Miracle-dz/UniFAR-FTUs huggingface dataset:
Can area measurement error explain the inverse farm size productivity relationship

The existence of an inverse relationship (IR) between farm size and productivity in tropical agriculture remains a debated issue with policy relevance. Poor agricultural statistical data, including data on farm sizes and farm plot sizes that typically are self-reported by farmers, can lead to biased results and wrong policy conclusions. This study combines self-reported and GPS-measured farm plot and farm sizes to assess how measurement error affects the IR using three rounds of farm plot and household data from Malawi. The results show that measurement error covers up more than 60% of the IR for the total sample but leads to an upward bias in the IR on farms less than one ha. Land and labor market imperfections in combination with food self-sufficiency motives appear to explain most of the IR and lead to a strong IR on farms below one ha.
Impact of Quality of Life, Social Awareness and Political Willingness on Prevention from HIV/AIDS in Pakistan: A Mediation and Moderation Model

Currently, modern world is focusing on the control of fatal diseases including HIV/AIDS. However, in developing countries these diseases are spreading rapidly. There are various factors, which encourage HIV/AIDS. One of the major reasons is lack of awareness among public. In Pakistan, scholars did not discuss the role of awareness about HIV/AIDS as a prevention tool. This study is one of the efforts to observe the impact of quality of life, social awareness and political will on the prevention of HIV/AIDS in Pakistan. Majority of the past literature concentrated on the issue of HIV/AIDS; however, there is paucity of literature on highlighting the major factors of HIV/AIDS prevention. Therefore, this is one of the few studies, which formally investigated the major factors contributing towards the prevention of HIV/AIDS, particularly in the Sind province of Pakistan. A questionnaire was utilized to collect the data. Partial Least Square (PLS 3)-Structural Equation Modeling (SEM) was used to analyze the data. It is found that political willingness, social awareness and quality of life has a significant relation with HIV/AIDS prevention. In order to formulate the strategy, this study is important for governments and NGOs to consider social awareness, government willingness and quality of life as a tool to prevent HIV/AIDS in Pakistan.
Computing the Moments of the Complex Gaussian: Full and Sparse Covariance Matrix

Given a multivariate complex centered Gaussian vector Z = ( Z 1 , ⋯ , Z p ) with non-singular covariance matrix Σ , we derive sufficient conditions on the nullity of the complex moments and we give a closed-form expression for the non-null complex moments. We present conditions for the factorisation of the complex moments. Computational consequences of these results are discussed.
Strong Self‐Enhancement of Optical Nonlinearity in a Topological Insulator with Generation of Tamm State

As new states of matter currently attracting broad attention in the optics field, topological insulators (TIs) present topologically‐protected conducting surface states surrounding insulating bulk and prominent third‐order optical nonlinearities in an ultrawide frequency range. Optical frequency conversion in TI nanofilms plays a crucial role in TI nonlinear optical functionalities, whose enhancement is in demand for high‐performance devices. Herein, an optical Tamm state is observed between the Sb2Te3 TI nanofilm and 1D photonic crystal (PC) with a self‐enhancement behavior of optical nonlinearity in the TI nanofilm. The experiments demonstrate that there exists a distinct reflection dip in the forbidden band of the PC, which is attributed to the appearance of Tamm state in the TI/PC structure. The wavelength of the TI‐based Tamm state possesses a nearly linear redshift with increasing TI film thickness. The measurements are in excellent agreement with simulation and theoretical calculations. Moreover, a strong third‐harmonic generation reinforcement of 30‐fold is experimentally observed in a 90 nm Sb2Te3 film, which stems from the field enhancement induced by the TI self‐excited Tamm state at the fundamental frequency. This work opens a new door for generation of optical Tamm states and applications of TIs in high‐efficiency nonlinear optical functionalities, especially frequency conversion.
Psychological Contract Perspective and Implications in Small and Medium Enterprises

MSMEs are micro, small and medium enterprises which are contributing significantly to India’s total GDP. In fact, it is the growth engine of Indian economy which generates highest employments. This study attempts (a) to understand the importance of psychological contract (PC) in small and medium enterprises (SMEs), (b) to assess the extent to which organisational culture dimensions and happiness contribute in determining PC in SMEs. Three standardised questionnaires namely Organisational Culture: OCTAPACE Profile, Psychological Contract Scale and The Oxford Happiness Scale have been used to collect the data. Four hundred samples have been drawn from different SMEs. The statistics used is multiple regression analysis to analyse the data and interpret the results. The study has thrown many interesting results. The present research findings show that PC is the function of happiness and organisational culture dimensions. It is found that the dimensions of culture namely trust, collaboration and pro-action have come out to be the most promising influencers to PC along with happiness. Numerous studies have identified a strong linkage between PCs, organisational performance and productivity. Based on the findings organisation development (OD) and behavioural intervention strategies are suggested to enhance PC in SMEs.
A Review of Research Exploring Pre-service Teacher Education for the Digital Era

Internet and digital technologies play increasingly important roles in schooling systems. With teaching and schools evolving due to the integration of technologies, pre-service teacher education will also change. Through a systematic and full literature review of papers published in the recent over 10 years, this study examines research exploring pre-service teacher education for the digital era. The articles analyze critically what has as well as has not been studied over a series of literature and broad contexts on digital integration in schools. Digital competencies should be generic, teaching and professional. The future research lies in how pre-service teachers learn to take part in the professional work in education system infused with digital technologies. A idea of professional digital literacy is proposed.
Pronunciation Dictionary-Free Multilingual Speech Synthesis Using Learned Phonetic Representations

This article presents a multilingual speech synthesis approach that leverages learned phonetic representations to eliminate the need for pronunciation dictionaries in target languages. The learned phonetic representations consist of unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR). To extract UPRs, a pre-trained wav2vec 2.0 model is utilized, while a language-independent automatic speech recognition (LI-ASR) model with a connectionist temporal classification (CTC) loss is employed to derive segment-level SPRs from the speech data of target languages. An acoustic model using UPRs and SPRs as intermediate representations is then designed, comprising a UPR predictor, an SPR predictor, and a representation-to-mel-spectrogram (RTM) converter. The two predictors generate UPRs and SPRs from texts, respectively. The RTM converter first combines UPRs with SPRs using a Transformer-based encoder, and then feeds the merged representations into a decoder to produce mel-spectrograms. Considering the difficulty of collecting large training corpora for all languages in multilingual speech synthesis, the parameters of both the two predictors and the RTM converter can be pre-trained on non-target languages to further improve model performance. Experimental results on six target languages demonstrate that our method outperformed the approaches directly predicting mel-spectrograms from character or phoneme sequences, and pre-training the acoustic model using a multilingual corpus further improved the performance of synthetic speech.
Using Large Language Models to Shape Social Robots' Speech

Social robots are making their way into our lives in different scenarios in which humans and robots need to communicate. In these scenarios, verbal communication is an essential element of human-robot interaction. However, in most cases, social robots’ utterances are based on predefined texts, which can cause users to perceive the robots as repetitive and boring. Achieving natural and friendly communication is important for avoiding this scenario. To this end, we propose to apply state-of-the-art natural language generation models to provide our social robots with more diverse speech. In particular, we have implemented and evaluated two mechanisms: a paraphrasing module that transforms the robot’s utterances while keeping their original meaning, and a module to generate speech about a certain topic that adapts the content of this speech to the robot’s conversation partner. The results show that these models have great potential when applied to our social robots, but several limitations must be considered. These include the computational cost of the solutions presented, the latency that some of these models can introduce in the interaction, the use of proprietary models, or the lack of a subjective evaluation that complements the results of the tests conducted.
Initial Validation of NDVI time seriesfrom AVHRR, VEGETATION, and MODIS

The paper will address Theme 7: Multi-sensor opportunities for VEGETATION. We present analysis of a long-term vegetation record derived from three moderate resolution sensors: AVHRR, VEGETATION, and MODIS. While empirically based manipulation can ensure agreement between the three data sets, there is a need to validate the series. This paper uses atmospherically corrected ETM+ data available over the EOS Land Validation Core Sites as an independent data set with which to compare the time series. We use ETM+ data from 15 globally distributed sites, 7 of which contain repeat coverage in time. These high-resolution data are compared to the values of each sensor by spatially aggregating the ETM+ to each specific sensors' spatial coverage. The aggregated ETM+ value provides a point estimate for a specific site on a specific date. The standard deviation of that point estimate is used to construct a confidence interval for that point estimate. The values from each moderate resolution sensor are then evaluated with respect to that confident interval. Result show that AVHRR, VEGETATION, and MODIS data can be combined to assess temporal uncertainties and address data continuity issues and that the atmospherically corrected ETM+ data provide an independent source with which to compare that record. The final product is a consistent time series climate record that links historical observations to current and future measurements.
Epigenetic Control of Plant Response to Heavy Metals

Plants are sessile organisms that must adapt to environmental conditions, such as soil characteristics, by adjusting their development during their entire life cycle. In case of low-distance seed dispersal, the new generations are challenged with the same abiotic stress encountered by the parents. Epigenetic modification is an effective option that allows plants to face an environmental constraint and to share the same adaptative strategy with their progeny through transgenerational inheritance. This is the topic of the presented review that reports the scientific progress, up to date, gained in unravelling the epigenetic response of plants to soil contamination by heavy metals and metalloids, collectively known as potentially toxic elements. The effect of the microbial community inhabiting the rhizosphere is also considered, as the evidence of a transgenerational transfer of the epigenetic status that contributes to the activation in plants of response mechanisms to soil pollution.
A Low-Profile, Wide-Angle Scanning Phased Array Based on Dual-Mode Patch Elements for Vehicular Millimeter-Wave Applications

In this brief, based on dual-mode patch elements, an 8-channal wide-angle scanning phased array is proposed and demonstrated at <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-band. By jointly exciting the <inline-formula> <tex-math notation="LaTeX">$\rm TM_{11}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rm TM_{01}$ </tex-math></inline-formula> modes of a via-loaded stacked-patch radiator, the array can possess one-dimensional wide scanning properties. In addition, by incorporating a four-element sub-array with in-phase Dolph-Chebyshev excitations, directive patterns with low sidelobe levels (SLLs) can be achieved in the orthogonal main plane of the scanned beam. An array prototype operating at 25.2 GHz is fabricated and characterized, achieving a 3-dB scanning coverage of ±70° with a SLL of −9 dB. In the orthogonal main plane of the broadside beam, a directional pattern with a SLL of −22 dB is obtained. The demonstrated phased array can be suitable for vehicular millimeter-wave applications.
Research on the Reconfiguration Method of Space-Based Exploration Satellite Constellations for Moving Target Tracking at Sea

In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population’s evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea.
Control strategies for offshore wind farms based on PMSG wind turbines and HVdc connection with uncontrolled rectifier

The selection of the bulk power transmission technology in offshore wind farms is strongly related to the wind farm size and its distance to shore. Several alternatives can be evaluated depending on the rated power of the offshore wind farm, the transmission losses and the investment cost for constructing the transmission system. However, when is necessary to connect larger and more distant offshore wind farms; the best technological solution tends to the transmission system based on highvoltage and direct-current with line commutated converters (LCC-HVdc). This dissertation proposes the use of diode-based rectifers as a technical alternative to replace the thyristor-based rectifers in an LCC-HVdc link with unidirectional power flow. This alternative shows advantages with regard to lower conduction losses, lower installation costs and higher reliability. Nonetheless, as a counterpart the offshore ac-grid control performed by the thyristor-based HVdc rectifer is no longer available. This lack of control is compensated by using new control strategies over an offshore wind farm composed by wind turbines with permanent-magnet generators and fully-rated converters. The control strategies have been based mainly on the ability of the wind turbine grid-side converter to perform the control of the offshore ac-grid voltage and frequency. The performance has been evaluated by using PSCAD. Wherein, the most common grid disturbances have been used to demonstrate the fault-ride-through capability as well as the adequate steady state and transient response.
An Expandable ZVZCS High Step-Down Interleaved DC–DC Converter

This article introduces a new expandable multiphase interleaved converter. The presented idea can be applied to K phases, where K is an even number (2, 4, 6,…). In this converter, zero voltage zero current switching (ZVZCS) and zero voltage switching (ZVS) conditions are achieved for the main switches at turn-on and turn-off instants, respectively. The synchronous rectifiers (SRs) and auxiliary switches operate under ZVS conditions. Moreover, the body diodes of SRs turn off under zero current switching (ZCS) conditions. This topology employs the series capacitors to improve the voltage gain and clamp the voltage stress of switches. Furthermore, ultrahigh step-down conversions can be achieved by adjusting the turns ratio of coupled windings. In the proposed converter, by utilizing one auxiliary four-quadrant switch for each pair of phases and a new switching algorithm, ZVS and phase interleaving are simultaneously provided, regardless of the number of phases. Although the leakage energy of coupled inductors is used to provide soft switching, the current ripple of the leakage inductor is not reflected in the output current, and uniform current sharing between modules is fully automatic. In addition, by increasing the number of phases, the duty cycle range in which the current sharing is uniform does not shrink. Continuous output current with low ripple and common input and output ground are other important strengths of this converter. The above-mentioned merits are achieved all together without using any extra magnetic core or current sharing control circuit. Also, the number of blocking capacitors is low and equal to the number of phases minus one. The operation principles of the introduced topology are presented and the theoretical analysis is confirmed by two laboratory prototypes.
A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks

In modern smart cities, the scale of urban backbone networks used to provide Internet communication environment are constantly increasing. When faults occur, it usually takes lots of efforts to detect and locate the faults. As a result, automatic detection and positioning of faults with use of intelligent algorithms have been a practical demand in this area. In this paper, the complicated whole urban backbone network is viewed as a graph-level object, in which massive nodes and edges are involved. On this basis, a two-stage graph deep learning-based fault detection and positioning method for Internet communication networks. For the first stage, the graph neural network is employed to extract graph-level features from Internet communication networks. This is expected to obtain proper feature representation for core characteristics of backbone networks. For the second stage, the fault detection and positioning algorithm is formulated to output final results. At last, experiments are conducted to assess performance of the proposal. The results show that the proposed method has good performance in abnormal node detection as well as high accuracy in fault positioning. The accuracy of the two-stage graph deep learning algorithm proposed in this chapter is much higher than that of KNN algorithm, reaching 96.5% in the end, slightly lower than that of pure graph deep learning algorithm, while the accuracy of IRBFG algorithm can only reach 92%.
The Crucial Role of the Interstitial Cells of Cajal in Neurointestinal Diseases

Neurointestinal diseases result from dysregulated interactions between the nervous system and the gastrointestinal (GI) tract, leading to conditions such as Hirschsprung’s disease and irritable bowel syndrome. These disorders affect many people, significantly diminishing their quality of life and overall health. Central to GI motility are the interstitial cells of Cajal (ICC), which play a key role in muscle contractions and neuromuscular transmission. This review highlights the role of ICC in neurointestinal diseases, revealing their association with various GI ailments. Understanding the functions of the ICC could lead to innovative perspectives on the modulation of GI motility and introduce new therapeutic paradigms. These insights have the potential to enhance efforts to combat neurointestinal diseases and may lead to interventions that could alleviate or even reverse these conditions.
Self-assessment System: Detrimental Effects on Entrepreneurial and Innovative Activity

Efficient regulatory mechanisms that induce innovation, co-operation and deter competition law infringements have recently been the subject of growing attention. C ompetition is essential to the innovation process which in general terms enables entrepreneurship. But so too is co-operation between firms which requires an exchange of information and may lead to inefficient collusive behaviour. The optimal trade-off between the provision of stable entrepreneurial incentives and the new European competition law’s reform with the self-assessment system has been largely missing from the current scholarly debate. This paper identifies the unintended, harmful horizontal side effects of this new European self-assessment system upon the entrepreneurial activity, offers a legal evaluation of the optimal entrepreneurial incentive mechanisms and provides legal and entrepreneurial arguments for an improved regulatory response.
Purified Serum IgG from a Patient with Anti-IgLON5 Antibody Cause Long-Term Movement Disorders with Impaired Dopaminergic Pathways in Mice

Background: Anti-IgLON5 disease is a rare autoimmune disease of the central nervous system. It typically manifests as a chronic condition, characterized by cognitive impairments, movement disorders, and sleep disorders. The mechanisms underlying movement disorders in this disease remain poorly understood due to a lack of research. Furthermore, this disease exhibits both neuroimmune and neurodegenerative characteristics. The objective of this study is to explore the underlying mechanisms of movement disorders caused by anti-IgLON5 antibodies for the first time. Methods: Antibodies were purified from the serum of a confirmed patient of anti-IgLON5 disease. The passive transfer animal models were employed, where antibodies were continuously injected into the substantia nigra pars compacta (SNc) of the mouse midbrain using stereotactic injection to explore the mechanism of movement disorder. The effects of anti-IgLON5 antibodies on dopaminergic neurons in the SNc and neurodegeneration were examined through immunohistochemistry. Changes in neurotransmitter levels in the basal ganglia were assessed using high-performance liquid chromatography. Additionally, RNA-seq was employed to identify the differentially expressed genes associated with the short-term and long-term effects of anti-IgLON5 antibody on the SNc. Results: Mice injected with anti-IgLON5 antibodies in the SNc exhibited persistent movement impairments for up to 3 months. One week after antibody injection, the number of TH neurons significantly decreased compared to the control group, accompanied by reduced projection fibers in the basal ganglia and decreased dopamine levels. After 3 months of antibody injection, an increase in phosphorylated Tau was observed in the SNc of the midbrain. Additionally, long-term sustained activation of microglia was detected in the SNc. The differentially expressed genes of long-term effects of IgLON5 antibodies were different from their short-term effects on the SNc. Conclusion: Purified serum IgG from a patient with anti-IgLON5 antibodies can cause long-term movement disorder in mice. The movement disorders appear to be linked to the impaired dopaminergic pathway, and the increased p-Tau showed neurodegenerative changes induced by the anti-IgLON5 antibody.