Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Farm-Level Interventions on Households' Livelihoods in Nandi County, Kenya

Smallholder dairying is significant economic activity. However, the activity is fraught with low productivity thus increasing vulnerability of dairy farmers to poverty. Thus, the main purpose of this study was to investigate the contribution of dairy projects interventions on households’ livelihoods in EADD projects in Nandi County of Kenya. The objective of this paper was to establish the influence of farm-level interventions on the households’ livelihoods in Nandi County. The study adopted the mixed-methods approach and utilized the descriptive research design, with the target population of 5,400 small-scale dairy farmers and staff spread over three dairy projects of Kabiyet, Lelchego and Tanykina Dairies in Nandi County. The sample size was 360 respondents based on modified Cochran’s formula with the head of the household being selected using a proportionate stratified random sampling technique. Data was collected using semi-structured questionnaires and interview schedules, which was validated by expert judgments while reliability were assessed using Cronbach Alpha coefficient. Quantitative data obtained was analysed using descriptive and inferential statistics which Pearson Correlation Coefficient. Qualitative data from interview schedules was analysed through content and thematic analysis. The inferential statistics showed that households’ livelihoods positively correlated with farm-level interventions (r = 0.385, p< 0.05). It was concluded that farm-level interventions have a significant influence on households’ livelihoods and positively relates to livelihoods. The study thus recommended that dairy farming cooperatives should improve the nature and form of technological interventions through the introduction of new breeds and sires to the farmers’ pool of resources to improve the milk production. They should also promote knowledge diffusion and innovation by taking the best farmers to the farmers training centres for short-term courses.
PHYTOCHEMICAL ACCUMULATION WITH PHOTOMORPHOGENESIS AND PHYSIOLOGY OF SALVIA OFFICINALIS L.

We have investigated the leaf development of sage including the stomata, trichome and clorophyll parameters, their response, and the interaction of the relationship between these parameters and quantity and content of the phytochemicals in the plant with different photoperiod applications. Sage plants were exposed to shortdat, middle-day and long-day conditions in a controlled environment for 3 months. To confirm the morphological responses of stomata in response to photoperiod, stomatal density, stomatal sizes (Lg/Wg), stomatal area and relative stomatal area on both leaf surfaces were determined using SEM analyses. Phytochemicals parameters were determined using SPME and GS/MS analyses. Light period caused significant changes in morphoparameters on both surfaces of leaves. Significant changes in pythochemical quantity and content of sage were observed as well. In the light of the morphologic data such as plant growth, leaf surface area, stomatal and trichome parameters, chlorophyll and phytochemical content gathered from sage plants exposed to different photoperiod lengths, we hereby describe the circadian rhytm mechanism of the plant.
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers

Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks. However, on real-world scenarios with partially or no labeled data, DL methods are also prone to the well-known domain shift problem. Multi-source unsupervised domain adaptation (MSDA) aims at learning a predictor for an unlabeled domain by assigning weak knowledge from a bag of source models. However, most works conduct domain adaptation leveraging only the extracted features and reducing their domain shift from the perspective of loss function designs. In this paper, we argue that it is not sufficient to handle domain shift only based on domain-level features, but it is also essential to align such information on the feature space. Unlike previous works, we focus on the network design and propose to embed Multi-Source version of DomaIn Alignment Layers (MS-DIAL) at different levels of the predictor. These layers are designed to match the feature distributions between different domains and can be easily applied to various MSDA methods. To show the robustness of our approach, we conducted an extensive experimental evaluation considering two challenging scenarios: digit recognition and object classification. The experimental results indicated that our approach can improve state-of-the-art MSDA methods, yielding relative gains of up to +30.64% on their classification accuracies.
Efficient Instruction Scheduling Using Real-time Load Delay Tracking

Issue time prediction processors use dataflow dependencies and predefined instruction latencies to predict issue times of repeated instructions. In this work, we make two key observations: (1) memory accesses often take additional time to arrive than the static, predefined access latency that is used to describe these systems. This is due to contention in the memory hierarchy and variability in DRAM access times, and (2) we find that these memory access delays often repeat across iterations of the same code. We propose a new processor microarchitecture that replaces a complex reservation-station-based scheduler with an efficient, scalable alternative. Our scheduling technique tracks real-time delays of loads to accurately predict instruction issue times and uses a reordering mechanism to prioritize instructions based on that prediction. To accomplish this in an energy-efficient manner we introduce (1) an instruction delay learning mechanism that monitors repeated load instructions and learns their latest delay, (2) an issue time predictor that uses learned delays and dataflow dependencies to predict instruction issue times, and (3) priority queues that reorder instructions based on their issue time prediction. Our processor achieves 86.2% of the performance of a traditional out-of-order processor, higher than previous efficient scheduler proposals, while consuming 30% less power.
Learning grounded word meaning representations on similarity graphs

This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations, conditioned to another modality, through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding. The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgments and concept categorization, outperforming the state of the art.
Etude multi-échelle de l'initiation de l'endommagement laser en surface et dans le volume de matériaux optiques

L'endommagement laser est le resultat d'une interaction laser-matiere qui se traduit par une degradation physique des optiques, entrainant une deterioration de leur(s) fonction(s) optique(s). Dans les materiaux dielectriques et en regime nanoseconde, ce phenomene est en grande partie du a la presence de centres precurseurs de taille nanometrique sur lesquels s'initie l'endommagement. L'objectif des travaux presentes dans ce manuscrit est d'etudier les caracteristiques de ces centres initiateurs. Nous montrons en particulier qu'une etude multi-echelle spatiale de l'endommagement laser realisee a plusieurs longueurs d'onde permet de discriminer differentes populations de defauts, et d'observer separement leur evolution sous flux laser. Cette etude a ete appliquee au cas de l'endommagement initie en volume de materiaux optiques a travers l'exemple du cristal de KH2PO4 (KDP), puis en surface sur des mono-couches minces de HfO2. Le developpement d'un nouveau modele statistique couple a un modele electromagnetique et thermique nous a permis d'aboutir a des estimations sur la nature et la taille des centres initiateurs presents dans les materiaux etudies. D'un point de vue metrologie, nous montrons que la discrimination de ces populations influe sur la mesure des seuils d'endommagement laser. Enfin, le cas d'une initiation par des contaminants de surface a ete etudiee grâce au dispositif de microscopie photothermique. Dans ce cas, un protocole de conditionnement a pu etre etabli.
Hypoplastic left heart syndrome (HLHS): molecular pathogenesis and emerging drug targets for cardiac repair and regeneration

ABSTRACT Introduction Hypoplastic left heart syndrome (HLHS) is a severe developmental defect characterized by the underdevelopment of the left ventricle along with aortic and valvular defects. Multiple palliative surgeries are required for survival. Emerging studies have identified potential mechanisms for the disease onset, including genetic and hemodynamic causes. Genetic variants associated with HLHS include transcription factors, chromatin remodelers, structural proteins, and signaling proteins necessary for normal heart development. Nonetheless, current therapies are being tested clinically and have shown promising results at improving cardiac function in patients who have undergone palliative surgeries. Areas covered We searched PubMed and clinicaltrials.gov to review most of the mechanistic research and clinical trials involving HLHS. This review discusses the anatomy and pathology of HLHS hearts. We highlight some of the identified genetic variants that underly the molecular pathogenesis of HLHS. Additionally, we discuss some of the emerging therapies and their limitations for HLHS. Expert opinion While HLHS etiology is largely obscure, palliative therapies remain the most viable option for the patients. It is necessary to generate animal and stem cell models to understand the underlying genetic causes directly leading to HLHS and facilitate the use of gene-based therapies to improve cardiac development and regeneration.
Charge Transport Layer Doping Influence on Perovskite CH3NH3PbI3-xClx Solar Cell Performance

Organometal hybrid trihalide perovskite solar cells (PSCs) have emerged a new class of optoelectronic devices for various applications. PSCs have demonstrated unprecedented progress in efficiency reaching certified power conversion efficiency 22.1% after only several years of active research. In this paper, we demonstrate inverted planar mixed halide perovskite solar cells where perovskite layers are built by two-step modified interdifussion and one-step methods. We demonstrate how PSC parameters change by doping electron transport layer (ETL). We used N,N-dimethyl-N-octadecyl (3-aminopropyl) trimethoxysilyl chloride (DMOAP) as dopant for ETL [6,6]-phenyl C61 butyric acid methyl ester (PCBM). The highest EQE and Voc values were obtained for cell prepared by one-step method with fast crystallization and doped ETL but higher fill factor (FF) and shunt resistance (Rshunt) values were obtained for cells prepared by two-step method with undoped ETL.
Failure Mode and Effect Analysis Based on Probabilistic Linguistic Preference Relations and Gained and Lost Dominance Score Method

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.
Investigation of the Effects of an Online Support Group for Mental Health Problems on Stigma and Help-Seeking Among Japanese Adults: Cross-sectional Study

Background Online support groups vary widely in both goals and structures owing to the rapid development of social networking services. Several studies have shown the potential effectiveness of online support groups, such as reducing psychological distress (eg, depression) among individuals with mental health problems. However, online support groups often do not aim at effectiveness regarding distress relief–related outcomes. Objective This study aims to examine whether the use frequency of online support group platform functions (U2plus) is associated with lower stigma and higher consumer activation. Methods A total of 350 U2plus users participated in a web-based survey. They were asked what therapy they had received in the past and how often they logged on to it, used each of its functions, and completed the following questionnaires: the Patient Health Questionnaire-9, the Devaluation-Discrimination Scale, and the General Help-Seeking Questionnaire. Results Regarding the therapy received, 88% (308/350) of participants had taken medication for mental health problems, and 66.6% (233/350) had received psychotherapy or mental health counseling. Regarding use frequency, 21.7% (74/341) of the participants signed in to U2plus and used its functions more than once a week. The use frequency of U2plus functions was not correlated with perceived stigma, but the use frequency of some functions was weakly correlated with help-seeking intentions from formal sources (eg, doctors and psychologists). However, multiple regression analyses revealed that the use frequency of those functions did not uniquely predict help-seeking intentions. Conclusions It was suggested that online support groups may serve as an alternative treatment option for those who are already undergoing pharmacological treatment and are willing to seek help from whatever source they deem helpful.
Identifying and mitigating the impacts on primates of transportation and service corridors

Most primate populations are declining; 60% of species face extinction. The expansion of transportation and service corridors (T&S) (i.e., roads and railways and utility and service lines) poses a significant yet underappreciated threat. With the development of T&S corridors predicted to increase across primates' ranges, it is necessary to understand the current extent of its impacts on primates, the available options to mitigate these effectively, and recognize research and knowledge gaps. By employing a systematic search approach to identify literature that described the relationship between primates and T&S corridors, we extracted information from 327 studies published between 1980 and 2020. Our results revealed that 218 species and subspecies across 62 genera are affected, significantly more than the 92 listed by the IUCN Red List of Threatened Species. The majority of studies took place in Asia (45%), followed by mainland Africa (31%), the Neotropics (22%), and Madagascar (2%). Brazil, Indonesia, Equatorial Guinea, Vietnam, and Madagascar contained the greatest number of affected primate species. Asia featured the highest number of species affected by roads, electrical transmission lines, and pipelines and the only studies addressing the impact of rail and aerial tramways on primates. The impact of seismic lines only emerged in the literature from Africa and the Neotropics. Impacts are diverse and multifaceted, for example, animal–vehicle collisions, electrocutions, habitat loss and fragmentation, impeded movement and genetic exchange, behavioral changes, exposure to pollution, and mortality associated with hunting. Although several mitigation measures were recommended, only 41% of studies focused on their implementation, whereas only 29% evaluated their effectiveness. Finally, there was a clear bias in the species and regions benefiting from research on this topic. We recommend that government and conservation bodies recognize T&S corridors as a serious and mounting threat to primates and that further research in this area is encouraged.
Lamellar Assembly Mechanism on Dendritic Ring-Banded Spherulites of Poly(ε-caprolactone).

The self-assembly structures of lamellae in optical ring bands have a critical effect on their optical and physical arrangements. Two different types of dendritic banded spherulites (namely ring-banded and zig-zag ring-banded) have been formed in poly(ε-caprolactone)/ poly (phenyl methacrylate) blend at crystallization temperatures of 42°C and 46°C, respectively. The difference in optical birefringence of ring bands in two types spherulites has been resolved by means of direct morphological comparison. Banded spherulites have been fractured carefully to facilitate lamellar orientation analyses of both the top-surface and the interior surface. The results have revealed the existence of tree-like dendritic fractal growth lamellar assemblies in both banded spherulites. The optical ring patterns of the banded spherulites are differentiated mainly by the fractal orientation of the edge-on crystal branches in the ridge region. On the basis of detailed morphological analysis, 3D-lamellar assembly mechanisms have been proposed to explain the growth of dendritic ring-banded spherulites at 42°C and dendritic zig-zag ring-banded spherulites at 46°C. This article is protected by copyright. All rights reserved.
Impact of Atopic Dermatitis on Work and Activity Impairment in Taiwan

Atopic dermatitis has a substantial impact on work and activity impairment according to studies from Western communities. Prospective studies of work productivity and activity impairment in Asian patients with atopic dermatitis are lacking. The aims of this study were to investigate the impacts of atopic dermatitis on work productivity and activity impairment among Taiwanese patients, and to stratify the analyses by disease severity. One-third of employed participants reported missing work (absenteeism) in the preceding week due to atopic dermatitis, while 88.5% of the remaining two-thirds reported impaired work effectiveness (presenteeism). In addition, 92.5% of all participants reported impaired daily activities. Overall work impairment (aggregate productivity loss from absenteeism and presenteeism) was 1.8- and 2.6-fold greater in subjects with moderate and severe atopic dermatitis, respectively, compared with those with mild atopic dermatitis. Presenteeism, but not absenteeism, contributes to the majority of total work impairment in this cohort. Daily activity impairment was 1.5-fold greater in moderate atopic dermatitis, and 2.0-fold greater in severe atopic dermatitis, compared with mild atopic dermatitis. Both work and activity impairment showed significant positive correlations with atopic dermatitis severity scores (SCORing Atopic Dermatitis; SCORAD). In conclusion, work productivity and activity impairment is significantly correlated with disease severity in this Taiwanese atopic dermatitis cohort. In order to obtain a full picture of disease burden to patients and caregivers, patients with atopic dermatitis should be monitored for disease activity as well as corresponding impacts on quality of life.
Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation

This work explores the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.
Effect of High Cholesterol Regulation of LRP1 and RAGE on Aβ Transport Across the Blood-brain Barrier in Alzheimer's Disease.

BACKGROUND High cholesterol aggravates the risk development of Alzheimer's disease (AD). AD is closely related to the transport impairment of amyloid-β (Aβ) in the blood-brain barrier. It is unclear whether high cholesterol affects the risk of cognitive impairment in AD by affecting Aβ transport. The purpose of the study is to investigate whether high cholesterol regulates Aβ transport through low-density lipoprotein receptor-related protein 1 (LRP1) and receptor for advanced glycation end products (RAGE) in the risk development of AD. METHODS We established high cholesterol AD mice model. The learning and memory functions were evaluated by Morris water maze (MWM). Cerebral microvascular endothelial cells were isolated, cultured, and observed. The expression levels of LRP1 and RAGE of endothelial cells and their effect on Aβ transport in vivo were observed. The expression level of LRP1 and RAGE was detected in cultured microvessels after using Wnt inhibitor DKK-1 and β-catenin inhibitor XAV-939. RESULTS Hypercholesterolemia exacerbated spatial learning and memory impairment. Hypercholes- terolemia increased serum Aβ40 level, while serum Aβ42 level did not change significantly. Hyper- cholesterolemia decreased LRP1 expression and increased RAGE expression in cerebral microvascular endothelial cells. Hypercholesterolemia increased brain apoptosis in AD mice. In in vitro experiment, high cholesterol decreased LRP1 expression and increased RAGE expression, increased Aβ40 expression in cerebral microvascular endothelial cells. High cholesterol regulated the expressions of LRP1 and RAGE and transcriptional activity of LRP1 and RAGE promoters by the Wnt/β-catenin signaling pathway. CONCLUSION High cholesterol decreased LRP1 expression and increased RAGE expression in cerebral microvascular endothelial cells, which led to Aβ transport disorder in the blood-brain barrier. Increased Aβ deposition in the brain aggravated apoptosis in the brain, resulting to cognitive impairment of AD mice.