Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytochemical Analysis and Toxicity Assessment of Bouea Macrophylla Yoghurt

The Bouea macrophylla fruit is native to Malaysia and is known for its many beneficial effects on one’s health. Probiotics are well-known for their roles as anti-inflammatory, antioxidant, and anti-tumour properties due to their widespread use. As a result, the purpose of this study was to incorporate the ethanolic extract of Bouea macrophylla into yoghurt and then assess the rodents for any toxicological effects. According to the findings of the nutritional analysis, each 100 mL serving of the newly formulated yoghurt contains 3.29 g of fat, 5.79 g of carbohydrates, 2.92 g of total protein, and 2.72 g of sugar. The ability of the newly developed yoghurt to stimulate the growth of Lactobacilli was demonstrated by the fact that the peak intensity of Lactobacillus species was measured at 1.2 × 106 CFU/g while the titratable acidity of the lactic acid was measured at 0.599 CFU/g. In order to carry out the toxicological evaluation, forty-eight male Sprague Dawley (SD) rats were utilized. Oral administration of single doses of 2000 mg/kg over the course of 14 days was used for the study of acute toxicity. Subacute toxicity was studied by giving animals Bouea macrophylla yoghurt (BMY) at repeated doses of 50, 250, 500, and 1000 mg/kg/day over a period of 28 days, while the control group was given normal saline. The results of the acute toxicity test revealed that rats treated with increasing doses up to a maximum of 2000 mg/kg exhibited no signs of toxicity. After an additional 14 days without treatment, acute toxicity of a single dose (2000 mg/kg) of BMY did not show any treatment-related toxicity in any of the rats that were observed. According to the data from the subacute toxicity study, there were no differences between the treated groups and the control groups in terms of food and water intake, body weight, plasma biochemistry (AST, ALT, ALP, and creatinine), haematological products, or organ weights. The architecture of the liver, heart, and kidney were all found to be normal upon histological examination. This indicates that oral consumption of BMY did not result in any negative effects being manifested in the rodents.
Borel Cardinal Invariant properties of countable Borel equivalence relations

Boykin and Jackson recently introduced a property of countable Borel equivalence relations called Borel boundedness, which they showed is closely related to the unions problem for hyperfinite relations. In this paper, we introduce a family of properties of countable Borel equivalence relations which correspond to combinatorial cardinal invariants of the continuum in the same way that Borel boundedness corresponds to the bounding number $\mathfrak{b}$. We analyze some of the basic behavior of these properties, showing for instance that the property corresponding to the splitting number $\mathfrak{s}$ coincides with smoothness. We then settle many of the implication relationships between the properties; these relationships turn out to be closely related to (but not the same as) the Borel Tukey ordering on cardinal invariants.
A generalized reaching‐law‐based discrete‐time integral sliding‐mode controller with matched/mismatched disturbance attenuation

A generalized reaching‐law‐based (RL‐based) discrete‐time integral sliding‐mode controller, which is versatile for either matched or mismatched disturbances, is designed in this paper to obtain high output tracking accuracy and avoid tremendous control efforts. Specifically, a disturbance decomposition‐based discrete‐time integral sliding surface is designed, and a generalized discrete‐time reaching law is established. Different from the existing integral sliding surfaces, the proposed sliding surface synthesizes a disturbance‐related integral term that is defined based on disturbance decomposition; this is crucial to the disturbance attenuation. Moreover, different from the available discrete‐time reaching laws, the proposed reaching law introduces an adaptive exponential term into the control gains, and hence, the conventional RL‐based discrete‐time integral sliding‐mode control (DISMC) and the equivalent‐control‐based (EC‐based) DISMC can be integrated. Rigorous analysis shows that the closed‐loop system is stable, the control effort can be satisfactory, and the steady‐state output tracking accuracy is of order O(T2)$$ O\left({T}^2\right) $$ for both matched and mismatched disturbances. The proposed method is proven effective through numerical simulations.
Effects of Macleaya Cordata Extract on Performance, Nutrient Apparent Digestibilities, Milk Composition, and Plasma Metabolites of Dairy Goats

Simple Summary In this study, we aimed to investigate the effects of Macleaya cordata extract (MCE) supplementation on performance, nutrient apparent digestibilities, plasma metabolites, and milk quality in dairy goats. The active ingredients of MCE are sanguinarine and chelerythrine, which are from the medicinal plant (Willd.) R. Br. Dietary supplementation with MCE can increase performance and the apparent nutrient digestibility of fiber in dairy goats. Abstract In this study, we aimed to investigate the effects of Macleaya cordata extract (MCE) supplementation on performance, nutrient apparent digestibilities, plasma metabolites, and milk quality in dairy goats. Twenty-four lactating Guanzhong dairy goats (n = 24) were randomly divided into two groups (each containing 12 goats) in a 52-day trial: the CON group was fed a basal diet; the MCE group was fed a basal diet supplemented with 400 mg/kg MCE. The results indicated that the 4% fat corrected milk yield (4% FCM); uncorrected milk yield; milk-fat concentration; content of C4:0, C18:0, and C18:1n9c fatty acids in milk; and apparent digestibility of neutral detergent fiber (NDF) and acid detergent fiber (ADF) in the MCE group were significantly higher (p < 0.05). Furthermore, the lactoferrin (LTF), alpha-lactalbumin (α-La), and beta-lactoglobulin (β-Lg) of the milk and feed conversion rate (FCR) of the goats were significantly greater (p < 0.01) in the MCE group than in the CON group. In contrast, the somatic cell count (SCC) (p < 0.01), content of C14:0 fatty acids (p < 0.01) of milk, and blood urea nitrogen (BUN) concentrations (p < 0.05) were significantly lower in the in the MCE goats. These results show that the feeding of MCE can increase the performance and apparent nutrient digestibility of fiber in dairy goats, improving the quality of goat milk.
Medication Adherence of People Living with HIV in Japan—A Cross-Sectional Study

Long-term medical care for people living with HIV (PLHIV) is critical for treatment efficacy, and various studies have examined reasons for antiretroviral therapy (ART) non-adherence. In Japan, doctors assume patients maintain high adherence. However, little is known about real-world treatment adherence. We conducted an anonymous self-administered web-based survey asking about adherence for a total of 1030 Japanese PLHIV who were currently on ART. Adherence was determined using the eight-item Morisky Medication Adherence Scale (MMAS-8), for which scoring ranged from 0 to 8 and scores < 6 points were classified as low adherence. Data were analyzed based on patient-related factors; therapy-related factors; condition-related factors, such as a comorbidity with depression (utilizing the Patient Health Questionnaire 9, PHQ-9); and healthcare/system-related factors. Among 821 PLHIV who responded to the survey, 291 responders (35%) were identified as being in the low adherence group. A statistically significant relationship was found between the number of missed anti-HIV drug doses within the previous 2 weeks and long-term adherence, per the MMAS-8 score (p < 0.001). Risk factors for low adherence included age (younger than 21 years, p = 0.001), moderate to severe depression (p = 0.002, using the PHQ-9), and drug dependence (p = 0.043). Adherence was also influenced by a shared decision-making process, including treatment selection, doctor–patient relations, and treatment satisfaction. Adherence was mainly affected by treatment decision factors. Hence, support of care providers should be considered critical for improving adherence.
Crowd-Aware Mobile Robot Navigation Based on Improved Decentralized Structured RNN via Deep Reinforcement Learning

Efficient navigation in a socially compliant manner is an important and challenging task for robots working in dynamic dense crowd environments. With the development of artificial intelligence, deep reinforcement learning techniques have been widely used in the robot navigation. Previous model-free reinforcement learning methods only considered the interactions between robot and humans, not the interactions between humans and humans. To improve this, we propose a decentralized structured RNN network with coarse-grained local maps (LM-SRNN). It is capable of modeling not only Robot–Human interactions through spatio-temporal graphs, but also Human–Human interactions through coarse-grained local maps. Our model captures current crowd interactions and also records past interactions, which enables robots to plan safer paths. Experimental results show that our model is able to navigate efficiently in dense crowd environments, outperforming state-of-the-art methods.
Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services

Hospitals need to optimize patient care, as, among other factors, life expectancy has increased due to improvements in sanitation, nutrition, and medicines. Hospitalization-at-home (HaH) could increase admission efficiency, moderate costs, and reduce the demand for beds. This study aimed to provide data on the feasibility, acceptability, and effectiveness of the integration of IoT-based technology to support the remote monitoring and follow-up of patients admitted to HaH units, as well as the acceptability of IoT-based solutions in healthcare processes. The need for a reduction in the number of admission days, the percentage of admissions after discharge, and the actions of the emergency services during admission were the most relevant findings of this study. Furthermore, in terms of patient safety and trust perception, 98% of patients preferred this type of digitally-supported hospitalization model and up to 95% were very satisfied. On the professional side, the results showed a reduction in work overload and an increase in trust when the system was adopted.
Telegraph noise mechanism and LLG noise model

A preliminary study on random telegraph noise (RTN) is made. RTN is defined as the magnetization states switching by thermal fluctuations. State jumping resulting in baseline shift from the media field could also be RTN-associated multi-states switching. Our analysis shows that the root cause of such magnetization multi-states could be due to: (1) free layer edge states switching caused by a weak PM field; (2) surface and/or interface roughness, especially the lapped air bearing surface (ABS); and (3) random defects in the stack layers. A thermal noise model with Landau-Lifshitz-Gilbert equation (LLG) was developed and was applied to RTN demonstration.
Large-scale optimization methods applied to the cutting stock problem of irregular shapes

Abstract This paper describes a real feasibility study of applying large-scale optimization methods to the cutting stock problem of irregular shapes. We identify two approaches for minimizing waste in the cutting stock problem of irregular shapes: better packing and better scheduling of cuts. This paper is concerned with the scheduling problem only. By scheduling of cuts we mean deciding which combination of parts to group together on the cutting table so that overall material needed by all cuts is minimized. Such a problem usually requires considering many combinations. However, with the development of various feasibility requirements imposed on the column generation process this number can be reduced considerably. Furthermore, the introduction of interior-point algorithms for linear programming by Karmarkar in 1984, allows the consideration of much larger linear programming problems than was possible just a few years ago.
Perceived Value of Osteopathic Recognition.

BACKGROUND AND OBJECTIVES In 2020 the Accreditation Council on Graduate Medical Education (ACGME) became the sole accrediting body for osteopathic and allopathic residency programs, with an option for programs to apply for Osteopathic Recognition (OR) to distinguish their training in osteopathic principles and practice. There is limited research regarding this transition. The goal of our study was to assess the perceived value of OR and perceived difficulty of obtaining OR for family medicine residency programs. METHODS We performed analyses regarding the difficulty of obtaining OR status and the value of OR and Osteopathic Principles and Practice (OPP) using questions on the 2020 Council of Academic Family Medicine Educational Research Alliance (CERA) omnibus survey. RESULTS Of the 280 program directors (PD) responding, 69 (24.6%) had OR status, 126 (45.0%) were considering applying or would apply if needed resources were available, and 85 (30.4%) were not considering OR. Of the 73 PDs reporting on experience with the OR process, 28 (38.4%) found it "very smooth," 30 (41.1%) found it "a little bumpy," and 15 (20.5%) found it "very bumpy"; 87.0% of PDs (60 of 69) with OR felt it had value in recruiting DO students and 31.8% (22/69) in recruiting MD students; 86.9% of programs with OR status perceived OPP to be somewhat or very valuable in enhancing patient satisfaction compared to 77% of those considering OR and 44.7% not considering OR. CONCLUSIONS Program directors perceive value in OR status for recruiting and in osteopathic practice for patient care. Since 75.4% of responding program directors have or are interested in achieving OR status, further research is needed on its benefits and barriers.
Airbrushed PSF/ZnO Composite Coatings as a Novel Approach for the Consolidation of Historical Bones

In this work, the preparation and characterization of films based on polysulfone (PSF) filled with zinc oxide, ZnO, nanoparticles (NPs) are conducted. The novelty of this research mainly relies on two points: (i) the use of a commercial airbrush to prepare or modify materials, and (ii) the design of new materials (nanocomposites) for the consolidation and restoration of historical bones. To accomplish these objectives, free-standing thin films and ancient bone coatings of PSF/ZnO nanocomposites with different particle contents (0%, 1%, 2%, 5% and 10%, % wt) are prepared using a commercial airbrush. Mechanical characterization is carried out to correlate properties between free-standing thin films and coatings, thus understanding the final performance of the coatings as consolidants for ancient bones. Thin films of PSF/ZnO show that the elastic modulus (E) increases with particle content. The mechanical behavior of the surfaces of the treated and untreated bones is studied locally using Martens hardness measurements. Maximum values of Martens hardness are obtained for the bone samples treated with polysulfone filled with 1% ZnO nanoparticles (HM = 850 N·mm−2) or 2% ZnO (HM = 625 N·mm−2) compared to those treated just with neat PSF (HM = 282 N·mm−2) or untreated bone (HM = 140 N·mm−2), indicating there is a correspondence between rigidity of free-standing films and hardness of the corresponding coatings. In terms of mechanical performance, it is demonstrated the existence of a balance between nanoparticle concentration and probability of nanoparticle aggregation, which allows better material design for ancient bones consolidation.
Leveraging What Students Know to Make Sense of Texts: What the Research Says About Prior Knowledge Activation

This systematic literature review examined the research on prior knowledge and its activation to ascertain how these terms are defined, what specific techniques have been empirically investigated, and the conditions under which prior knowledge activation facilitated students’ comprehension. Fifty-four articles met the inclusion criteria and revealed that the terms prior knowledge and prior knowledge activation were often vaguely defined. Further, 30 unique techniques for activating readers’ prior knowledge representing eight different categories were identified. Those categories were open-ended prompts, procedural or strategic supports during reading, visual representations, analogical reasoning, text alteration, augmented activation, extratextual activities, and spontaneous activation. Techniques meant to facilitate knowledge activation prior to reading were most common, although the prompting of students’ existing knowledge was beneficial during and after reading as well. Variability in the effectiveness of activation techniques was related, in part, to the amount, accuracy, and specificity of students’ knowledge. Based on the key findings identified in this review, recommendations for future inquiry are forwarded, including suggested definitions of prior knowledge and prior knowledge activation.
Persistent Hypochloremia Is Associated with Adverse Prognosis in Patients Repeatedly Hospitalized for Heart Failure

Background: Hypochloremia reflects neuro-hormonal activation in patients with heart failure (HF). However, the prognostic impact of persistent hypochloremia in those patients remains unclear. Methods: We collected the data of patients who were hospitalized for HF at least twice between 2010 and 2021 (n = 348). Dialysis patients (n = 26) were excluded. The patients were divided into four groups based on the absence/presence of hypochloremia (<98 mmol/L) at discharge from their first and second hospitalizations: Group A (patients without hypochloremia at their first and second hospitalizations, n = 243); Group B (those with hypochloremia at their first hospitalization and without hypochloremia at their second hospitalization, n = 29); Group C (those without hypochloremia at their first hospitalization and with hypochloremia at their second hospitalization, n = 34); and Group D (those with hypochloremia at their first and second hospitalizations, n = 16). Results: a Kaplan–Meier analysis revealed that all-cause mortality and cardiac mortality were the highest in Group D compared to the other groups. A multivariable Cox proportional hazard analysis revealed that persistent hypochloremia was independently associated with both all-cause death (hazard ratio 3.490, p < 0.001) and cardiac death (hazard ratio 3.919, p < 0.001). Conclusions: In patients with HF, prolonged hypochloremia over two hospitalizations is associated with an adverse prognosis.
A robust approach for background subtraction with shadow removal for moving object detection

Background subtraction is the primary step that can be applied in numerous applications, including object tracking, surveillance, robot vision, object-based coding, image database and video teleconferencing. However, background subtraction approaches are very sensitive to dynamic scene changes. This paper aims to investigate ways to exploit the characteristics of the wavelet domain to improve moving object segmentation using background subtraction for object tracking applications. In this paper, we also innovate by using a high frequency sub-band to neutralise the negative influences caused by cast shadow, without using any colour information. The state-of-theart performance of the proposed approach is illustrated by using both indoor and outdoor video sequences.
Clarias Size Clustering to Determine Market Segmentation Using K-Means Algorithm

Clarias is one of the fish that is easily found in Indonesia. Besides being cheap, clarias is easy to find in the market, both in traditional and modern markets. There are quite a lot of types of markets in Indonesia, so a grouping analysis is needed considering the market location based on the size of the fish. This analysis aims to make it easier for clarias farmers to distribute their crops according to existing market segments. Therefore, this study conducted a segmentation analysis of the clarias market using the K-Means algorithm to help clarias farmers in distributing clarias harvests to the appropriate market. The results of this study indicate that the sillhoutte value in the clarias dataset obtained $\mathbf{K}$ values between 3 – 10 clusters (55.02% – 47.77%). The most optimal value is $\mathbf{K}=3$ with a sillhoutte index value of 55.02%. This means that the application of the K-Means algorithm provides positive assumptions for market segmentation so that clarias farmers can distribute clarias harvests to the right market.
IRS-Assisted Wireless Powered IoT Network With Multiple Resource Blocks

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.