Miracle-dz/UniFAR-FTUs huggingface dataset:
A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intraclass differences of cropland changes in high-resolution images. In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, in this article, we propose a CNN-transformer network with multiscale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland CD. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based MSCA is designed to encode and aggregate context information. Finally, a multibranch prediction head with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of CD dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset, which contains 600 pairs of 512 × 512 bi-temporal images with the spatial resolution of 0.5–2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic CD dataset, and of 71.29% on CLCD.
Mainstreaming of Environmental Education in the Physical Science Courses of Bicol University College of Education

The main objective of the study is mainstreaming an environmental education (EE) program in the Bachelor in Secondary Education (BSED) curriculum of Bicol University College of Education and determining its effectiveness in developing awareness and understanding of environmental concepts and principles. The study utilized mixed methods of research, specifically the developmental-descriptive-evaluative method in the development of the curriculum guide for physical science courses. The curriculum guide showing the integration of environmental education concepts and principles consisted of ten lessons distributed across the different subjects. This was successfully implemented over a nine-week period. The EE-enhanced physical science lessons were effective in developing awareness and understanding of the environmental concepts and principles. Students further gained positive attitudes and behaviors, which prepared them for their job as future eco-mentors. It is recommended that the curriculum guide and the EE-enhanced physical science lessons be implemented in other teacher education institutions and in other institutions of higher learning offering a variety of undergraduate courses other than teacher education.
Safeguarding Consumers Through Minimum Quality Standards: Milk Inspections and Urban Mortality, 1880-1910

We examine the effect of enforcing minimum quality standards (MQSs) on consumer health. In the late 1800s, the urban milk supply was regularly skimmed and diluted with water, but consumers could not easily determine its quality because dyes, caramel, and salt were added. To protect consumers, milk inspectors were tasked with enforcing a well-defined MQS. Using citylevel data for the period 1880-1910, we find that milk inspections reduced mortality from waterborne and foodborne diseases by 8-19 percent. Ours is the first study to provide evidence that MQSs can improve consumer health when directly applied to an experience or credence good.
Non-Intelligence Influencing Factors on Academic Performance of Primary School Students

After entering the elementary school, children begin to conduct purposive and systematic learning activities under the guidance of teachers. The objective evaluation standard of learning activities, that is, the academic performance, is also receiving more and more attention. This research explores the non-intelligence factors that affect the academic performance of primary school students, mainly focusing on their personal factors and family factors. Among the personal factors, self-regulation, motivation, personality and fitness all have significant impacts on academic achievement. In terms of family factors, parental involvement, family emotional environment and socioeconomic status are all related to the academic performance of primary school students.
Development of a bioorthogonal fluorescence-based assay for assessing drug uptake and delivery in bacteria

Bioorthogonal chemistry can facilitate the development of fluorescent probes that can be used to sensitively and specifically detect the presence of biological targets. In this study, such an assay was developed to evaluate the uptake and delivery of antimicrobials into Escherichia coli, building on and extending previous work which utilised more resource intensive LCMS detection. The bacteria were genetically engineered to express streptavidin in the periplasmic or cytoplasmic compartments, which was used to localise a bioorthogonal probe (BCN-biotin). Azido-compounds which are delivered to these compartments react with the localised BCN-biotin–streptavidin in a concentration-dependent manner via a strain-promoted alkyne–azide cycloaddition. The amount of azido-compound taken up by bacteria was determined by quantifying unreacted BCN-biotin–streptavidin via an inverse electron demand Diels–Alder reaction between remaining BCN-biotin and a tetrazine-containing fluorescent dye. Following optimisation and validation, the assay was used to assess uptake of liposome-formulated azide-functionalised luciferin and cefoxitin. The results demonstrated that formulation into cationic liposomes improved the uptake of azide-functionalised compounds into the periplasm of E. coli, providing insight on the uptake mechanism of liposomes in the bacteria. This newly developed bioorthogonal fluorescence plate-reader based assay provides a bioactivity-independent, medium-to-high throughput tool for screening compound uptake/delivery.
Oral Janus kinase inhibitors in the treatment of atopic dermatitis: A systematic review and meta‐analysis

Abstract Background Janus kinase (JAK) inhibitors are being evaluated as promising upcoming treatments for atopic dermatitis (AD). Objectives To systematically assess the efficacy of oral JAK inhibitors in patients with AD and provide comparisons among JAK inhibitors. Methods A systematic literature review of JAK inhibitors in the treatment of AD was conducted and reported based on Preferred Reporting Items for Systematic Reviews and Meta‐Analyses using PubMed, ClinicalTrials.gov, CENTRAL, MEDLINE/Ovid, Embase and sponsor websites from inception to 30 September 2021. References of relevant articles were reviewed by two authors. Only RCTs of JAK inhibitors for treating AD with more than one study were included. Data was extracted and the meta‐analysis was performed using the metan procedure in STATA version 12.1. Risk of bias was assessed with the Cochrane Risk of Bias Tool. The four outcomes analysed included Eczema Area Severity Index (EASI)‐75 response (≥75% improvement of EASI score from baseline), percent change in EASI score, percent of subjects achieving Investigator Global Assessment (IGA) of clear or almost clear (IGA 0/1), and ≥ 4‐point improvement in pruritus numerical rating scale (NRS). Results Fourteen randomized controlled trials (7051 subjects) assessing three different oral JAK inhibitors (abrocitinib, baricitinib and upadacitinib) in patients with moderate‐to‐severe AD were included in the meta‐analysis. Abrocitinib (100 and 200 mg), baricitinib (1, 2 and 4 mg) and upadacitinib (15 and 30 mg) were all found to be more efficacious compared to placebo in all four outcomes analysed. Upadacitinib 30 mg was more effective than all other dosages of JAK inhibitors in achieving EASI‐75, decrease in percent change of EASI, IGA 0/1 response rate, and ≥ 4‐point improvement in pruritus NRS. Conclusions JAK inhibitors were found to be an effective treatment for AD. Upadacitinib, at 30 mg, was found to be the most efficacious oral JAK inhibitor for AD. More clinical trial studies with comparisons among JAK inhibitors are needed to confirm these results as well as explore long‐term efficacy and safety of these molecules.
Collaborative governance in handling covid-19 for elderly social services

In 2050, Indonesia will enter an ageing population of around 21.1% of the total population. Life expectancy has increased significantly despite having a high vulnerability to neglect due to elderly poverty in the COVID-19 pandemic. This condition encourages the need for inclusive service design to promote health and empower ageing in collaborative governance. In this paper, the author presents a study on how collaborative governance interventions in inclusive services for long-term elderly care in the COVID-19 pandemic. As a representation of national strategic research, this research uses research and development designs through interviews, observations, focus group discussions, analysis of elderly data documentation in 2021. The findings show collaborative governance in inclusive services during the COVID-19 pandemic can increase social resilience and empowerment of the elderly. Although, the elderly mortality rate during the COVID-19 pandemic is still high, there is a strengthening of stakeholder participation, social inclusion, and long-term policy support. So, the adoption of collaborative governance in inclusive services can improve the social welfare of the elderly in the long term care post-COVID-19. The implication is that it is necessary to strengthen the role capacity and participation of stakeholders at the local level based on social capital.
Model-Free Control of Single-Phase Boost AC/DC Converters

Conventionally, the benchmark proportional-integral control method is implemented to realize power factor correction and output voltage regulation of single-phase ac/dc converters. However, the performance is deteriorated due to the influence of uncertainties and disturbances, e.g., periodic input voltage and load variation. Concerning this problem, this article proposes a model-free control (MFC) strategy of single-phase ac/dc converters. The MFC principle is revisited and a frequency response analysis is presented for the algebraic estimators of uncertainties and disturbances. Consequently, the closed-loop performance of MFC systems is allowed to be analyzed in frequency domain via classical linear control theory. Based on the theoretical results, the model-free current and voltage control systems are synthesized in detail to achieve control objectives of the converter. Simulation results demonstrate the performance of MFC systems in reference tracking and disturbance rejection. The feasibility and validity of the presented MFC strategy for single-phase boost ac/dc converters is also confirmed experimentally as compared to the benchmark scheme.
Learning Approach to FMCW Radar Target Classification With Feature Extraction From Wave Physics

Target classification is of great value when the number of transceivers in a sensing system is relatively small. This article proposes a high-accuracy and efficient classification method with machine learning techniques on frequency-modulated-continuous-wave (FMCW) radar. We first establish the novel mapping relationship from physical space to range-Doppler (PS-RD) image based on wave propagation theory, by which four physical features that effectively capture the kinematic and geometrical characteristics of targets, including speed, total reflectivity (ToRe), area, and incidence angle, are extracted from range-Doppler (R-D) image. Then, a multilayer perceptron (MLP) with a single hidden layer is employed to realize the classification. Since the above-mentioned four physical features, derived from wave physics, are chosen as the input of the neural network, our classifier does not work in a black-box way. The computational complexity of the whole classifier is the same as that of a 2-D fast Fourier transform (FFT), which guarantees a real-time operation. As an example, the proposed classifier is applied to the automotive radar system, where road targets are to be classified into five categories, including pedestrian, bike, sedan, truck/bus, and other static objects. Real-world data obtained from 77 GHz FMCW radars are provided for validation, where the proposed physics-assisted classifier turns out to outperform the state of the art in automotive radar application. The overall accuracy of the real data is about 99% even with complex multiple-target cases.
An improved H-Algorand consensus mechanism with credibility assessment applying in EMR storage system

In order to enhance the security and throughput of medical records storage system, a model using an improved H-Algorand consensus mechanism with credibility assessment is proposed. In this study, the model applies blockchain technology to realize distributed storage and privacy protection of medical data. To ensure the effective operation of the blockchain system, a credibility assessment algorithm is used to obtain the trust value of each medical institution node, and replaces the account balances in the H-Algorand consensus mechanism with the trust value as the node weight. Meanwhile, this paper designs a privacy protection scheme, through the searchable encryption algorithm and asymmetric encryption algorithm to achieve system data storage and sharing. Compared with the existing medical record storage model, the proposed model has higher security and throughput efficiency.
Changes in hemodynamic parameters affected by interval hypoxic exercises during the precontest training stage of qualified climbers

Purpose: to study the effects of the use of interval hypoxic training (IHT) in the 15–15 mode with breathing through the system into a confined space with a regulated composition of inhaled air in the integrated training process of climbers of the level of preparation of SP-I. Material & Methods: a study was conducted with the participation of the control and experimental groups using medical-biological methods and IHT in the experimental group with using mathematical statistics methods. Results: conducted studies have allowed to determine that the use of the regime of discontinuous hypoxia 15–15 in the training process of the precompetitive period contribute to an increase in the parameters of hemodynamics that affect the adaptation to the action of the load under conditions of hypoxia. Conclusion: reliable changes that are determined during the research testify to the effectiveness of the use of interval hypoxic training 15–15 in the precompetitive training of qualified climbers.
A system simulator for Broadband Global Area Network

Broadband Global Area Network (BGAN) is a mobile satellite communications service by Inmarsat [1], which is providing reliable, cost-effective global broadband data and voice using compact, lightweight portable terminals. BGAN provides almost global coverage with three geostationary satellites for maritime, enterprise, aviation, and governmental customers. BGAN uses a proprietary Inmarsat Air Interface-2 (IAI-2) protocol stack, which is also the basis of the technical specification of the SL (Satellite Link) family of the Satellite Universal Mobile Telecommunications System (S-UMTS) standard, which was proposed by Inmarsat and published by ETSI as TS 102 744 [2-5]. Unlike IAI-2, S-UMTS Family SL is an open standard. In this paper, a system level simulator that models the S-UMTS Bearer connection and Bearer control layers is presented. In particular, the focus is on the return link scheduling utilizing adaptive modulation and coding scheme (MCS) to balance reliability and efficient bandwidth usage. A custom algorithm was designed for the return link radio resource management (RRM) of the S-UMTS simulator.
ASSESSMENT OF BALANC ASSESSMENT OF BALANC ASSESSMENT OF BALANC ASSESSMENT OF BALANCE IN SPORT: SCIENCE E IN SPORT: SCIENCE E IN SPORT: SCIENCE E IN SPORT: SCIENCE AND REALITY

This paper deals with traditional and contemporary techniques for assessing balance, and the ways in which sport scientists can bridge the gap between balance research and practice. For many years our research has been centered on issues dealing with sport-specific balance testing. Particularly, static and dynamic posturography and recently task-oriented balance tests have also been used to assess postural stability under sport-specific conditions. Though such assessment has shown sufficient reliability of repeated measurements, there has been a problematic discrimination of athletes with a higher level of balance capabilities. Hence, alternative methods, such as stabilogram-diffusion analysis, have been used to investigate the dynamic nature of postural sway. Different approaches have provided us with experience in the role balance plays in sport performance. This has also contributed to a better understanding of physiological mechanisms of post-exercise balance impairment and served as a basis for the design of goal-specific balance training programs. Findings gained in the field of sport can be explored in clinical medicine and rehabilitation.
Offline Training-Based Mitigation of IR Drop for ReRAM-Based Deep Neural Network Accelerators

Recently, resistive RAM (ReRAM)-based hardware accelerators showed unprecedented performance compared the digital accelerators. Technology scaling causes an inevitable increase in interconnect wire resistance, which leads to IR drops that could limit the performance of ReRAM-based accelerators. These IR drops deteriorate the signal integrity and quality, especially in the crossbar structures which are used to build high-density ReRAMs. Hence, finding a software solution, which can predict the effect of IR drop without involving expensive hardware or SPICE simulations, is very desirable. In this article, we propose two neural networks models to predict the impact of the IR drop problem. These models are used to evaluate the performance of the different deep neural network (DNN) models including binary and quantized neural networks showing similar performance (i.e., recognition accuracy) to the golden validation (i.e., SPICE-based DNN validation). In addition, these predication models are incorporated into the DNN training framework to efficiently retrain the DNN models and bridge the accuracy drop. To further enhance the validation accuracy, we propose incremental training methods. The DNN validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method even with challenging datasets, such as CIFAR10 and SVHN.
Paleogene drainage system evolution in the NE Faroe-Shetland Basin

Incised drainage systems can provide a record of the timing, duration and magnitude of ancient vertical crustal motions. The NE Atlantic underwent rapid uplift ∼56 Ma, resulting in widespread incision and the formation of Paleogene unconformities across the Faroe-Shetland Basin. This study uses 3D seismic data to map a newly-identified incised drainage system in the northern Faroe-Shetland Basin that formed during this uplift, and compare it to similar incision surfaces in the region. The ∼30x50 km erosional surface is a composite feature, comprising the Upper Thanetian Unconformity, which was rejuvenated and eroded by the younger Flett Unconformity, and records prolonged regional uplift during the late Thanetian-earliest Ypresian punctuated by a phase of subsidence. The drainage system was influenced by igneous intrusions resulting in diversion of channels around forced fold structures. Several preserved paleoshorelines occur close to the basinward limit of the incision surface, recording progressive backstepping of the shoreline and marine transgression during early Ypresian subsidence. The drainage system may have supplied ‘clean' sand, eroded from the North Shetland Platform, to the Flett Sub-basin, with the potential to form high-quality reservoir units. Such sands may have been concentrated along shorelines coeval with the drainage system, but potentially were also delivered further out into the basin.
Transparency beyond information disclosure: strategies of the Scandinavian public health authorities during the COVID-19 pandemic

Abstract The concept of transparency has been problematized in risk research. This exploratory study contributes to the risk literature by considering an established three-dimensional transparency framework (information substantiality, accountability, and participation) and discussing the opportunities for and challenges to risk communication in relation to the framework. Furthermore, we examine the strategies of Scandinavian health authorities during the COVID-19 pandemic and the different levels of public trust in these authorities. In general, Norwegian authorities received higher levels of trust than their Swedish and Danish counterparts. We argue that this was partly due to differences in transparency management. Our findings support the importance of the three transparency dimensions and indicate that transparency regarding uncertainties positively impacts levels of trust.
Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter

Abstract. Jakarta Governor Election 2017 discussed in society or internet, especially Twitter. Everyone is free opine on Jakarta governor candidate 2017 so many opinions, not only positive or neutral opinion but also negative. Social media, especially Twitter now become promotions or campaigns are effective and efficient. This research is expected be useful to conduct on public opinion containing sentiment positive, neutral or negative. The method used in this study, for data preprocessing using tokenisasi, cleansing and filtering, to define class sentiment with methods Lexicon Based. For classification using Naive Bayes classifier (NBC) and Support Vector Machine (SVM). The data is 300 tweet in Indonesian by keyword AHY, Ahok, Anies. The results of research is analysis sentiment Jakarta governor candidate 2017. The highest accuracy when using the method of classification NaÃ¯ve Bayes Classifier (NBC), with average 95% accuracy, 95% precision, 95% recall, TP rate 96,8% and TN rate 84,6%. Keywords: analisis sentimen, jakarta governor candidate 2017, lexicon based, naÃ¯ve bayes classifier, support vector machineAbstrak. Pemilihan Gubernur DKI Jakarta 2017 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat atau beropini tentang calon Gubernur DKI Jakarta 2017 sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral tapi juga yang negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien. Penelitian ini diharapkan dapat bermanfaat membantu untuk melakukan riset atas opini masyarakat yang mengandung sentimen positif, netral atau negatif. Metode yang digunakan dalam penelitian ini, untuk preprocessing data menggunakan tokenisasi, cleansing dan filtering, untuk menentukan class sentimen dengan metode Lexicon Based. Untuk proses klasifikasinya menggunakan metode NaÃ¯ve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Data yang digunakan adalah tweet dalam bahasa Indonesia dengan kata kunci AHY, Ahok, Anies, dengan jumlah dataset sebanyak 300 tweet. Hasil dari penelitian ini adalah analisis sentimen terhadap calon gubernur DKI Jakarta 2017. Akurasi tertinggi didapat saat menggunakan metode klasifikasi NaÃ¯ve Bayes Classifier (NBC), dengan nilai rata-rata akurasi mencapai 95%, nilai presisi 95%, nilai recall 95% nilai TP rate 96,8% dan nilai TN rate 84,6%. Kata Kunci: analisis sentimen, calon gubernur dki jakarta 2016, lexicon based, naÃ¯ve bayes classifier, support vector machine