Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Assessment Questionnaire disability progression in early rheumatoid arthritis: Systematic review and analysis of two inception cohorts Seminars in Arthritis and Rheumatism

Objective: The Health Assessment Questionnaire is widely used for patients with in ﬂ ammatory poly- arthritis (IP) and its subset, rheumatoid arthritis (RA). In this study, we evaluated the progression of HAQ scores in RA (i) by systematically reviewing the published literature on the methods used to assess changes in functional disability over time and (ii) to study in detail HAQ progression in two large prospective observational studies from the UK. Methods: Data from two large inception cohorts, ERAS and NOAR, were studied to determine trajectories of HAQ progression over time by applying latent class growth models (LCGMs) to each dataset separately. Age, sex, baseline DAS28, symptom duration, rheumatoid factor, ful ﬁ lment of the 1987 ACR criteria and socio-economic status (SES) were included as potential predictors of HAQ trajectory subgroup membership. Results: The literature search identi ﬁ ed 49 studies showing that HAQ progression has mainly been based on average changes in the total study population. In the HAQ progression study, a LCGM with four HAQ trajectory subgroups was selected as providing the best ﬁ t in both cohorts. In both the cohorts, older age, female sex, longer symptom duration, ful ﬁ lment of the 1987 ACR criteria, higher DAS28 and lower SES were associated with increased likelihood of membership of subgroups with worse HAQ progression. Conclusion: Four distinct HAQ trajectory subgroups were derived from the ERAS and NOAR cohorts. The fact that the subgroups identi ﬁ ed were nearly identical supports their validity. Identifying distinct groups of patients who are at risk of poor functional outcome may help to target therapy to those who are most likely to bene ﬁ t.
A Compact LNA with 23.3 dB Gain and 3.4 dB NF for V-Band Phased Array Systems in 65-nm CMOS Technology

This paper presents a compact V-band low noise amplifier (LNA) employed stability enhancement and optimized transformer-based matching technique. The LNA is composed of two pseudo-difference common-source stages structure. Each stage adopts the neutralizing capacitor technology to optimize the noise figure and employ the transformer-matching network for a compact footprint. The proposed LNA is implemented in a 65-nm CMOS technology and consume a DC power of 38 mW at a sup-ply of 1.2 V. The amplifier achieves a 23.3 dB gain with a 3-dB bandwidth between 48.3 and 55.8 GHz. At 52 GHz, the optimum noise figure (NF) is 3.4 dB and is below than 3.65 dB over the whole band. Thanks to the compact transformer-based matching network, the LNA achieves a core chip size of only 0.09 mm2,
Increased crop density reduces weed seed production without increasing screenings.

Results from a series of related field trials confirm that Western Australian crop varieties do not increase screenings with increased seeding rates contrary to the general belief held by farmers. This has management implications for weed control as increasing the crop density can substantially reduce weed growth and seed production in the absence of in-crop herbicides. In one trial, seeding rates of up to 400 kg ha of 4 cereals (oats, barley, wheat and triticale) were investigated as a management tool to control annual ryegrass and wild radish. All 4 cereals were successful in reducing weed seed production to manageable levels at high seeding rates. The yields of both wheat and triticale decreased when seeding rates increased, due to the extremely dry finish to the season in 1998, but only triticale increased screenings. The earlier maturing oats and barley were unaffected. Other trials investigating seeding rates and/or seeding pattern and row spacing with different varieties of wheat have corroborated the uniformity of screenings with seeding density.
A Data Collection Quality Model for Big Data Systems

Big data applications have gained widespread usage across various fields, including healthcare, business, and education. The effectiveness and accuracy of these applications heavily rely on the availability of a large volume of data. However, the collected and generated data for these applications often suffer from incompleteness, inaccuracy, and lack of structure. Consequently, significant efforts are required to clean and process the vast amount of data collected. In this research, we conduct a comprehensive review of existing data quality models that address data and big data quality in general. Building upon this review, we propose a data collection quality model that incorporates a wide range of quality factors. Our model aims to produce clear and accurate data that can be readily utilized, thereby enhancing the value of data and supporting the performance of big data systems. Additionally, the proposed model contributes to reducing storage space requirements and processing time for data. To validate the effectiveness of the model, a case study is conducted using a predefined dataset. The results indicate that the model significantly streamlines the process of obtaining clean and accurate data. Nonetheless, further investigation is necessary to address additional aspects such as legal and privacy considerations pertaining to the collected data. Overall, this research presents a robust data collection quality model that addresses existing challenges and provides a foundation for improved utilization of big data in various domains.
The course of neuropsychiatric symptoms in patients with dementia in Norwegian nursing homes

ABSTRACT Background: Neuropsychiatric symptoms (NPS) are common in patients with dementia, and cause distress for patients. Studies on the prevalence, incidence, persistence and resolution of NPS in patients living in nursing homes are sparse. The aim of this study was to evaluate the course of NPS in patients with dementia living in Norwegian nursing homes. Methods: 169 patients from seven Norwegian nursing homes were assessed five times over a period of 16 months with the Neuropsychiatric Inventory (NPI). The severity and the frequency of the NPI were analyzed. Results: 91.7% of the patients had at least one clinically significant NPS at one or more assessments over the 16 months. Irritability (63.5%), agitation (51.0%) and disinhibition (50.0%) had the highest cumulative prevalence, while irritability (42.6%), disinhibition (37.8%) and depression (31.5%) showed the highest cumulative incidence. Delusion, agitation and irritability were enduring symptoms while the other symptoms had high resolution rates. The severity of the NPS did not vary significantly over time. Conclusion: Almost every patient in Norwegian nursing homes had at least one clinically significant NPS over 16 months, but individual NPS show a fluctuating course. This should influence how we monitor and treat NPS in patients with dementia.
Q-Learning Routing Protocol Applied to Energy Internet for Power Distribution

Several routing methods for power distribution have been presented in the literature. However, the majority either have a high computation time and complexity or do not support congestion management and dynamic networks. As a solution, this paper proposes a novel power routing protocol that allows congestion management, supports dynamic networks, and has low computation time and communication overhead. The power routing problem is treated as a distributed control and learning problem based on the multi-agent reinforcement learning (MARL) approach. We aim to find the best routing policy for minimizing power losses by supporting all power constraints. The proposed protocol is implemented in each energy router (ER) and requires only local information to choose the next router to transmit a power packet (PP) for a specific destination. Simulation results show that the Q-learning-based power routing protocol has low communication overhead (only local information is required), low computational time, and supports dynamic change in the power network. Finally, it outperforms Dijkstra’s algorithm in terms of computation time, communication overhead, and application in a dynamic environment.
Predicting the Helpfulness of Online Customer Reviews

Customers rely on online reviews when making purchase decisions on e-commerce platforms. Reading the massive amount of available reviews on products is a timeconsuming process. Hence, several online platforms allow customers to vote on whether a review is helpful. However, customers may consider the helpfulness voting process hard due to the number of reviews. This research paper presents an automated review helpfulness prediction method called HelpPrd. HelpPrd adopts text mining and machine learning techniques to predict the review helpfulness. The presented approach extracts features from the textual reviews to train three helpfulness classifiers. The experimental results demonstrate that HelpPrd-logistic regression achieved better performance than other implementations of HelpPrd in terms of accuracy, precision, recall, and F1-score.
Deterministic fabrication of circular Bragg gratings coupled to single quantum emitters via the combination of in-situ optical lithography and electron-beam lithography

In the present work, we investigate the coupling of deterministically pre-selected In(Ga)As/GaAs quantum dots (QDs) to low Q circular Bragg grating cavities by employing a combination of state-of-the-art low-temperature in-situ optical lithography and electron-beam lithography. The spatial overlap between the cavity mode and quantum emitter is ensured through the accurate determination of the QD position via precise interferometric position readout. Simultaneously, the high precision of the electron-beam lithography is exploited for the cavity fabrication. In order to optimize the spectral overlap, prior to cavity fabrication, finite-difference time-domain simulations are performed to estimate the spectral position of the cavity mode. A Purcell factor of 2 together with an increased count rate is reported for a deterministically positioned cavity where the emission line is detuned by 3.9 nm with respect to the cavity mode. This non-negligible Purcell enhancement for large detunings and, thus, the large range where this can be achieved points towards the possibility of using the cavity for the simultaneous enhancement of spectrally distinct transitions from the same quantum emitter located spatially in the mode maximum. Furthermore, investigations on the bending of the cavity membrane and the effects on the cavity mode and QD emission are presented.In the present work, we investigate the coupling of deterministically pre-selected In(Ga)As/GaAs quantum dots (QDs) to low Q circular Bragg grating cavities by employing a combination of state-of-the-art low-temperature in-situ optical lithography and electron-beam lithography. The spatial overlap between the cavity mode and quantum emitter is ensured through the accurate determination of the QD position via precise interferometric position readout. Simultaneously, the high precision of the electron-beam lithography is exploited for the cavity fabrication. In order to optimize the spectral overlap, prior to cavity fabrication, finite-difference time-domain simulations are performed to estimate the spectral position of the cavity mode. A Purcell factor of 2 together with an increased count rate is reported for a deterministically positioned cavity where the emission line is detuned by 3.9 nm with respect to the cavity mode. This non-negligible Purcell enhancement for large detunings and, thus, the large ra.
ANALISIS SPASIAL KERAPATAN BANGUNAN DAN PENGARUHNYA TERHADAP SUHU Studi Kasus di Kabupaten Bogor

Kabupaten Bogor merupakan salah satu kabupaten yang saat ini pembangunannya cukup berkembang. Pada tahun 2016 jumlah penduduk di Kabupaten Bogor berjumlah 5.715. 009 jiwa. Bertambahnya penduduk menyebabkan berkurangnya lahan dengan tutupan vegetasi menjadi daerah yang terbangun, dari bertambahnya lahan terbangun meyebabkan meningkatnya suhu di Kabupaten Bogor. Dengan pemanfaatan Sistem Informasi Geografis (SIG) dapat diketahui peningkatan suhu akibat dari padatnya bangunan di Kabupaten Bogor. Melalui pengolahan Citra Landsat 8 OLI/TIRS C1 Level-1 dengan ukuran 30 x 30 m tahun 2018 digunakan metode Normalized Difference Vegetation Index (NDVI) untuk mengetahui indeks kerapatan vegetasi, Normalized Difference Built Index (NDBI) untuk mengetahui kerapatan bangunan, dan metode Land Surface Temperature (LST) untuk mengetahui suhu permukaan di Kabupaten Bogor. Hasil yang didapatkan dari penelitian ini adalah semakin rapatnya bangunan maka suhu semakin tinggi dan sebaliknya, begitu juga dengan luas dari wilayah yang mengalami kenaikan suhu.
Design and Build a System for Illegal Logging Detection Based on Acoustic Signal and Automatic Warning Using LoRA Network

The situation of illegal logging is severe in many countries, especially in Vietnam-a tropical country with more than 40% [1] of forest cover. Illegal logging contributes directly to global warming, natural disasters, and many other negative impacts on our world. However, this problem can be reduced and even prevented with proper and effective monitoring solutions. In this work, we proposed and designed a low-cost, long-lasting operation time and scalable device based on STM32F746 microcontroller and EdgeAI for illegal logging detection based on acoustic signal and LoRa for long range wireless communication. The device uses a microphone to capture short audio samples periodically, then executes audio preprocessing and processing using deep learning to perform audio recognition. Once an illegal logging is detected, the device can send warnings to users through a web interface built on the ThingsBoard IoT platform. We are able to achieve an accuracy of over 90% when evaluating the device with a large amount of audio samples not belong to the training dataset. By power consumption calculation and simulation, we proved that the device can operate independently in more than 1 month without charging.
Perspective Herb – Drug Interactions: Challenges and Opportunities for Improved Predictions

Supported by a usage history that predates written records and the perception that “ natural ” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb – drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb – drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb – drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. characteriz-ing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb – drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.
Ethnic Identity: Creation, Conflict, and Accommodation

In this thought-provoking reader of largely new or newly revised articles, scholars link ethnicity to language, nationalism, localism, religion and other issues in various crucial areas around the globe: former Yugoslavia, Eastern Europe and the Baltic States, Sri Lanka, Southeast and East Asia, Africa and Latino communities in the United States. An important professional resource and an excellent teaching tool for courses in anthropology and ethnic studies.
BreathSign: Transparent and Continuous In-ear Authentication Using Bone-conducted Breathing Biometrics

As one of the most natural physiological activities, breathing provides an effective and ubiquitous approach for continuous authentication. Inspired by that, this paper presents BreathSign, which reveals a novel biometric characteristic using bone-conducted human breathing sound and provides an anti-spoofing and transparent authentication mechanism based on inward-facing microphones on commercial earphones. To explore the breathing differences among persons, we first analyze how the breathing sound propagates in the body, and then derive unique body physics-level features from breathing-induced body sounds. Furthermore, to eliminate the impact of behavioral biometrics, we design a triple network model to reconstruct breathing behavior-independent features. Extensive experiments with 20 subjects over a period have been conducted to evaluate the accuracy, robustness, and vulnerability of BreathSign. The results show that our system accurately authenticates users with an average authentication accuracy rate of 95.17% via only one breathing cycle, and effectively defends against various spoofing attacks with an average spoofing attack detection rate of 98.25%. Compared with other continuous authentication solutions, BreathSign extracts hard-to-forge biometrics in the effortless human breathing activity for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.
Neuroactive Steroid–Gut Microbiota Interaction in T2DM Diabetic Encephalopathy

The pathological consequences of type 2 diabetes mellitus (T2DM) also involve the central nervous system; indeed, T2DM patients suffer from learning and memory disabilities with a higher risk of developing dementia. Although several factors have been proposed as possible contributors, how neuroactive steroids and the gut microbiome impact brain pathophysiology in T2DM remain unexplored. On this basis, in male Zucker diabetic fatty (ZDF) rats, we studied whether T2DM alters memory abilities using the novel object recognition test, neuroactive steroid levels by liquid chromatography–tandem mass spectrometry, hippocampal parameters using molecular assessments, and gut microbiome composition using 16S next-generation sequencing. Results obtained reveal that T2DM worsens memory abilities and that these are correlated with increased levels of corticosterone in plasma and with a decrease in allopregnanolone in the hippocampus, where neuroinflammation, oxidative stress, and mitochondrial dysfunction were reported. Interestingly, our analysis highlighted a small group of taxa strictly related to both memory impairment and neuroactive steroid levels. Overall, the data underline an interesting role for allopregnanolone and microbiota that may represent candidates for the development of therapeutic strategies.
Biosfer: Jurnal Pendidikan Biologi

The learning media used in schools is in the form of books. In addition, the media used by schools contains material that is not yet specific and not yet interactive, so it is necessary to develop media that suits needs, such as interactive e-books. This study aims to produce an interactive e-book learning media on the tissue culture sub-material on the structure and function of tissues in plants that are valid. This development research uses the ADDIE development model whose implementation reaches the third stage, namely Analysis, Design, and Development. This interactive e-book was created using the Flip PDF Corporate Edition application. The sampling technique used in this research is random sampling. The sample used in the study was 45 students of class XI IPA SMA. The data collection instruments used were validation sheets and validated student response questionnaires. Data collection was carried out using student and teacher response questionnaires. Product validation uses validation sheets carried out by material, media
Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems

The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad’s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.
Investigation of the Global Escarpment, Including the Fretted Terrain, in the Martian Northern Hemisphere

The global escarpment and associated fretted terrain are located in the Martian northern hemisphere. Two competing hypothesis presently in play explain the origin of Mars’ global escarpment. These hypotheses involve endogenic and exogenic processes and both could help explain the extreme topographic difference between the southern highlands and the northern lowlands. The focus of this study, the fretted terrain area of the global escarpment, is a transition zone of mesa-like features located directly north of the global escarpment. With the use of digital imagery analysis, georeferencing of existing maps, and the interpretation of current models, the most plausible origin of the escarpment proposed herein would be an exogenic process, namely a single, mega-scale impact shortly after formation of the planet. The main lines of evidence supporting this favored hypothesis are the modeled elliptical shaped basin, similarities between crustal thickness and topographic elevations, mineralogy, and the orientation and size distribution of the northern fretted terrain.