Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative Effect of Local Politics on Multifactor Leadership of Junior High School Principals: Empirical Facts from Indonesia

This research focuses on investigating and examining the influence of local politics on the multifactor leadership of junior high school principals. The method used is a quantitative survey, with the unit of analysis being principals and teachers as respondents from 15 districts (regencies and cities) in Lampung Province. The data collection instrument used was the standardized multifactor leadership questionnaire in the Indonesian version resulting from the translation from English by an expert in the subject and was then discussed in the Forum Group Discussion to get good readability items. The questionnaire was completed by 393 teachers as respondents in 15 districts. The collected data were examined using the SEM Amos Version 22. The results show that the score of local political influence on multifactor leadership is -0,333, transformational is -1,877, transactional is -2,061, and laissez-faire is -,333. The authors conclude that local politics negatively affects all latent variables and all dimensions of multifactor leadership. This research contributes to human resource management and leadership in maintaining and developing and mainstreaming effective management and leadership practices which in this case is multifactor leadership by reducing a disturbing factor: anxiety.
Photogrammetry of Asymmetric Microcavities

Optical response of asymmetric microcavities is determined by their external geometry. Here, we propose to perform a photogrammetric study of asymmetric microcavities as a feasible way to obtain the geometry. The entire process is simulated and the results are presented. Such a photogrammetric study will benefit both optical microcavity and photogrammetry applications.
Phase Interpolator-Based Clock and Data Recovery With Jitter Optimization

In this paper, it is proposed a jitter analysis methodology, targeting on the optimization of a phase interpolator (PI) based clock and data recovery circuit (CDR). The methodology is applied for the optimized design of an 8-bit dual-loop CDR, designed with the CMOS TSMC 65 nm process node. The CDR is based on an extended, in terms of phase resolution, version, with a novel PI topology proposed in this work. The proposed CDR loop has a minimum frequency offset tracking ability equal to 500ppm at 5.83 Gbps, and so is suitable for adoption either in mesochronous or plesiochronous High Speed Serial Interface (HSSI) receivers. It consumes 14.2 mW with 1 V supply voltage and is able to achieve better than 10−10 Bit Error Rate (BER) performance. The CDR loop performance verification has been realized through the AMS simulator of Analog Design Environment of Cadence, by co-simulations of the transistor level CDR circuit with the Verilog-AMS based jitter generator.
Vertical distribution of sources and sinks of volatile organic compounds within a boreal forest canopy

Abstract. The ecosystem–atmosphere flux of biogenic volatile organic compounds (BVOCs) has important impacts on tropospheric oxidative capacity and the formation of secondary organic aerosols, influencing air quality and climate. Here we present within-canopy measurements of a set of dominant BVOCs in a managed spruce- and pine-dominated boreal forest located at the ICOS (Integrated Carbon Observation System) station Norunda in Sweden, collected using proton-transfer-reaction mass spectrometry (PTR-MS) during 2014–2016 and vertical emission profiles derived from these data. Ozone concentrations were simultaneously measured in conjunction with these PTR-MS measurements. The main BVOCs investigated with the PTR-MS were isoprene, monoterpenes, methanol, acetaldehyde, and acetone. The distribution of BVOC sources and sinks in the forest canopy was explored using Lagrangian dispersion matrix methods, in particular continuous near-field theory. The forest canopy was found to contribute ca. 86 % to the total monoterpene emission in summertime, whereas the below-canopy and canopy emissions were comparable (ca. 42 % and 58 %, respectively) during the fall period. This result indicates that boreal forest litter and other below-canopy emitters are a principal source of total forest monoterpene emissions during the fall months. During night, our results for methanol, acetone, and acetaldehyde seasonally present strong sinks in the forest canopy, especially in the fall, likely due to the nighttime formation of dew on vegetation surfaces.
The fuzzy-IAVOA energy-aware routing algorithm for SDN-based IoT networks

—The Internet of Things (IoT) has rapidly grown in the past decade as an emerging technology. Due to the heterogeneity and energy limitations of IoT devices, adopting efficient management practices for developing IoT applications and managing IoT networks is a challenging task. One of the most critical IoT challenges that needs to be considered is routing due to its significant impact on energy consumption. Software Defined Networking (SDN) is a novel approach that decouples the control plane from the data plane, enabling network administrators to program and manage their networks more efficiently. This paper proposes an energy-aware routing mechanism for IoT networks by leveraging the capabilities of SDN. In the proposed method, the SDN controller has a global view of the network devices and establishes several optimal clusters in the IoT environment using Fuzzy logic. Then, the controller computes optimal routes by combining the Fuzzy logic system and the Improved African Vulture Optimization Algorithm (IAVOA). Applying this mechanism enables data packets to be routed through IoT devices with sufficient energy, leading to prolonged network lifetime and improved Quality of Service (QoS). Simulation results confirm that the proposed solution significantly improves energy efficiency and QoS in terms of packet delivery ratio.
Narrative Review of the Complex Interaction between Pain and Trauma in Children: A Focus on Biological Memory, Preclinical Data, and Epigenetic Processes

The incidence and collective impact of early adverse experiences, trauma, and pain continue to increase. This underscores the urgent need for translational efforts between clinical and preclinical research to better understand the underlying mechanisms and develop effective therapeutic approaches. As our understanding of these issues improves from studies in children and adolescents, we can create more precise preclinical models and ultimately translate our findings back to clinical practice. A multidisciplinary approach is essential for addressing the complex and wide-ranging effects of these experiences on individuals and society. This narrative review aims to (1) define pain and trauma experiences in childhood and adolescents, (2) discuss the relationship between pain and trauma, (3) consider the role of biological memory, (4) decipher the relationship between pain and trauma using preclinical data, and (5) examine the role of the environment by introducing the importance of epigenetic processes. The ultimate scope is to better understand the wide-ranging effects of trauma, abuse, and chronic pain on children and adolescents, how they occur, and how to prevent or mitigate their effects and develop effective treatment strategies that address both the underlying causes and the associated physiological and psychological effects.
MFIRA: Multimodal Fusion Intent Recognition Algorithm for AR Chemistry Experiments

The current virtual system for secondary school experiments poses several issues, such as limited methods of operation for students and an inability of the system to comprehend the users’ operational intentions, resulting in a greater operational burden for students and hindering the goal of the experimental practice. However, many traditional multimodal fusion algorithms rely solely on individual modalities for the analysis of users’ experimental intentions, failing to fully utilize the intention information for each modality. To rectify these issues, we present a new multimodal fusion algorithm, MFIRA, which intersects and blends intention probabilities between channels by executing parallel processing of multimodal information at the intention layer. Additionally, we developed an augmented reality (AR) virtual experiment platform based on the Hololens 2, which enables students to conduct experiments using speech, gestures, and vision. Employing the MFIRA algorithm, the system captures users’ experimental intent and navigates or rectifies errors to guide students through their experiments. The experimental results indicate that the MFIRA algorithm boasts a 97.3% accuracy rate in terms of interpreting users’ experimental intent. Compared to existing experimental platforms, this system is considerably more interactive and immersive for students and is highly applicable in secondary school experimental chemistry classrooms.
Perception-inspired background subtraction in complex scenes based on spatiotemporal features

Background subtraction (BGS) is a fundamental preprocessing step in most video-based applications. Most BGS methods fail to handle dynamic unconstrained scenarios accurately. This is because of overreliance on statistical model. In this paper, we develop a novel non-parametric sample-based background subtraction method. First, the background sample set is initialized by a clean sample frame rather than the first frame. This can avoid introducing a ghost when the first frame contains foreground objects. Here, we utilize the Gaussian mixture model to validate whether a pixel at the location is clean or not and construct the initialization of background model. Second, for an actual scenario with diversified environmental conditions (e.g., illumination changes, dynamic background), we employ normalized color space and a scale invariant local ternary pattern operator to handle these variations. In the meantime, in order to achieve high detection accuracy in the unconstrained scenarios without requiring any scenario-specific parameter tuning, we employ the perception-inspired confidence interval to modify the threshold in the color space. Third, the hole filling approach is used to reduce noise which comes from false segmentation, fill the blank area in the foreground region and maintain the integrity of foreground object. Our experimental results indicate that the proposed approach is superior to several state-of-the-art methods in terms of F-score and kappa index.
Suppressing Stimulated Brillouin Scattering Through Higher Order Mode Excitation in a Multimode Fiber

We numerically and experimentally demonstrate that spreading power into higher order modes of a multimode optical fiber can be used to suppress stimulated Brillouin scattering for a high-power narrow linewidth system. This opens the opportunity for suppressing nonlinear effects in fibers using adaptive mode launching techniques while retaining the ability to produce spatially coherent beams through control of the light phase.
Reconstruction par transformation parcimonieuse de solutions à alphabet fini de systèmes linéaires sous-déterminés

Nous considerons le probleme d'estimer un vecteur a alphabet fini a partir d'un systeme sous-determine y = Af , ou A est une matrice aleatoire generique reelle donnee de dimension n × N . Une methode originale par optimisation convexe est proposee pour reconstruire le vecteur par minimisation L1 . Cette methode est basee sur une transformation du probleme dans un domaine ou la solution recherchee est parcimonieuse. Le comportement theorique de cette methode est donne et illustree experimentalement.
Machine learning approaches to determining truck type from bridge loading response

The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.
Noninvasive monitoring of blood glucose concentration in diabetic patients with optical coherence tomography

Optical coherence tomography (OCT) has been used to noninvasively monitor the blood glucose concentration (BGC) in healthy subjects with good accuracy and acceptable specificity. Based on this, the paper further considered the possibility of OCT in noninvasive monitoring BGC in diabetic patients. The OCT signal slope (OCTSS) changed with variation of BGC. The correlation coefficient R between BGC and OCTSS in diabetic patients was 0.91; while the correlation coefficient R in healthy volunteers was 0.78. Thus, a better linear dependence of OCTSS on BGC in diabetic patients was presented in the experiment. The results showed that the capability and accuracy of OCT in noninvasive monitoring BGC of diabetic patients, and the noninvasive monitoring BGC in diabetic patients may be better than the monitoring in the healthy subjects.
High-performance electrochromic device based on nanocellulose/polyaniline and nanocellulose/poly(3,4-ethylenedioxythiophene) composite thin films

Abstract. With the development of nanotechnology, nanocomposite materials based on renewable resources are the focus of this research. Nanocellulose was prepared using sulfuric acid to swell cotton pulp, following with extensive ultrasonication. Nanocellulose/polyaniline (NC/PANI) and nanocellulose/poly(3,4-ethylenedioxythiophene) (NC/PEDOT) nanocomposites with core/shell structure were manufactured by in situ polymerization. The film-forming properties and electrochromic properties of PANI and PEDOT were significantly improved using the nanocellulose as matrix. NC/PANI and NC/PEDOT composite films were studied in single and dual electrochromic devices (ECDs). A viscous gel electrolyte (GE) was used in ECDs. The architectural design of single and dual device was ITO/NC-PANI/GE/ITO or ITO/NC-PEDOT/GE/ITO and ITO/NC-PANI/GE/NC-PEDOT/ITO, respectively. The dual ECD based on NC/PANI and NC/PEDOT composite films exhibited a higher color contrast (30.3%), shortest response time (1.5 s for bleaching and 1.9 s for coloring), largest coloration efficiency (241.6 C/cm2), and best cycling stability (over 150 cycles) compared with the single devices.
The Elements of Style: Aspiration and the Writing Life in the Early Years of Esquire

abstract:Much of the scholarship on Esquire has focused on the magazine's ideological fusion of masculinity and capitalism. Many scholars argue that Esquire's chief accomplishment was to create what was, at the time of its founding in 1933, an entirely new audience of male consumers. Informed by recent research on the philosophy of aspiration, this article expands these readings by recovering the magazine's 1930s-era interest in writing and authorship. Under the direction of editor Arnold Gingrich, Esquire was an agent of aspiration, promoting a romantic view of authorship that suggested that writing was an accessible pathway to stylish self-improvement.
Cypermethrin, deltamethrin, and hexachlorobenzene contents in milk and dairy products in Tehran, Iran

ABSTRACT The aim of this study was to determine pesticide residues in milk and dairy product samples from Tehran, Iran. A total of 50 samples (raw milk, ultra-high temperature milk, pasteurised milk, pasteurised and traditional butter, and pasteurised and traditional cheese) were randomly collected from June to August 2022, and their pesticide contents were examined through high-performance liquid chromatography. Out of the 50 samples, 38, 42, and 100% contained cypermethrin, deltamethrin, and hexachlorobenzene over the maximum residue limit set by the EU, respectively. Traditional butter had Cypermethrin and hexachlorobenzene means concentration of 75.64 ± 22.40 and 61.69 ± 17.78 µg/kg, respectively.
Towards non-classical walks with bright laser pulses

In the avid search for means to increase computational power in comparison to that which is currently available, quantum walks (QWs) have become a promising option with derived quantum algorithms providing an associated speed up compared to what is currently used for implementation in classical computers. It has additionally been shown that the physical implementation of QWs will provide a successful computational basis for a quantum computer. It follows that considerable drive for finding such means has been occurring over the 20+ years since its introduction with phenomena such as electrons and photons being employed. Principal problems encountered with such quantum systems involve the vulnerability to environmental influence as well as scalability of the systems. Here we outline how to perform the QW due to interference characteristics inherent in the phenomenon, to mitigate these challenges. We utilize the properties of vector beams to physically implement such a walk in orbital angular momentum space by manipulating polarization and exploiting the non-separability of such beams.
Estimation of Productivity in the KoreanElectricity Generation Sector:A Semi-Parametric Approach

This study estimated the level and growth of total factor productivity in the Korean electricity generation sector using plant-level panel data from over the period 2002-2019. In particular, we compared the productivity differences across different plant types. This study used a semiparametric estimation method that enables the control for the endogeneity of productivity shocks without using instrumental variables (IVs). It was found that steam power plants presented the highest total factor productivity, followed by internal combustion and combined cycle. The productivity growth fluctuated over time, ranging from -0.285% to 0.203%, and presented a declining trend during the sample period. The results also indicated that the load and plant factors significantly affected the total factor productivity of the plants. However, the age variable that may reflect the learning-by-doing effect did not affect plant productivity.
Forecasting Transitions in Digital Society: From Social Norms to AI Applications

: The use of AI and digitalization in many areas of everyday life holds great potential but also introduces signiﬁcant societal transitions. This paper takes a closer look at three exemplary areas of central social and psychological relevance that might serve as a basis for forecasting transitions in the digital society: (1) social norms in the context of digital systems; (2) surveillance and social scoring; and (3) artiﬁcial intelligence as a decision-making aid or decision-making authority. For each of these areas, we highlight current trends and developments and then present future scenarios that illustrate possible societal transitions, related questions to be answered, and how such predictions might inform responsible technology design.
Would frontal midline theta indicate cognitive changes induced by non-invasive brain stimulation? A mini review

To the best of our knowledge, neurophysiological markers indicating changes induced by non-invasive brain stimulation (NIBS) on cognitive performance, especially one of the most investigated under these procedures, working memory (WM), are little known. Here, we will briefly introduce frontal midline theta (FM-theta) oscillation (4–8 Hz) as a possible indicator for NIBS effects on WM processing. Electrophysiological recordings of FM-theta oscillation seem to originate in the medial frontal cortex and the anterior cingulate cortex, but they may be driven more subcortically. FM-theta has been acknowledged to occur during memory and emotion processing, and it has been related to WM and sustained attention. It mainly occurs in the frontal region during a delay period, in which specific information previously shown is no longer perceived and must be manipulated to allow a later (delayed) response and observed in posterior regions during information maintenance. Most NIBS studies investigating effects on cognitive performance have used n-back tasks that mix manipulation and maintenance processes. Thus, if considering FM-theta as a potential neurophysiological indicator for NIBS effects on different WM components, adequate cognitive tasks should be considered to better address the complexity of WM processing. Future research should also evaluate the potential use of FM-theta as an index of the therapeutic effects of NIBS intervention on neuropsychiatric disorders, especially those involving the ventral medial prefrontal cortex and cognitive dysfunctions.
Some reliability characteristics and stochastic ordering of series and parallel systems of bivariate generalized exponential distribution

ABSTRACT In this paper, the reliability properties of two-component parallel and series systems are considered for bivariate generalized exponential (BVGE) distribution introduced by Kundu and Gupta [Bivariate generalized exponential distribution. J Multivar Anal. 2009;100:581–593]. For this model, the moments and mean residual life functions of these systems and the regression mean residual life function are derived. Stochastic comparisons of series and parallel systems of BVGE distribution are investigated. Moreover, various ordering results for the comparisons of series and parallel systems arising from BVGE random vectors are obtained with respect to the usual stochastic, reversed hazard rate and likelihood ratio orderings.
Multi-criteria optimization model integrated with AHP for evaluation and selection of COTS components

This paper deals with COTS evaluation and selection for developing a modular software system under single application development task. We consider both quantitative and qualitative criteria which fulfils the specific needs of a software system. We use analytical hierarchy process (AHP) technique for evaluating the fitness of COTS components based upon various criteria and sub-criteria thereby providing overall score of each COTS component. We develop optimization models integrating AHP and multi-criteria decision-making, which aim at: (i) maximize the total value of purchasing (TVP) subject to budget, compatibility and reliability constraints, and (ii) maximize TVP and minimize the total cost of purchase simultaneously subject to compatibility and reliability constraints. The efficiency of the models is illustrated by means of numerical illustrations.