moisture that explained that unique event. GRACE‐based GWS showed concordance detecting the occurrence of the unique episodic recharge. However, the variation in terms of volumes obtained by GRACE does not represent the behaviour observed in the aquifer by the WTF method. The results also indicated that changes in aquifer storage caused by episodic recharge events directly affect low flows in rivers over long periods. The main knowledge gap addressed here relates to exploring a unique episodic recharge event quite rare to observe with its long‐term impacts on hydroclimatic variability over a humid subtropical portion of the Caiuá aquifer.
Magnetic Polymer Models for Epigenetics-Driven Chromosome Folding

Epigenetics is a driving force of important and ubiquitous phenomena in nature such as cell differentiation or even metamorphosis. Oppositely to its widespread role, understanding the biophysical principles that allow epigenetics to control and rewire gene regulatory networks remains an open challenge. In this work we study the effects of epigenetic modifications on the spatial folding of chromosomes – and hence on the expression of the underlying genes – by mapping the problem to a class of models known as magnetic polymers. In this work we show that a first order phase transition underlies the simultaneous spreading of certain epigenetic marks and the conformational collapse of a chromosome. Further, we describe Brownian Dynamics simulations of the model in which the topology of the polymer and thermal fluctuations are fully taken into account and that confirm our mean field predictions. Extending our models to allow for non-equilibrium terms yields new stable phases which qualitatively agrees with observations in vivo. Our results show that statistical mechanics techniques applied to models of magnetic polymers can be successfully exploited to rationalize the outcomes of experiments designed to probe the interplay between a dynamic epigenetic landscape and chromatin organization.
Criminal Histories of Intimate Partner Homicide Offenders

This exploratory study investigates intimate partner homicide (IPH) offenders’ criminal histories to examine opportunities for criminal justice system intervention. Data were collected from the Michigan Violent Death Reporting System and Michigan State Police for the 117 IPHs committed in 2014 and 2015 in Michigan. Descriptive statistics on types of criminal charges and convictions for the 103 IPHs committed by aggressors (e.g., not in self-defense) are presented, with relatively few domestic violence charges or convictions. Twenty-nine percent of offenders had no criminal history. Findings highlight a need for more effective and greater implementation of interventions for both criminal justice system-involved and not-involved offenders.
Lignocellulosic Corn Stover Biomass Pre-Treatment by Deep Eutectic Solvents (DES) for Biomethane Production Process by Bioresource Anaerobic Digestion

The valorization study of the largely available corn stover waste biomass after pretreatment with deep eutectic solvent (DES) for biomethane production in one-liter glass bioreactors by anaerobic digestion for 21 days was presented. Ammonium thiocyanate and urea deep eutectic solvent pretreatments under different conditions in terms of the components ratio and temperature were examined on corn stover waste biomass. The lignocellulose biomass was characterized in detail for its chemistry and morphology to determine the effect of the pretreatment on the natural biocomposite. Furthermore, the implications on biomethane production through anaerobic digestion with different loadings of corn stover biomass at 35 g/L and 50 g/L were tested. The results showed an increase of 48% for a cumulative biomethane production for a DES-pretreated biomass, using a solid-to-liquid ratio of 1:2 at 100 °C for 60 min, which is a strong indication that DES-pretreatment significantly enhanced biomethane production.
An extension of fast iterative shrinkage‐thresholding algorithm to Riemannian optimization for sparse principal component analysis

Sparse principal component analysis (PCA), an important variant of PCA, attempts to find sparse loading vectors when conducting dimension reduction. This paper considers the nonsmooth Riemannian optimization problem associated with the ScoTLASS model 1 for sparse PCA which can impose orthogonality and sparsity simultaneously. A Riemannian proximal method is proposed in the work of Chen et al. 9 for the efficient solution of this optimization problem. In this paper, two acceleration schemes are introduced. First and foremost, we extend the FISTA method from the Euclidean space to the Riemannian manifold to solve sparse PCA, leading to the accelerated Riemannian proximal gradient method. Since the Riemannian optimization problem for sparse PCA is essentially nonconvex, a restarting technique is adopted to stabilize the accelerated method without sacrificing the fast convergence. Second, a diagonal preconditioner is proposed for the Riemannian proximal subproblem which can further accelerate the convergence of the Riemannian proximal methods. Numerical evaluations establish the computational advantages of the proposed methods over the existing proximal gradient methods on a manifold. Additionally, a short result concerning the convergence of the Riemannian subgradients of a sequence is established, which, together with the result in the work of Chen et al., 9 can show the stationary point convergence of the Riemannian proximal methods.
Fundamentals of Density Functional Theory: Recent Developments, Challenges and Future Horizons

Density Functional Theory (DFT) is a powerful and commonly employed quantum mechanical tool for investigating various aspects of matter. The research in this field ranges from the development of novel analytical approaches focused on the design of precise exchange-correlation functionals to the use of this technique to predict the molecular and electronic configuration of atoms, molecules, complexes, and solids in both gas and solution phases. The history to DFT’s success is the quest for the exchange-correlation functional, which utilizes density to represent advanced many-body phenomena inside one element formalism. If a precise exchange-correlation functional is applied, it may correctly describe the quantum nature of matter. The estimated character of the exchange-correlation functional is the basis for DFT implementation success or failure. Hohenberg-Kohn established that every characteristic of a system in ground state is a unique functional of its density, laying the foundation for DFT, which is being utilized to explore the novelty of materials. This chapter is aimed to present an overview of DFT by explaining the theoretical background, commonly used approximations as well as their recent developments and challenges faced along-with new horizons.
The Prevalence of Metabolic Syndrome of Patients on Treatment with Haloperidol and Risperidone or Olanzapine

Background: The background of the work stems from the observation due to the prevalence of antipsychotic drugs in causing metabolic disorder. The metabolic disorder is one of the major concern of antipsychotic drugs for schizophrenic patients. The present study is mainly aimed to establish the role of haloperidol and risperidone or olanzapine in causing metabolic syndrome of schizophrenic patients. Methods: Sixty-four schizophrenic patients were divided into two groups. First and second group received haloperidol and risperidone or olanzapine respectively for three months. Body mass index, random blood sugar and lipid profile were investigated before and after the treatment. Results: Both groups of schizophrenic patients have shown a significant increase in the BMI, random blood sugar and lipid profile indicating a propensity to cause metabolic disorder. Conclusion: From the present study it is concluded that there is a high prevalence of metabolic disorder those who received antipsychotic drugs like haloperidol and risperidone or olanzapine.
Preliminary development and validation of the perceived strengths-based human resource system scale

PurposeThe purpose of the current article was to propose the strengths-based human resource (HR) system construct as well as develop and validate the perceived strengths-based HR system scale by using three independent studies.Design/methodology/approachStudy 1 mainly adopted exploratory factor analysis to test whether fifteen items proposed by the authors can represent the perceived strengths-based HR system construct. The aim of Study 2 was to examine the discriminant validity and criteria validity of the fifteen-item perceived strengths-based HR system scale and reliability of this scale. By structural equation modeling analysis, Study 3 primarily tested the incremental predictive validity of the perceived strengths-based HR system for employee performance (i.e. task performance and innovative behavior) after controlling for the perceived high-performance work system (HPWS) and perceived high-commitment work system (HCWS). FindingsStudy 1 showed that initial fifteen items of the perceived strengths-based HR system appropriately are loaded on one factor and exhibit a good reliability. Study 2 found that there is good discriminant validity between the perceived strengths-based HR system, perceived organizational support, perceived supervisory career support, and work engagement, and the perceived strengths-based HR system exhibits better convergent validity and criteria validity. Study 3 demonstrated that the perceived strengths-based HR system could significantly predict employee performance (i.e. task performance and innovative behavior) even after controlling for perceived HPWS and HCWS.Originality/valueThe current article contributes to advancing HR theory and research and provides a valuable tool for future empirical research on the strengths-based HR system.
The Response of Some Rubber Seedling (Hevea brasiliensis) Clones on Various Types of Indigenous Mycorrhizae in Ex-Coal Mined Soil Media

The former coal mining activities caused marginal lands, which generally neglect land. Various activities are needed to rehabilitate the ground, especially technology that is effective and environmentally friendly. Mycorrhizae technology is an alternative strategy that should try and develop to optimize rubber plants' growth and development in the area. This experiment aims to get the best indigenous mycorrhizae types for each type of rubber clone in the ex-coal mining land as a planting media. This experiment is a two-factor factorial experiment using a Completely Randomized Design of three replications: The first factor, mycorrhizae isolate, consists of five levels: without AMF inoculation, Glomus sp-3, Glomus sp-6, Glomus sp-15 , and Glomus sp-16 inoculation. The second factor, the type of rubber clone, consists of RRIM-300, GT-1, PB-260, and RRIC-100. The results showed an interaction between the typical isolate and rubber clones' styles in enhancing rubber seedlings in the leading nurseries. The best growth for RRIM-300 and GT-1 rubber clones in rubber seedlings which inoculated by Glomus sp-6 , whereas in PB-260 and RRIC-100 clones in Glomus sp-16 .
DLA: Dense-Layer-Analysis for Adversarial Example Detection

In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even showed superhuman capabilities in a broad range of domains. This led people to trust in DNN classifications even in security-sensitive environments like autonomous driving. Despite their impressive achievements, DNNs are known to be vulnerable to adversarial examples. Such inputs contain small perturbations to intentionally fool the attacked model. In this paper, we present a novel end-to-end framework to detect such attacks without influencing the target model's performance. Inspired by research in neuron-coverage guided testing we show that dense layers of DNNs carry security-sensitive information. With a secondary DNN we analyze the activation patterns of the dense layers during classification run-time, which enables effective and real-time detection of adversarial examples. Our prototype implementation successfully detects adversarial examples in image, natural language, and audio processing. Thereby, we cover a variety of target DNN architectures. In addition to effectively defending against state-of-the-art attacks, our approach generalizes between different sets of adversarial examples. Our experiments indicate that we are able to detect future, yet unknown, attacks. Finally, during white-box adaptive attacks, we show our method cannot be easily bypassed.
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms

In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they do not consider the real dynamic environments (e.g., dynamic tasks, dynamic workers), so they may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and the future rewards and can be updated quickly to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework.