Miracle-dz/UniFAR-FTUs huggingface dataset:
Parents' Reactions and their Reflection on Externalizing Behavior of their Children at Early Childhood: Literature Review

Background: Externalizing behavior at early childhood and its effects on development are recognized as a public health issue that compromises child's social, emotional, and learning abilities as well as those of their families, friends, other adults, and even the environment. There are many familial, contextual, and child-related risk factors due to externalizing behavior among children. One such risk factor is a positive link between early externalizing behavior and high levels of negative parenting, which is most likely to manifest persistent problems when children start to enter school. Various therapies are frequently employed to control externalizing behavior. These interventions are typically available to all parents in the general population as a preventive strategy and support the effectiveness of parenting programs in margining children who already demonstrate externalizing behavior. Nurses have a unique chance to collaborate with parents, teachers, and school psychologists for the aim of early identification, prevention, and intervention of externalizing behavior problems at an early childhood, which saves more significant and expensive issues in later life. This article review describes Parents' reactions and their reflection on externalizing behavior of their children.
Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters—namely heart rate variability, oxygen saturation, and body profiles—to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.
Taxic and morphological diversification during the early radiation of Clupeomorpha (Actinopterygii, Teleostei)

Evolutionary radiation is a problematic concept whose definition and classification have recently changed. Radiations can be defined as the pattern of abrupt increase in diversity of a lineage. It is relevant to evaluate the presence and interaction of different types of radiation in extant and fossil organisms to adequately delimitate the radiation types and to know the diversity in the context of Earth's history. Here, we employed the superorder Clupeomorpha at the Early–Late Cretaceous boundary as a study case to investigate radiation types and their interactions, using both taxic and morphological approaches. Clupeomorpha is an extensively studied, diverse and ancient teleostean superorder with wide geographical and ecological distributions. We propose a model for calculating rates of origination in order to analyse the taxic diversification and employ geometric morphometrics to analyse the morphological diversification that occurred at the temporal boundary. The results suggest the absence of taxic radiation due to a constant increase in taxon origination. However, the expansion of the phylomorphospace occupation and the disparity increase suggest the presence of a ‘climatic–geographical’ or ‘broad diversification‐like’ disparity, according to current classifications. This illustrates the incompatibility of current radiation classifications with this case study.
Cavernous weathering forms in SW Iceland: a case study on weathering of basalts in a cold temperate maritime climate

Abstract The article is a case study of the development of cavernous weathering forms (alveoli and tafoni) on a sea cliff near Keflavik on the Reykjanes peninsula in southwestern Iceland. The majority of forms are found on vertical or inclined rock faces and cluster around the uppermost edge of the cliff. Single hollows gradually evolve due to back wearing of their sidewalls into complex caverns, until finally the whole outermost layer of basalt is removed. Particular attention is paid to mineralogical changes of basalts due to chemical and salt weathering. Chemical decomposition of feldspars, pyroxenes and olivines followed by precipitation of iron on and within the weathering rind, the presence of micro- and macro-pores such as gas bubbles and delivery of marine salts are proposed to be the key factors influencing cavern development.
Kyoto: Seven Paths to the Heart of the City

What better way to see Kyoto than at dawn, when the back streets and alleys of this 1,200-year-old city are still under the spell of the past? Old folks fuss about with their bonsai and laundry poles, pausing to chat on their way to the neighborhood shrine with flowers and morning prayers. Knock-kneed white egrets stalk the stream beds for breakfast, and the giant bronze temple bells awaken the former capital of Japan every morning as they have done for centuries. Through wars, fires, famines, tyrants, and now rapid modernization, the old neighborhoods of Kyoto are the places where the customs and traditions of this fascinating city, with its confusing blend of old and new, have managed to survive. American writer and former Kyoto resident Diane Durston introduces seven must-see precincts of this ancient capital city, including four Historic Preservation Districts. Among them are the world-famous geisha quarter, the kimono textile neighborhoods, the sake-brewing area of Fushimi, and the tea-growing region of Uji. Durston weaves together local lore and historical sites to create a panoply of provocative walking tours that take the reader back in time to the elegance of earlier days. Accompanying each section is a full-color map and the fine photography of Katsuhiko Mizuno, a Kyoto native and one of the city's most famous photographers. Sections highlighting Kyoto crafts, cuisine, festivals, and tourist essentials round out this attractive volume, making it the perfect introduction -and guide- to one of the world's most evocative cities. FEATURES * Photographs by one of Kyoto's most renowned photographers * Locations of the most important Historic Preservations Districts * Detailed maps and suggested routes * Scenic and little-known walking paths * Completely revised and updated information
Linear and non-linear relations between the high-frequency-limit conductivity, AC-ECM signals and ECM signals of Dome F Antarctic ice core from a laboratory experiment

Abstract Laboratory experiments were done to better understand the electrical conduction mechanisms of impure, polycrystalline ice as represented by the 2503 m Dome Fuji (Antarctica) ice core. Also, two electrical measurement techniques for ice cores were compared and their usefulness for determining the acidity of ice cores was studied. We measured the electrical conductivity and complex permittivity of 167 slab-ice samples at frequencies from 20 Hz to 1 MHz. Measurements were performed at –21˚C for all samples, and at –110˚ to –20˚C for several samples, to examine the effects of temperature. We found linear relations between the AC loss factor and the molarity of sulfuric acid, and between the high-frequency-limit conductivity and the AC loss factor. Thus, the acidity levels can be determined from the AC loss factor. In contrast, the electrical conductivity measurement (ECM) current correlated weakly with the other parameters; furthermore, the correlation worsens at larger signal. In several samples containing high acidity, the dielectric properties had distinct changes near –81˚C. We argue that these changes were caused by a change from a liquid-vein-mediated conduction mechanism above the eutectic point of the solute/water/ ice system to a solid-phase conduction mechanism at lower temperatures.
Analysis of Patient Satisfaction on Disease Polyclinic Services in Pirngadi General Hospital with Fuzzy Service Quality Method

The needs and demands of patients for health services are one of the basic needs. The hospital must know the extent of patient satisfaction with the services provided by the hospital. The purpose of this study was to determine patient expectations for the services received and find out what are the priority improvements to improve the quality of service at Pirngadi Hospital. The method used in this study is the servqual approach combined with fuzzy theory so that the measurement of perceptions and expectations of patients can be measured accurately. The highest gap value is the reliability dimension of -28,17. Thus it can be seen that the reliability dimension is a concern to be able to improve the quality of service especially in ways and attitudes of health workers in serving patients which has the highest gap value of -31,25 of all question variables.
Revisiting Estimation of Agricultural Production Function for Sustainable Agricultural Policies in Sub-Saharan Africa (SSA): Evidence from Togo.

In Sub-Saharan Africa, the bulk of agricultural output is produced by small holder farmers who continue to depend on rainfall. As such, agricultural economists often use average rainfall as a summative environmental indicator in estimating agricultural production function. This methodology is flawed. A close scrutiny of agricultural practices /agronomic sciences reveals that agricultural output is more determined by rainfall distribution than average rainfall. This relationship is explored in the Togolese context. The conclusion reached is that, between 1965 and 1992, intra-annual rainfall distribution measured by its standard of deviation has not been relevant in explaining the variation of food production in Togo due to the continuous degradation of the ecosystems. This result provides additional information for improved decision making and calls for urgent account of environmental aspects in the formulation of sustainable agricultural policies in SSA.
Isolation of euryhaline microalgal strains from tropical waters of Brunei Darussalam for potential biomass production

Abstract. Roseli W, Tanaka Y, Taha H. 2023. Isolation of euryhaline microalgal strains from tropical waters of Brunei Darussalam for potential biomass production. Biodiversitas 24: 4651-4660. Rapid urbanization has increased the accumulation of excess nitrogen (N) and phosphorus (P) in natural water bodies (eutrophication). Microalgae are seen as a viable candidate to resolve the problem due to their ability to absorb nutrients from the eutrophicated water (phycoremediation). Euryhaline microalgae could significantly benefit because they can tolerate various salinities while maintaining high biomass productivity. This study aimed to isolate euryhaline microalgae from tropical waters and evaluate the algae's potential to produce biomass and absorb nutrients in a wide range of salinities. Three strains labeled W1, W2, and W3 were isolated from Brunei waters and confirmed by DNA barcoding as Desmodesmus sp. , Micractinium sp. , and Chlorococcum sp. , respectively. These strains were screened for salinity tolerance by exposing them to eight conditions (salinity 0, 5, 10, 15, 20, 25, 30, and 35) and evaluated for their high growth performance based on optical density. While the growth of Desmodesmus sp. and Chlorococcum sp. decreased from its highest value by 51% and 45%, respectively, the growth of Micractinium sp. was only reduced by 30%, and therefore, Micractinium sp. was considered to be the most euryhaline species and studied further in the next scale-up experiment. Micractinium sp. was cultivated at salinities of 0, 15, and 30 and recorded the highest biomass production of 54.1 ± 6.0 mg L? 1 d? 1 at salinity 15. Furthermore, they produced high amounts of N and P in their biomass, indicating that they are suitable for the phycoremediation of eutrophic water bodies. Therefore, Micractinium sp. is a potential candidate for open-pond cultivation because of its tolerance to a wide range of salinities and high biomass productivity.
Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site

Mining of anti-diabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning (DL) model called BERT-DPPIV, specifically designed to classify DPP-IV-IPs and exploring their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned bidirectional encoder representations (BERT) architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in benchmark dataset showed BERT-DPPIV yielded state-of-the-art accuracy of 0.894, surpassing the 0.797 obtained by sequence-feature model. Furthermore, we leverage the attention mechanism to uncover that our model could recognize restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules of DPP-IV inhibitory tripeptides and pentapeptides were validated and they can be used to screen potent DPP-IV-IPs.
Expanding access to evidence-based psychotherapy in VA settings: implementation of the brief cognitive behavioral therapy for depression program

Introduction Evidence-based psychotherapies (EBPs) are effective for mental health conditions, but access to these services remains limited and rural Veterans are particularly underserved. Specialized implementation and dissemination programs are needed to improve access to known EBPs. Methods The current project sought to improve access to a known EBP—brief Cognitive Behavioral Therapy for depression (Brief CBT). Diverse Veterans and those from rural settings were a focus of this work. Aligned with the RE-AIM framework, a multifaceted implementation program was used to train and support VHA providers in their use of Brief CBT in VHA mental health settings, with specific outreach efforts made to providers at VHA Community-Based Outpatient Clinics (CBOCs) where rural Veterans often receive care. Evaluation included all facets of RE-AIM with a particular focus on adoption, effectiveness, and maintenance. Results During the first two years, over 40 VHA facilities adopted the program across four regional networks. Eighty-three providers were approached, and 54 (65.1%) providers completed the training and are delivering the intervention. A total of 688 Veterans, 174 rural (25.7%), received 2,186 sessions (average of 3.5 sessions per Veteran). Veterans receiving Brief CBT with elevated depression scores who completed three or more sessions were found to have significant symptom reductions of 4.6 points (first to last available evaluations). Discussion Implementation efforts of Brief CBT resulted in rapid uptake and significant clinical impact on Veterans. Rural outreach efforts, including targeted training for CBOC providers and use of tele-mental health, enhanced availability of EBP services for rural Veterans.
Stability analysis of a pressure‐solution surface

We present a linear stability analysis of a dissolution surface subjected to non‐hydrostatic stress. A sinusoidal perturbation is imposed on an initially flat solid/fluid interface, and the consequent changes in elastic strain energy and surface energy are calculated. Our results demonstrate that if the far‐field lateral stresses are either greater, or much smaller than the fluid pressure, the perturbed configuration has a lower strain energy than the initial one. For wavelengths greater than a critical wavelength this energy decrease may be large enough to offset the increased surface energy. Under these conditions, the perturbation grows unstably. If these conditions are not met, the surface becomes flat. The growth rate and wavelength of the maximally unstable mode depend on the mechanism of matter transport. We conclude that the instability discussed in this paper may account for the formation of stylolites and other pressure‐solution phenomena, such as roughening of grain contacts.
Effects of Treadmill Running at Different Light Cycles in Mice with Metabolic Disorders

Type 2 diabetes mellitus accounts for about 90% of cases of diabetes and is considered one of the most important problems of our time. Despite a significant number of studies on glucose metabolism, the molecular mechanisms of its regulation in health and disease remain insufficiently studied. That is why non-drug treatment of metabolic disorders is of great relevance, including physical activity. Metabolic changes under the influence of physical activity are very complex and are still difficult to understand. This study aims to deepen the understanding of the effect of physical exercise on metabolic changes in mice with diabetes mellitus. We studied the effect of forced treadmill running on body weight and metabolic parameters in mice with metabolic disorders. We developed a high-fat-diet-induced diabetic model of metabolic disorders. We exposed mice to forced treadmill running for 4 weeks. We determined glucose and insulin levels in the blood plasma biochemically and analyzed Glut-4 and citrate synthase in M. gastrocnemius muscle tissue using Western blotting. The research results show that daily treadmill running has different effects on different age groups of mice with metabolic disorders. In young-age animals, forced running has a more pronounced effect on body weight. At week 12, young obese mice had a 17% decrease in body weight. Body weight did not change in old mice. Moreover, at weeks 14 and 16, the decrease in body weight was more significant in the young mice (by 17%) compared to the old mice (by 6%) (p < 0.05). In older animals, it influences the rate of glucose uptake. At 60 min, the blood glucose in the exercised older mice decreased to 14.46 mmol/L, while the glucose concentration in the non-exercised group remained at 17 mmol/L. By 120 min, in mice subjected to exercise, the blood glucose approached the initial value (6.92 mmol/L) and amounted to 8.35 mmol/L. In the non-exercised group, this difference was 45%. The effects of physical activity depend on the time of day. The greater effect is observed when performing shift training or exercise during the time when animals are passive (light phase). In young mice, light phase training had a significant effect on increasing the content of Glut-4 in muscle tissue (84.3 ± 11.3%, p < 0.05 with control group—59.3 ± 7.8%). In aged mice, shift training caused an increase in the level of Glut-4 in muscle tissue (71.3 ± 4.1%, p < 0.05 with control group—56.4 ± 10,9%). In the group of aged mice, a lower CS level was noticed in all groups in comparison with young mice. It should also be noted that we observed that CS increased during exercise in the group of young mice, especially during light phase training. The CS content in the light phase subgroup (135.8 ± 7.0%) was higher than in the dark phase subgroup (113.3 ± 7.7%) (p = 0.0006). The CS decreased in aged chow-fed mice and increased in the high-fat-fed group. The CS content in the chow diet group (58.2 ± 5.0%) was 38% lower than in the HFD group (94.9 ± 8.8%).
Dynamic Analysis of Multiplex Networks With Hybrid Maintenance Strategies

The advent of smart terminals and the IOT era has prompted the emergence of the multiplex network system. With the rapid information transmission in multiplex networks, security incidents caused by malicious attackers occur frequently. In light of the foregoing, this paper concentrates on modeling and analyzing the propagation process of multiplex networks. Particularly, this paper proposes an epidemic-based RCMO (Running- Confined- Malfuntioned- Overhauled) model after considering the operating state of equipment and a class of visibility-aware malware. After that, the stability analysis of the equilibria is performed to verify the effectiveness of the propagation threshold obtained in RCMO. Then, we introduce static controls including unique control, target control, acquaintance control, and pulse control, as well as dynamic controls including continuous-time and pulsed forms to the suppression strategies for preventing the spread of malware. Furthermore, the control strategies are cross-combined into $6\times 6$ hybrid maintenance strategies (HMS), and the simulation analysis is performed from three perspectives: evolution of state variables, accumulation of revenue, and change of controls. We discovered through experimental results that the optimal HMS to inhibit the propagation of malware and the HMS with the highest revenue are always different under various network topologies, but they are all hybrid combinations of continuous-time and pulse controls. To some extent, dynamic control can reduce discrepancies between the HMS and generate approximate returns. Finally, we propose a few cyber defense recommendations for network administrators.
BR-HIDF: An Anti-Sparsity and Effective Host Intrusion Detection Framework Based on Multi-Granularity Feature Extraction

Host-based intrusion detection systems (HIDS) have been widely acknowledged as an effective approach for detecting and mitigating malicious activities. Among various data sources utilized in HIDS, system call traces have gained significant popularity due to their inherent advantage of providing fine-grained information. Nevertheless, conventional feature extraction techniques relying on system calls tend to overlook the issue of high-dimensional sparse feature space. In this paper, we conduct a theoretical analysis to investigate the underlying causes of the sparsity problem. Subsequently, we propose an anti-sparse theory (anti-ST) as a solution to address this issue. Then, we design a multi-granularity feature extraction method (MGFE), which also meets the prerequisite mathematical conditions of the anti-ST. By applying this method, we effectively reduce the size of the feature space and minimize the number of generated features, thus mitigating sparsity. Furthermore, leveraging this approach, we propose a robust and anti-sparsity host intrusion detection framework, known as the MGFE-based Host Intrusion Detection Framework (BR-HIDF). A series of experiments were conducted to evaluate the proposed framework and compare it with the state-of-the-art method. The results demonstrate that our framework achieves impressive accuracy (97.26%), precision (97.62%), recall (96.85%), and F1 score (97.23%) in the intrusion detection task, surpassing existing frameworks. Moreover, the proposed framework significantly reduces the time overhead by 38.80%, exhibiting the highest AUC value of 0.992. Furthermore, we enhance the robustness of the detection system by integrating host-based and network-based detection, which provides greater flexibility in identifying various types of attacks.