Current Work Research Articles

A Further Step towards a Comprehensive Theory of Affectivity

Based on the conclusions of our last published paper on affectivity, we decided to go a step further and look at the implications of its development. First, we return to the cyclical and bivalent view of the model that we have elaborated after several years of empirical research and logical reflection. We attempt to detail the interconnection of these cycles (which together constitute the affective mode of information processing), with the rest of the behaviour (mainly the cognitive mode of information processing), and with each other, continuing with the work of the interactionist current of the emotion/cognition interaction. Finally, we outline some practical applications of these theoretical advances.

Performance Evaluation of Deep Learning Techniques in The Detection of IOT Malware

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.

Synthesis of Some Novel 4-bromobenzoic Acid Clubbed Hydrazone Schiff Base Derivatives as Potent α-amylase Inhibitors: In vitro and In silico Studies

Aims: Synthesis of novel 4-bromobenzoic acid-based hydrazone-Schiff base derivatives and to screen them for their α-amylase inhibitory activity. Objective: The biological activities of hydrazone-Schiff base compounds encouraged us to evaluate the synthesized derivatives (4-32) for in-vitro inhibition activity against the α-amylase enzyme. Methods: In current research work twenty-nine Schiff base derivatives (4-32) of 4-bromobenzoic acid were synthesized in worthy yields by treating various replaced aldehydes with 4- bromobenzohydrazide using methanol solvent in catalytic quantity of acetic acid. The products were structurally described through the support of several spectroscopic methods (EI-MS and 1HNMR) and finally evaluated against α-amylase enzyme. Results: All the made derivatives exhibited worthy inhibition potential from IC50 = 0.21 ± 0.01 to 5.50 ± 0.01 μM when equated to the usual acarbose drug having IC50 = 1.34 ± 0.01 μM. Compound 21 (IC50 = 0.21 ± 0.01 μM) was established as the most active inhibitor among the series better than standard. The structure-activity relationship study showed that the alteration in the activity of the produced products might be due to the attached position and nature of the substituents. Furthermore, in-silico study supported the effects of groups attached on the binding interaction with α-amylase enzyme. Conclusion: A series of substituted hydrazone Schiff bases based on 4-bromobenzoic acid were produced, confirmed the structures by EI-MS and 1H-NMR spectroscopic methods and lastly tested for their in-vitro α-amylase inhibitory potential. Among the series, twenty-four products indicated brilliant inhibition potential having IC50 values from 0.21 ± 0.01 to 1.30 ± 0.01 μM. The structure-activity relationship study showed that the alteration in the activity of the synthesized products might be due to the attached position and nature of the substituents. On the other hand, in silico studies advocated that the synthesized Schiff base derivatives have prevalent interactions of binding within the active site of the α-amylase enzyme, and because of their various attached substituent, their conformation is altered in the active site of the enzyme. The current study recognized a number of lead candidates derived from 4-bromobenzoic acid. Additional investigation of the synthesized derivatives for coming research to get novel α-amylase inhibitors.

Biginelli Reaction: A Multi-Component Type of Reaction and Synthetic Advancement in the Synthesis of Bioactive Dihydropyrimidinone Derivatives

Background: In synthetic and medicinal chemistry, multi-component reactions (MCRs) are considered an essential tool in synthesizing bioactive heterocyclic scaffolds. These reactions have been strategically used in drug discovery and development because of ease and economy. Objective: The current manuscript aims to highlight the importance of the Biginelli reaction in the synthesis of diverse dihydropyrimidinones with medicinal applications. Methods: We searched various keywords, including “multicomponent reaction”, “Biginelli reaction” and “dihydropyrimidinone” on “PubMed, PubChem, and google scholar” and collected the relevant articles for including the current work. Results: Biginelli reaction involving ketoester, aldehyde, and urea is a high-yielding, atomeconomical, environmentally benign reaction for developing a library of new dihydropyrimidinones to drive the process of drug discovery. Several developments were achieved with modifications of synthetic techniques, including C-H activation, coupling, cycloaddition, etc. Inclusively, these modifications give access to a wide range of dihydropyrimidinones. Conclusion: The current review provides an overview of recent developments in the Biginelli reaction and insights into synthesizing bioactive dihydropyrimidinones.

Practical Attribute Reconstruction Attack Against Federated Learning

Existing federated learning (FL) designs have been shown to exhibit vulnerabilities which can be exploited by adversaries to compromise data privacy. However, most current works conduct attacks by leveraging gradients calculated on a small batch of data. This setting is not realistic as gradients are normally shared after at least 1 epoch of local training on each participant's local data in FL for communication efficiency. In this work, we conduct a unique systematic evaluation of attribute reconstruction attack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared local model gradients after 1 epoch of local training can still reveal sensitive attributes of local training data. To demonstrate this leakage, we develop a more effective and efficient gradient matching based method called cos-matching to reconstruct the sensitive attributes of any victim participant's training data. Based on the reconstructed training data attributes, we further show that an attacker can even reconstruct the sensitive attributes of any records that are not included in any participant's training data, thus opening a new attack surface in FL. Extensive experiments show that the proposed method achieves better attribute attack performance than existing state-of-the-art methods.

Forum on Canada and the United Nations: Why Canada should marginalize the UN

The history and current workings of the United Nations suggest it is not an effective vehicle for the promotion of Canadian interests and values. As it has generally been, the Security Council is paralyzed by great power vetoes, while the General Assembly has an overwhelming majority that reliably votes against the West, and against Canada. The creation of the postwar liberal international order owed more to other multilateral organizations and groupings with which Canada is associated than the UN. In its core function of underwriting global security and stability, the UN has generally failed and often been irrelevant, with the crucial understandings reached between the great powers directly. In an age of increased international polarization, the UN will remain of only modest utility and Canada would be better advised to forward its values and interests primarily through other multilateral vehicles, working with like-minded states.

Factors associated with construction apprenticeship completion in the United States

Abstract The construction industry is experiencing high demand for workers. Apprenticeship programmes are essential pipelines of skilled workers into the construction industry; however, apprenticeship completion rates are only around 25%. To promote apprenticeship retention and increase the number of apprentices, it is necessary to identify factors that relate to cancellation from apprenticeship programmes (i.e., leaving prior to programme completion). Using data from the Registered Apprenticeship Partners Information Database System, we descriptively characterised completion and cancellation, then conducted a time-to-event analysis of n = 335,212 construction apprentices from 2012 to 2023 to examine factors related to cancellation. Among all apprentices, 40.1% cancelled from their apprenticeship programmes, while 24.8% completed and 35.0% were actively registered at the end of the study period. Results from the time-to-event analysis show females had significantly higher odds of cancellation than males (OR: 1.11; 95% CI: 1.08, 1.15). Compared to White apprentices, American Indian/Alaska Native (OR: 1.13; 95% CI: 1.08, 1.18), Black/African American (OR: 1.41; 95% CI: 1.39, 1.44), and multiracial apprentices (OR: 1.09; 95% CI: 1.02, 1.17) had significantly higher odds of cancellation, while Asian apprentices had significantly lower odds of cancellation (OR: 0.79; 95% CI: 0.75, 0.83). Non-unionised workers were significantly more likely to cancel their apprenticeship programmes (OR: 1.77; 95% CI: 1.74, 1.80). These results indicate that individual demographic and organisational factors can influence apprenticeship cancellation. Reducing barriers to apprenticeship completion can help address the current skilled worker shortage, and identifying factors that impact entry into the industry for minoritised groups can promote equity within the industry.

Formulation and Optimization of Solid Lipid Nanoparticle-based Gel for Dermal Delivery of Linezolid Using Taguchi Design.

Linezolid (LNZ) is a synthetic oxazolidinone antibiotic approved for the treatment of uncomplicated and complicated skin and soft tissue infections caused by gram-positive bacteria. Typically, LNZ is administered orally or intravenously in most cases. However, prolonged therapy is associated with various side effects and life threatening complications. Cutaneous application of LNZ will assist in reducing the dose, hence minimizing the unwanted side/adverse effects associated with oral administration. Dermal delivery provides an alternative route of administration, facilitating a local and sustained concentration of the antimicrobial at the site of infection. The current research work aimed to formulate solid lipid nanoparticles (SLNs) based gel for dermal delivery of LNZ in the management of uncomplicated skin and soft tissue infections to maximise its benefits and minimise the side effects. SLNs were prepared by high-shear homogenisation and ultrasound method using Dynasan 114 as solid lipid and Pluronic F-68 as surfactant. The effect of surfactant concentration, drug-to-lipid ratio, and sonication time was investigated on particle size, zeta potential, and entrapment efficiency using the Taguchi design. The main effect plot of means and signal-to-noise ratio were generated to determine the optimized formulation. The optimized batch was formulated into a gel, and ex-vivo permeation study, in-vitro and in-vivo antibacterial activity were conducted. The optimised process parameters to achieve results were 2% surfactant concentration, a drug-to-lipid ratio of 1:2, and 360 s of sonication time. The optimized batch was 206.3± 0.17nm in size with a surface charge of -24.4± 4.67mV and entrapment efficiency of 80.90 ± 0.45%. SLN-based gel demonstrated anomalous transport with an 85.43% in vitro drug release. The gel showed a 5 cm zone of inhibition while evaluated for in vitro antibacterial activity against Staphylococcus aureus. Ex-vivo skin permeation studies demonstrated 20.308% drug permeation and 54.96% cutaneous deposition. In-vivo results showed a significant reduction in colony-forming units in the group treated with LNZ SLN-based gel. Ex-vivo studies ascertain the presence of the drug at the desired site and improve therapy. In-vivo results demonstrated the ability of SLN-based gel to significantly reduce the number of bacteria in the stripped infection model. The utilization of SLN as an LNZ carrier holds significant promise in dermal delivery.

The Application of Kinesin Inhibitors in Medical Issues.

Kinesins are a group of motor proteins in charge of several crucial functions in the cell. These proteins often bind to microtubules and perform their functions using the energy produced by ATP hydrolysis. One function of mitotic kinesin, a subclass of kinesin that is expressed during cell division at the mitotic phase, is to create the mitotic spindle. Uncontrolled cell growth is one trait of cancerous cells. Traditional anticancer medications still used in clinics include taxanes (paclitaxel) and vinca alkaloids (vincristine, vinblastine), which interfere with microtubule dynamics. However, because non-dividing cells like post-mitotic neurons contain microtubules, unwanted side effects like peripheral neuropathy are frequently found in patients taking these medications. More than ten members of the mitotic kinesin family play distinct or complementary roles during mitosis. The mitotic kinesin family's KSP, or Eg5, is regarded as its most dramatic target protein. The current work systematically reviews the use of kinesin inhibitors in the medical field. The challenges of KSP and the practical solutions are also examined, and the outcomes of the previous works are reported. The significant gaps and shortcomings of the related works are also highlighted, which can be an onset topic for future works.

Investigating child-facing practitioners' understanding of adversity and its impact on children's development and service provision

Adversity during childhood is a common risk factor for poorer outcomes across physical, mental, and social health. Despite growing evidence and policy around preventing adversity and its sequalae, the incidence of adversity in childhood remains high. Child-facing practitioners (CfPs) may be well-placed to identify adversity and address its impact on children. This study investigated the understanding of adversity and current practice of CfPs working in the Education system, Healthcare and Social Care in England. An anonymous online survey was completed by 113 CfPs between April and June 2022. Data were analysed using descriptive statistics. Participating CfPs' reported using a range of assessment tools and direct observation, but there was a lack of consistency in the extent to which different types of adversity were explored in assessments. CfPs working in the Education system and Healthcare reported only liaising with Social Care services when a current worker was known to be involved with the family, or to make a referral. Indeed, a quarter of respondents from the Education system and Healthcare only considered early life experiences when capacity allowed. Over half of the CfPs in this survey 'did not know' or 'did not agree' that public services offer interventions to support families experiencing domestic abuse, parental mental health difficulties and addiction, or the impact of these adversities on children's wellbeing. The study highlights that CfPs could benefit from further training about the prevalence and impact of adversity to inform service delivery. A review of CfPs' routine assessments is needed to ensure that children's exposure to adversity is routinely identified; this will facilitate families to access appropriate support to mitigate the impact of such experiences.

A Rationalized Approach to Design and Discover Novel Non-steroidal Derivatives through Computational Aid for the Treatment of Prostate Cancer.

Prostate cancer is one of the most prevalent cancers in men, leading to the second most common cause of death in men. Despite the availability of multiple treatments, the prevalence of prostate cancer remains high. Steroidal antagonists are associated with poor bioavailability and side effects, while non-steroidal antagonists show serious side effects, such as gynecomastia. Therefore, there is a need for a potential candidate for the treatment of prostate cancer with better bioavailability, good therapeutic effects, and minimal side effects. This current research work focused on identifying a novel non-steroidal androgen receptor antagonist through computational tools, such as docking and in silico ADMET analysis. Molecules were designed based on a literature survey, followed by molecular docking of all designed compounds and ADMET analysis of the hit compounds. A library of 600 non-steroidal derivatives (cis and trans) was designed, and molecular docking was performed in the active site of the androgen receptor (PDBID: 1Z95) using Auto- Dock Vina 1.5.6. Docking studies resulted in 15 potent hits, which were then subjected to ADME analysis using SwissADME. ADME analysis predicted three compounds (SK-79, SK-109, and SK-169) with the best ADME profile and better bioavailability. Toxicity studies using Protox-II were performed on the three best compounds (SK-79, SK-109, and SK-169), which predicted ideal toxicity for these lead compounds. This research work will provide ample opportunities to explore medicinal and computational research areas. It will facilitate the development of novel androgen receptor antagonists in future experimental studies.

Towards Fairness-Aware Federated Learning.

Recent advances in federated learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL and overlook the interests of the FL clients. This may result in unfair treatment of clients, which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse fairness-aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This article aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by the existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation, and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches and suggest promising future research directions toward FAFL.

Connect to Crowd

Crowdsourcing (CS) involves gathering needed services, ideas, or content by soliciting contributions from a broad audience, particularly from online communities. In our proposed system, we aim to implement a budget-constrained online crowdsourcing framework by introducing a two-tiered social crowdsourcing architecture. To improve these models, we create incentive mechanisms that enhance computational efficiency, individual rationality, budget feasibility, cost accuracy, and time accuracy. Crowdsourcing platforms are vital for promoting transparency and collaboration among participants, all while reducing resource use and costs. To support this, these platforms include mechanisms for monitoring and evaluating worker performance. Our project introduces a secure multi-tiered worker quality evaluation framework utilizing blockchain technology. Our Proof of Concept (PoC) analysis demonstrates that blockchain effectively addresses security concerns such as trust, privacy, and accountability in current worker performance evaluation systems

Pre-clinical Aspects and Contemporary Treatments of Parkinson's Disease.

After Alzheimer's disease, the second slot for the most common neurodegenerative disease, is occupied by Parkinson's disease. The symptoms of Parkinson's are classified as motor symptoms and non-motor symptoms. Motor symptoms involve rigidity, tremors, bradykinesia, and postural instability. Non-motor symptoms consist of cognitive dysfunction, salivation, lacrimation, etc. Objectives: The objectives of this study are to find out the most recent treatment options for Parkinson's disease. Research and review papers are collected from different databases like Google Scholar, PubMed, Mendeley, Scopus, Science Open, and the Directory of Open Access Journals using different keywords such as "Parkinson's disease, biomarkers, animal models". Currently, various novel therapeutics have been emerging for PD. These may include treatments that may control the symptoms without causing any other severe side effects with already available treatments. Better therapies such as gene therapies, cell-based treatments, and regenerative therapies, which may evolve over time, can be a better therapeutic option. There is a need for the development of novel and potential therapeutic strategies that offer fewer side effects to patients. Several clinical, biochemical, and imaging markers that are noteworthy in Parkinson's disease examination have been discussed here. Current work in the field of Parkinson's disease has developed a variety of significant small animal models, such as viral vector models and seeding models, including the insertion of preformed fibrils of alpha-synuclein. The brief concepts regarding risk factors, pathogenesis, clinical diagnosis, and emerging treatments of PD are discussed in this review article.

Supporting the willingness to express emotions in relationships: The role of perceived empathic effort and interpersonal accuracy.

Expressing emotions with others can be difficult as it puts individuals in a position of potential vulnerability. Research suggests that people are willing to express their emotions with communal partners; however, few studies have examined processes that might explain how this occurs. Using a cross-sectional design, we examined interpersonal accuracy and empathic effort as factors that support the likelihood of expression in communal relationships. Participants (N = 219) reported the communal motivation, accuracy, and effort they perceived from five targets varying in closeness (e.g., best friend, acquaintance, etc.); they rated their likelihood of expressing happiness, pride, gratitude, sadness, anxiety, guilt, and anger with each target. Perceived accuracy and effort were both significant mediators of the association between perceived communal motivation and reported likelihood of expressing all emotions. Perceived accuracy was a stronger predictor of the likelihood of expression than effort. These findings indicate that perceiving greater accuracy and effort each independently supports a greater likelihood of expression. A willingness to express emotions is critical to developing close relationships and the current work identifies processes that may encourage this willingness. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Treatment of Reactive Orange 16 Dye-Bearing Wastewater by Electro-Fenton Process with Stainless-Steel Electrodes: Statistical Optimization and Operational Analysis

Abstract: In the current work, the Electro-Fenton (EF) based Reactive Orange 16 (RO16) dye treatment was studied and compared with central composite (CC) and Taguchi design (TD) statistical optimization tools. Color removal (RC) and COD decay (RCOD) were chosen responses for the effect of pH (A), electrolysis time (B), initial dye concentration (C), and current density (D). The facecentred CC design and L16 orthogonal array were used in the experimental procedures. At optimal conditions, the coefficient of determination (R2) values of 0.99 for CC and 0.97 for TD suggest statistical significance and good model agreement. The results of the ANOVA and Prob. > F values supported the model’s successful experimental data fitting. Taguchi method was found as an appropriate methodology for parameter percentage contributions with fewer experimental runs. Moreover, the S/N ratio charts proved to be a successful CC design replacement. The current density and pH were found to be the most important factors for the EF process. A higher biodegradability (BOD5/COD) and minimum iron concentration (0.45 mg/L) in the effluent sludge demonstrated good environmental disposal suitability. In the last, the effect of various inhibitors/scavengers (SO4 −2, PO4 −3, EDTA, etc.) on the EF process performance was also carried out.

Learning With Incremental Instances and Features.

In many real-world applications, data may dynamically expand over time in both volume and feature dimensions. Besides, they are often collected in batches (also called blocks). We refer this kind of data whose volume and features increase in blocks as blocky trapezoidal data streams. Current works either assume that the feature space of data streams is fixed or stipulate that the algorithm receives only one instance at a time, and none of them can effectively handle the blocky trapezoidal data streams. In this article, we propose a novel algorithm to learn a classification model from blocky trapezoidal data streams, called learning with incremental instances and features (IIF). We attempt to design highly dynamic model update strategies that can learn from increasing training data with an expanding feature space. Specifically, we first divide the data streams obtained on each round and construct the corresponding classifiers for these different divided parts. Then, to realize the effective interaction of information between each classifier, we utilize a single global loss function to capture their relationship. Finally, we use the idea of ensemble to achieve the final classification model. Furthermore, to make this method more applicable, we directly transform it into the kernel method. Both theoretical analysis and empirical analysis validate the effectiveness of our algorithm.

Safety culture and worker fatigue management in the offshore oil and gas industry: An interview study

Multiple incidents in the offshore oil and gas industry have been associated with poor safety culture. Regular assessments of safety culture among operators and contractors is recommended as part of a safety management system. Poor safety culture has also shown to impact how operators manage offshore hazards, such as worker fatigue. Assessing workers' fatigue states is also critical to ensure safety in the offshore oil industry. This paper describes findings from an interview study that aimed to identify current safety culture assessment and worker fatigue management practices in the offshore oil and gas industry. One-hour virtual semi-structured interviews were conducted with eighteen offshore oil rig supervisors. Various state-of-the-art methods for assessing safety culture (e.g., experience sampling method) and worker fatigue (e.g., physiological sensors and psychomotor vigilance test) were introduced to the participants. Participants commented on the feasibility and potential barriers to implementation/administration of the various methods, as well as how the information might be useful in their supervisory decisions. User expectations for a safety dashboard displaying data from such tools and user requirements for such a dashboard were elicited. In addition, participants completed a modified technology readiness and acceptance model questionnaire to assess participants' readiness levels and perceived usefulness of a safety dashboard. The interview results revealed a mixed understanding of what safety culture is and opinions about safety culture measurements. Participants indicated that efforts to manage fatigue currently relied solely on supervisors' observation and workers' self-reports. Participants’ opinions about the new assessment methods varied. Some were supportive and commented that the new methods will be helpful to improve supervisory-level decisions, whereas others pointed out potential compliance issues.

Armature Reaction Analysis and Suppression of Voice Coil Actuator Based on an Improved Magnetic Equivalent Circuit Model

The high thrust density voice coil actuator (VCA) is a key component for the deployers to determine the CubeSats' separation velocity in orbit directly. To improve the VCA's thrust density and power consumption, it is essential to analyze the armature reaction under heavy current work conditions. This paper proposes a dynamic splitting method of the magnetic equivalent circuit (MEC) model for analyzing the armature reaction of the VCA. This MEC model can effectively reveal the interaction mechanism between the dynamic winding field and the permanent magnet (PM) field, which provides an approach to readily and quickly analyze the armature reaction. Based on the understanding of the mechanism, we found an effective way to suppress the armature reaction and propose a new VCA with double PM flux circuits. The electromagnetic performances of the new configuration are evaluated by comparing it with the conventional one. Both the theoretical and experimental results show that the new structure achieves lower magnetic saturation, lower armature reaction, and higher thrust density. A research prototype was constructed and experimentally tested to verify the theoretical results of the armature reaction, magnetic field, thrust, separation velocity, and other performances. The new VCA realizes a wide and accurate modulation of separation velocity for the CubeSats.

Black individuals' suspicion of Whites' motives negatively predicts expected efficacy with White social partners.

Interracial efficacy is critical to intergroup interactions. However, the antecedents of interracial efficacy are unclear and rarely studied from the perspectives of Black individuals. The current work addresses these empirical gaps by testing whether individual differences in suspicion of Whites' motives negatively relate to expected efficacy in interracial interactions. Suspicion was operationalized as beliefs that Whites' positivity toward people of color (POC) is primarily motivated by concerns with appearing prejudiced. Four studies employing correlational and experimental vignette designs with Black adult participants (Ntotal = 2,295; ∼60% female) tested suspicion's hypothesized negative relation with three conceptions of interracial efficacy (general efficacy, liking-based efficacy, and respect-based efficacy). Four studies provided consistent evidence that suspicion of Whites' motives negatively related to expected efficacy in interactions with White social partners. Moreover, this relationship was unique to contexts with White partners and did not extend to imagined interactions with Black partners or members of other outgroup members (e.g., Latine partners). Results further suggest suspicion increases expected threat (i.e., anticipated uncertainty/anxiety), which undermines Black individuals' confidence in interactions with White partners. (PsycInfo Database Record (c) 2024 APA, all rights reserved).