Behavioral Profile Research Articles

Attachment security, environmental adversity, and fast life history behavioral profiles in human adolescents.

One species-general life history (LH) principle posits that challenging childhood environments are coupled with a fast or faster LH strategy and associated behaviors, while secure and stable childhood environments foster behaviors conducive to a slow or slower LH strategy. This coupling between environments and LH strategies is based on the assumption that individuals' internal traits and states are independent of their external surroundings. In reality, individuals respond to external environmental conditions in alignment with their intrinsic vitality, encompassing both physical and mental states. The present study investigated attachment as an internal mental state, examining its role in mediating and moderating the association between external environmental adversity and fast LH strategies. A sample of 1169 adolescents (51% girls) from 9 countries was tracked over 10 years, starting from age 8. The results confirm both mediation and moderation and, for moderation, secure attachment nullified and insecure attachment maintained the environment-LH coupling. These findings suggest that attachment could act as an internal regulator, disrupting the contingent coupling between environmental adversity and a faster pace of life, consequently decelerating human LH.

The Impacts of Co-Occurring Developmental Language Disorder on the Academic, Interpersonal, and Behavioral Profiles of Children With Attention-Deficit/Hyperactivity Disorder

Purpose: Attention-deficit/hyperactivity disorder (ADHD) is a multifactorial disorder that has been linked to increased risk for behavioral, academic, and interpersonal problems. Developmental language disorder (DLD) has also been linked to poor outcomes in these areas. The presence of co-occurring disorders in children's profiles is generally associated with poorer outcomes relative to profiles consisting of only one disorder. This study investigated the extent to which co-occurring DLD in children already identified with ADHD presented increased risk for severity of their primary ADHD symptoms, the presence of secondary behavioral problems, and/or increased problems in the tertiary areas of academic performance and interpersonal interactions. Method: Data from 90 children from combined community-based and clinically based study samples were used in this retrospective study. Behavioral, academic, and social attributes were assessed from parent ratings on the Child Behavior Checklist. Results: Results indicated primary symptoms of ADHD and related tertiary interpersonal consequences were not increased when co-occurring DLD was present. However, the co-occurrence of DLD on ADHD resulted in increased risk for secondary externalizing problems and deficits in academic performance. Conclusions: Outcomes highlight the need for high-quality collaborations among clinical professionals, school-based professionals, and parents to ensure the complex needs of children with ADHD are adequately addressed. Children with ADHD who have elevated externalizing behaviors and who are struggling academically should be tested for possible co-occurring DLD.

A Multimodal, In Vivo Approach for Assessing Structurally and Phenotypically Related Neuroactive Molecules.

A recently reported behavioral screen in larval zebrafish for phenocopiers of known anesthetics and associated drugs yielded an isoflavone. Related isoflavones have also been reported as GABAA potentiators. From this, we synthesized a small library of isoflavones and incorporated an in vivo phenotypic approach to perform structure-behavior relationship studies of the screening hit and related analogs via behavioral profiling, patch-clamp experiments, and whole brain imaging. This revealed that analogs effect a range of behavioral responses, including sedation with and without enhancing the acoustic startle response. Interestingly, a subset of compounds effect sedation and enhancement of motor responses to both acoustic and light stimuli. Patch clamp recordings of cells with a human GABAA receptor confirmed that behavior-modulating isoflavones modify the GABA signaling. To better understand these molecules' nuanced effects on behavior, we performed whole brain imaging to reveal that analogs differentially effect neuronal activity. These studies demonstrate a multimodal approach to assessing activities of neuroactives.

A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: Implications for guideline compliance and associations with cardiometabolic health.

Movement behavior features such as time use estimates, average acceleration and intensity gradient are crucial in understanding associations with cardiometabolic health. The aim of this study was to 1) compare movement behavior features processed by commonly used accelerometer metrics among adults (i.e. Euclidian Norm Minus One (ENMO), Mean Amplitude Deviation (MAD) and counts per minute (CPM)), 2) investigate the impact of accelerometer metrics on compliance with movement behavior guidelines, and 3) explore potential variations in the association between movement behavior features and cardiometabolic variables depending on the chosen metric. This cross-sectional study collected movement behavior features (Actigraph GT3X+) and cardiometabolic variables. Accelerometer data were analyzed by four metrics, i.e. ENMO, MAD, and CPM vertical axis and CPM vector magnitude (GGIR). Intraclass correlations and Bland‒Altman plots identified metric differences for time use in single movement behaviors (physical activity, sedentary behavior), average acceleration and intensity gradient. Regression models across the four metrics were used to explore differences in 24-hour movement behaviors (24h-MBs; compositional variable) as for exploration of associations with cardiometabolic variables. Movement behavior data from 213 Belgian adults (mean age 45.8±10.8 years, 68.5% female) differed according to the metric used, with ENMO representing the most sedentary movement behavior profile and CPM vector magnitude representing the most active profile. Compliance rates for meeting integrated 24h-MBs guidelines varied from 0-25% depending on the metric used. Furthermore, the strength and direction of associations between movement behavior features and cardiometabolic variables (body mass index, waist circumference, fat% and HbA1c) differed by the choice of metric. The metric used during data processing markedly influenced cut-point dependent time use estimates and cut-point independent average acceleration and intensity gradient, impacting guideline compliance and associations with cardiometabolic variables. Consideration is necessary when comparing findings from accelerometry studies to inform public health guidelines.

Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias

We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that animals achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes, thereby reducing behavioral sensitivity to these outcomes. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons.

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state 1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are minimally affected by neuromodulatory inputs and are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

Inteligencia artificial y pulsión. Operatividad del hook model y affective computing en plataformas digitales

We are currently witnessing the rise of a platform capitalism that bases a significant part of its economy on producing behavioral profiles to direct users' actions towards private ends. By associating radical behaviorism techniques with algorithmic data processing technologies, a force that Bernard Stiegler has identified as "psychopower" has intensified and consolidated. This article aims to demonstrate how this is achieved by deploying two control technologies in the architecture of digital platforms: Affective Computing and the Hook Model. Through an ethnographic study on the BeReal social network, we show how these two technologies first capture users' attention and create usage habits and second, promote the circulation of emotions so that these can be linked to specific contexts and datafied to develop behavioral profiles. Finally, we conduct a theoretical exercise to argue that both control technologies are key elements of a new power dispositif that we call "pulsional," which triggers an action in individuals that bypasses their conscious reflection, leading to detrimental consequences for the exercise of their freedom.

PSIII-10 Flaxseed oil-based supplement alters water consumption and plasma fatty acid profile of beef heifers

Abstract The objective of this study was to determine the effects of a flaxseed oil-based supplement (FlaxLic, New Generations, SD) on reproductive parameters, feeding behavior, and lipid profile in beef heifers. Crossbred Angus heifers [n = 60 body weight (BW) = 397kg ± 5.2 kg) were blocked by BW and antral follicle count and randomly assigned to two supplementation treatments: free-choice commercial lose mineral supplement (CON; n = 30) or free-choice flaxseed oil-based supplement (FLAX; n = 30) for 7 wk. Heifers were individually fed via the Insentec feeding system (Insentec RIC, Hokofarm, Marknesse, Netherlands), where basal diet (TMR) and supplements were provided in separate feeders to measure supplement intake and feeding behavior. Further, water intake and behavior were measured via Insentec water system. Individual intake was compiled over 24 h for each animal. Body weight, transrectal ultrasonography, and blood samples were collected weekly. On wk 5, heifers were enrolled in a fixed-time artificial insemination (FTAI) commercial protocol. Largest follicle diameter (LF) and corpus luteum (CL) presence were recorded weekly and on d of the FTAI protocol (d 0, d 7, and d 0). Pregnancy diagnosis was performed 30 d and 60 d following AI. Blood samples were analyzed for progesterone (P4) by radioimmunoassay (RIA) to determine puberty attainment before wk 5, and fatty acid profile was determined via gas chromatography-flame ionization detection. Heifers with a CL or P4 ≤ 1.0 ng/mL were considered pubertal. Data were analyzed using SAS 9.4. ADG was similar between groups (P = 0.90) but was affected by week (P &amp;lt; 0.01). TMR intake was similar in duration and quantity (P &amp;gt; 0.18) between groups. Supplement intake was greater (P &amp;lt; 0.01) for FLAX (0.73 ± 0.03 kg/d) compared with CON (0.18 ± 0.02 kg/d) and from wk 2 to wk 8 (P = 0.01). Time spent consuming the supplements was greater for FLAX compared with CON heifers (62.65 ± 2.11 vs 0.10 ± 0.11 min/d, respectively) over the entire trial (P &amp;lt; 0.01). Further, FLAX heifers consumed less water than CON heifers (32.2 ± 0.39 vs 33.93 ± 0.35 kg/d, respectively; P = 0.03) and from wk 2 to wk 7 (P = 0.03). LF was not different between groups on any of the days of the protocol (d 0, d 7, d 0; P &amp;gt; 0.20). Additionally, P4 (P ≤ 0.20), puberty attainment by wk 5 (P = 0.24), and pregnancy rates (P &amp;gt; 0.48) were similar between groups. Concentrations of arachidonic acid (3.00 ± 0.24 vs 2.32 ± 0.19 mg/mL; P = 0.04), linoleic (60.11 ± 4.34 vs 40.35 ± 4.04 mg/mL; P = 0.002), αLinolenic (16.73 ± 0.26 vs 15.86 ± 0.23 mg/mL; P = 0.02), and total polyunsaturated fatty acids (PUFA; 130.52 ± 5.97 vs 111.19 ± 5.56 mg/mL; P = 0.02) were greater on wk 8 for FLAX compared with CON heifers. Collectively, these findings did not show evidence that the flaxseed oil-based supplement affects variables that impact reproductive performance. However, it altered the plasma lipid profile, increasing omega-3, arachidonic, and linoleic fatty acids, as well as total PUFA.

393 The role of on-animal sensors in understanding feed intake, behavior, and performance of grazing livestock

Abstract Intake by grazing animals is difficult to reliably measure in the field, particularly in more diverse grazing systems where pastures vary greatly across time and space in resource availability, which in turn leads to greater variation in animal time budgets and intake patterns. Advances in remote livestock monitoring (e.g., on-animal sensors, walk -over-weighing), and advanced analytics such as machine learning algorithms can contribute to the development of robust, adaptable tools for better understanding of animal performance and behavior in grazing systems. Many existing tools for predicting livestock production require estimates of feed intake, and while estimates based on forage sward characteristics have been developed and used for temperate, relatively homogenous pastures, they are less well defined for more diverse rangelands. These diverse pastures are more difficult to characterize due to their greater variation in quantity and quality; consequently, animal selectivity is greater in more variable pastures. However, such feedback loops between pasture diversity and animal behavior may not yet be adequately represented in biophysical models. On-animal sensors such as accelerometers and GPS devices can be used to develop new ways of assessing relative intake by providing accurate estimates of animal location and behavior profiles, such as the use of machine learning algorithms to detect how much time is spent grazing, walking, ruminating, or resting. Measurements of animal behavior could then be combined with other animal-based methods such as walk-over-weighing and fecal NIR to improve estimates of intake and production for a wide range of extensive grazing environments. Sensor-derived measurements of animal movement could also be used to improve estimates of the energetic costs of grazing, thereby improving estimates of energy available for growth. As an example, data on recent activity and paddock use from on-animal sensors could be entered into a model to estimate current feeding requirements, then combined with weather and pasture quality forecasts. The ensuing baseline could then be used to explore the potential variability of these projections and generate a range for possible expected performance over the next month or season. This approach could be repeated over time, and animal performance and behavior measures used to update model estimates and improve forward projections. These methods could also be used in conjunction with estimates of animal energy requirements to provide real-time prediction of relative feed intake, which would provide a step-change in modelling livestock production in extensive grazing systems. For these approaches to be successful, additional calibration data are needed across a wide variety of environments and species, and the tools must be cost-effective to implement. In summary, on-animal measurements provide an adaptable, data-driven approach that could be integrated with existing tools to improve our understanding of feed intake, behavior, and performance of grazing animals.

Considering management context when investigating behavioral profiles in a group of captive Javan gibbons (Hylobates moloch)

ABSTRACT Effective animal husbandry aims to produce behavioral profiles consistent with those observed in wild counterparts. However, few studies have examined the impacts of management history on sociality. We investigate social dynamics in a group of Javan gibbons (Hylobates moloch) at Perth Zoo using behavioral and proximity data. The group consists of an aging adult female and her two offspring – a maturing sub-adult female and a juvenile male. This composition diverges from commonly observed groupings in the wild. The study group’s social relationships generally align with species-typical behaviors, with the exception of the patterning of play behavior and resting-in-contact. These results indicate an uncharacteristically close bond between the mother and her maturing same sex-offspring, as well as an unexpectedly distant mother-juvenile bond. These relationships are best explained by the group’s management history and resulting atypical group composition including: the absence of an adult male, the mother’s advanced age, and the hand-rearing of the juvenile male. This study gives insight into the effects of a captive setting on social development and provides further evidence of social flexibility in gibbons.

124 Effects of Saccharomyces cerevisiae and dietary type offered to beef cattle on intake, feeding behavior, nutrient digestibility, and ruminal fermentation

Abstract The effects of live yeast and diet type offered to beef steers on intake, feeding behavior, nutrient digestibility, and ruminal fermentation profile were evaluated. Ruminally cannulated beef steers [n = 8; body weight (BW) = 550 ± 20 kg] were used in a duplicated 4 × 4 Latin square design following a 2 × 2 factorial arrangement of treatments: A) presence of live-yeast [Saccharomyces cerevisiae (CNCM I-1077), at 1 × 1010 CFUּ animal-1ּ d-1]; and B) diet type [steam-flaked corn-based grower or finisher diets (ad libitum intake)]. Live yeast was delivered with gel-capsules (0.25 g, as is) twice daily via ruminal-cannula. Four, 35 d periods were used. Feed refusal was subtracted from offered (dry matter basis) to calculate feed intake (DMI). The 24 h feeding behavior assessment was conducted on d 31 by trained personnel (time spent eating, ruminating, chewing, drinking, resting, and meal size/number). A 5-d fecal collection (twice daily) was used for the digestibility assessment. Ruminal pH was continuously measured (7 d, intra-ruminal Dascor wireless data loggers). Ruminal ammonia-N and volatile fatty acids (VFA) were analyzed at 0, 2, 4, 8, 16, and 23 h on d 35. The GLIMMIX procedure of SAS was used with animals as the experimental unit. No live yeast × diet interactions (P ≥ 0.17) were observed. Intake was not affected (P = 0.83) by live yeast, while steers offered the grower diet consumed more (P ≤ 0.01) NDF, ADF, and hemicellulose compared with the finisher diet. Live yeast increased (P ≤ 0.02) the dietary NDF, ADF, and hemicellulose digestibility of the grower diet by 13.7, 12.8, and 14.6%, and on the finisher diet by 6.7, 9, and 6.4%, respectively. Live yeast did not affect (P ≥ 0.23) major feeding behavior activities measured. Steers offered grower diet spent more time (P ≤ 0.01) on each meal, ruminating, eating, and chewing and less time (P &amp;lt; 0.01) resting. Live yeast did not affect (P ≥ 0.81) ruminal pH variables measured. Steers offered the grower diet had a greater (P &amp;lt; 0.01) ruminal pH pattern compared with the finisher diet. The ruminal ammonia-N and volatile fatty acids were not affected (P ≥ 0.16) by live yeast, while steers offered the finisher diet had a greater (P ≤ 0.01) total VFA and propionate molar proportion, while a lower (P ≤ 0.01) ammonia-N and acetate molar proportion compared with the grower diet. Regardless of the diet type, live yeast improved digestibility of fiber fractions, while dry matter intake was unaffected. Grower diets were more prone to stimulate feeding behavior activities than finisher, while live yeast seemed to not affect such variables. Expected effects of diet type on ruminal fermentation were ratified, while live yeast inclusion did not affect the ruminal fermentation variables evaluated.

Spectroscopic analysis of tungsten spectra in extreme-ultraviolet range of 10-480 Å observed from EAST tokamak with full tungsten divertor

Tungsten spectra in extreme ultraviolet (EUV) wavelength range of 10-480 Å have been observed from high-temperature plasmas in Experimental Advanced Superconducting Tokamak (EAST) with full tungsten divertor using four fast-time-response EUV spectrometers of EUV_Short (5-45 Å), EUV_Long_a (40–180 Å), EUV_Long_c (130–330 Å) and EUV_Long_b (270–480 Å) and two space-resolved EUV spectrometers of EUV_Short2_d (45–70 Å) and EUV_Long2_d (40–130 Å). The wavelength of measured spectra is accurately calibrated based on several well-known spectral lines emitted from low-Z (He, Li, C, N and O), medium-Z (Fe and Cu) and high-Z (Mo) impurity ions. Measurements of the tungsten spectra were taken from discharges accompanied with a transient tungsten burst event, which creates a pulsed influx of tungsten atoms into the EAST plasma. The tungsten spectra observed before and after the burst event are carefully analyzed with temporal behavior and radial profile distribution of the tungsten line intensity. As a result, 213 tungsten lines are successfully confirmed in the spectra observed after the tungsten burst, and the results are summarized in tables. These tungsten lines include line identifications of 78 lines of W XXIII - W XLVI (W22+ - W45+) at 10–140 Å and 88 lines of W V - W IX (W4+ - W8+) at 160–480 Å, while 47 tungsten lines at 50–380 Å could not be clarified the transition. In addition, quasi-continuum spectra called unresolved transition array (UTA) from tungsten ions in low- and high-ionization stages are also analyzed in three wavelength ranges of 18–38 Å, 45–70 Å and 150–280 Å at which W XXIII - W XXXVIII (W22+ - W37+), W XXVII - W XLVI (W26+ - W45+) and W VI—W IX (W5+ - W8+) are dominantly emitted, respectively. Through the analysis it is found that charge state distributions of tungsten UTA at 140–220 Å significantly vary with decrease in the electron temperature. Ionization stages of all observed tungsten lines including both isolated and quasi-continuum lines are experimentally reconfirmed by comparing the radial intensity profile with the electron temperature profile. Finally, spectral lines useful for tungsten diagnostic in fusion plasmas are selected and marked in the tables.

Information Systems and Mechanism for Prevention of Cyber Frauds

Cyber criminals are targeting online frauds, exploiting anonymity to deceive. They use fake websites, fake ads and stolen credit or debit cards for purchases. Bank frauds, despite high-speed processing and technical assistance, can damage a bank's reputation and operational efficiency, necessitating a strong emphasis on business ethics in the banking sector. The machine learning and artificial intelligence enhance online security of information systems. US enforces anti-trust laws and promotes stakeholder rights, addressing fraud and identity theft through safety tips and civil law implementation. Cyber criminals steal personal information for unauthorized purchases, identity theft, and fraudulent activities. Machine learning and artificial intelligence can enhance online security and user awareness. AI-based threat detection analyzes network activity for cyberattacks, limiting damage. Multi-Factor Authentication (MFA) combines traditional passwords with biometric authentication for enhanced security. The application of Big Data systems allows administrations for the collection and analysis of Data and its storage obtained from various sources, using platforms like Hadoop, Apache Spark, and Kafka for real-time processing and data analytics. Digital devices are increasingly being used in banking frauds, posing significant risks to both customers and the banking sector, necessitating stricter regulations and enforcement measures. Common banking sector issues include negligent, fraudulent, and deviant behavior, affecting various functions of getting, gathering, transporting, disbursing, loaning, trading, capitalizing, replacing, and servicing money-claims domestically and internationally. This paper discusses machine learning and anomaly detection techniques for preventing fraud in online payment systems, including behavioral profiling and Bagged Decision Tree models along with other methods.

Risk factor profile for non-communicable diseases: findings of a STEPS survey among the support staff at the University of Pretoria, South Africa

Background: Non-communicable diseases (NCDs) are an important problem in South Africa; they account for 60% of all deaths. Early screening could help lower NCD incidences and long-term consequences. Objective: The study aimed to profile NCD risk factors among insourced support staff at the University of Pretoria in 2018. Design: A cross-sectional, descriptive study was conducted at the University of Pretoria, Hatfield Campus. The World Health Organization (WHO) STEPwise approach to chronic disease risk factor surveillance (STEPS) questionnaire was administered to collect data in three steps. The participants’ behavioural risk profiles were gathered using the face-to-face interview technique (STEP I), followed by their physical risk profile using anthropometric and blood pressure measurements (STEP II). Last was the biochemical risk profile, including finger-prick blood glucose and cholesterol measurement (STEP III). Data were analysed using Epi-Info, version 3.54. Subjects: A convenient sample of participants aged 18–64 years from the Departments of Industrial Hygiene and Building Services, Landscape Services, Sports Fields Management, and Security Services (n = 146, 60% were females) took part in the study. Results: Most participants (97.8%) had low daily fruit and vegetable intake. Some 80% of the study population always or often added salt to their food when cooking. Daily alcohol consumption was reported by a quarter of the sample. More than two-thirds of all the participants were overweight or obese and 61% had central obesity. Conclusion: The study identified a high prevalence of several NCD risk factors. Tailored nutrition education and monitoring are needed to lower the elevated risk.

Design and Evaluation of New Gel-Based Floating Matrix Tablets Utilizing the Sublimation Technique for Gastroretentive Drug Delivery.

A gel-based floating matrix tablet was formulated and evaluated using the sublimation technique to enhance gastroretentive drug delivery. Anhydrous theophylline was employed as the active pharmaceutical ingredient, combined with sublimation agents and hydroxypropyl methylcellulose as the gel-forming polymer. The resulting tablets exhibited high porosity, immediate floatation, and sustained buoyancy for over 8 h. Optimization of the floating behavior and drug release profiles was achieved by adjusting the viscosity of and hydroxypropyl methylcellulose and the concentration of sublimation agents, specifically ammonium carbonate and menthol. These agents were selected for their effectiveness in creating a porous structure, thus reducing tablet density and enhancing floatation. Higher HPMC viscosity resulted in increased floating force, slower drug release, and improved swelling properties due to a slower erosion rate. A critical assessment of the balance between tablet porosity, mechanical strength, and drug release kinetics indicates that ammonium carbonate provided superior tablet hardness and lower friability compared to menthol, favoring a controlled release mechanism. The release dynamics of theophylline were best described by the anomalous (non-Fickian) diffusion model, suggesting a combined effect of diffusion and erosion. This research advances the development of gastroretentive drug delivery systems, highlighting the potential of sublimation-based floating matrix tablets for sustained drug release.

Artificial Intelligence Based Multi-Layer Approach for Finding Unknown Attacks in Cloud Network by Using Hybrid Intrusion Detection

Objectives : The objective of this study is to explore Intrusion Detection Systems and their various types by gathering research from previously published articles in refereed journals. The focus is on developing a proposed model capable of identifying unknown attacks in cloud networks using Signature and Anomaly-based Intrusion Detection Systems. Subsequently, the efficiency of the proposed model will be assessed, and a comparison with existing models will be conducted. The paper's main objective is to identify unknown attacks in a cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach. Methods: Leveraging insights from existing literature, the proposed model combines signature-based IDS for known threat detection and anomaly-based IDS for detecting unusual behavioral patterns indicative of new or unseen attacks. Experimental evaluations using NSL-KDD and ADFA datasets demonstrate competitive accuracy and detection rates, with the proposed artificial intelligence-based Hybrid IDS achieving high performance in detecting both normal and malicious activities. Findings: This model produces above 90%, 96%, and 98% efficiency in the wired, Wireless, and Cloud networks respectively, and this model finds known attacks effectively while using parameters like event logs, file transferring time, TCP and UDP addresses, CPU Usage, Weak and synthetic data, IP and MAC address. Existing literature said that the existing model using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The findings suggest that the artificial intelligence-based multi-layered approach offers a promising solution for enhancing cloud network security, with the potential for further optimization and integration of advanced technologies in future research endeavors. Novelty: This study presents an artificial intelligence-based multi-layered approach for detecting unknown attacks in cloud networks by integrating signature-based and anomaly-based intrusion detection systems (IDS). The authors developed the model to detect the intrusion by using the Behaviour Profiling algorithm and dynamically prevent the data from intrusion by using the Statistical approach model. The authors trying to find unknown attacks, therefore the authors defined the objective of this paper as to find the unknown attacks in cloud networks by using the combination of signature and anomaly-based intrusion detection systems. The objective is to develop a model capable of effectively identifying cyber threats in cloud environments. The existing models do not concentrate on identifying unknown attacks by using Signature-based Intrusion Detection. Very few of the literature said that known attacks can be identified easily by using Signature-based Intrusion Detection but the unknown attacks identifying process is hard. Some of the Literature said that using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The current paper identifies unknown attacks in the cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach and it produced above 97% accuracy. The unique feature of the model is artificial intelligence-based multi-layered approach and dynamic key have been utilized to avoid malicious activities in the network. Keywords: Anomaly based IDS, Cloud Network Security, Hybrid IDS, Multi-Layer Approach, Signature based IDS

UHT A2 milks marketed in Brazil: Physicochemical and electrical characterization, rheological behavior and fatty acid profile

UHT A2 Milk offers a consumption alternative for consumers experiencing intestinal discomfort from consuming dairy products. A2 milk, derived from cows with the A2A2 genotype, does not promote the formation of betacasomorphin-7 (BCM-7). However, little is known about the comprehensive composition of this product, as well as its impact on physicochemical and electrical parameters. This study aimed to investigate potential physicochemical and chemical differences between UHT milk with the A2 β-casein genotype and conventional milk without A2 certification, sourced from brands available in the Brazilian market. Therefore, the physicochemical parameters, rheological behavior, electrical impedance, and fatty acid composition were evaluated. The results indicated that A2 milk had higher density, fat, protein, and total solids content compared to UHT milk without A2 certification. Furthermore, it obtained a higher electrical impedance modulus and lower admittance within the analyzed frequency range (1 kHz a 5 MHz). The UHT milk samples showed Newtonian fluid behavior at both temperatures analyzed (10 and 25 °C). Furthermore, the type of UHT milk (with and without A2 certification) did not significantly influence the absolute viscosity of the samples (p > 0.05) across both commercial brands and temperature. Overall, both types of milk had the same composition of fatty acids, with palmitic acid being the major compound. However, C12:0 and C18:1 cis-7 were more abundant in A2 milk. In this sense, the analytical methods used in this study were able to identify differences in macromolecules and compounds within the lipid fraction in the samples investigated. These analytes are suggested as potential identity markers to evaluate the authenticity of UHT bovine containing only beta-casein of the A2 genotype in its composition.

Understanding the Residential Water Demand Response to Price Changes: Measuring Price Elasticity with Social Simulations

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand.

Parenting behavior profiles and adolescents’ self-handicapping: an analysis from the perspective of self-determination theory

Parenting behavior profiles and adolescents’ self-handicapping: an analysis from the perspective of self-determination theory