Jeddah University

Introduction The glucose management indicator (GMI) and time-in-range (TIR) are important glycemic parameters calculated from continuous glucose monitoring (CGM) values. KARAZ, a hybrid Internet of things—artificial intelligence platform, was developed in Saudi Arabia to help manage diabetes mellitus. The complex nature of self-care and behavior changes associated with diabetes mellitus requires breaking large behaviors into achievable ones and related incentives. Aim This study explored how tiny habits as a behavioral intervention and incentive system affect glycemic control among KARAZ Platform users with diabetes mellitus in various age groups in Saudi Arabia. Methods This retrospective study included KARAZ Platform users and analyzed the effects of behavioral interventions and incentives on GMI and TIR as glycemic control parameters. Results Of 296 active users, 118 (40%) and 148 (50%) maintained a desirable TIR and GMI, respectively. Adult females aged ≥ 26 years who consistently followed tiny habits and behavior changes exhibited a significant reduction in the GMI (5%). Intrinsic motivation through behavioral modification was more effective than external incentives for maintaining glucose control. Conclusion The findings highlight how behavioral interventions can impact GMI, suggesting their effectiveness in promoting better health behaviors and improving glycemic control in the Saudi Arabian context. Further research should investigate how these habits and behaviors can be maintained sustainably without relying on external incentives. Recommendations discussed how children with Type 1 diabetes mellitus would benefit from CGM connection to KARAZ Platform iteration and the integration of a comprehensive diabetes care program within the Saudi health system.

Background Influenza imposes a substantial global health burden, particularly among high-risk populations such as the elderly, young children, and individuals with chronic conditions. In Saudi Arabia, a national influenza sentinel surveillance program was established in 2017 to monitor respiratory virus trends, yet comprehensive estimates of the influenza-associated burden remain limited. This study aims to address this gap by quantifying influenza-associated severe acute respiratory infection (SARI) hospitalization rates and estimating the broader influenza burden across severity levels. Methods Data from four sentinel hospitals in three regions of Saudi Arabia were analyzed across three influenza seasons: 2017–2018, 2018–2019, and 2022–2023. Weekly SARI case counts were combined with census population data to calculate SARI hospitalization rates per 100,000 population. Influenza positivity rates, derived from laboratory-confirmed cases, were used to estimate influenza-associated SARI hospitalization rates, stratified by age and season. The John Hopkins University/WHO Seasonal Influenza Burden Disease Estimator (Flutool) was employed to extrapolate national estimates of influenza-associated hospitalizations, deaths, and mild/moderate cases. Confidence intervals and age-specific stratifications were computed to enhance precision and comparability. Results The average annual SARI hospitalization rate was 294 per 100,000 population (95% CI: 288–300). Influenza-associated SARI hospitalization rates averaged 48 per 100,000 population (95% CI: 45–50), with the highest burden observed in individuals aged 65 years and older (269 per 100,000, 95% CI: 240–301) and children aged 0–4 years (118 per 100,000, 95% CI: 107–131). Seasonal variation was noted, with the highest rates in the 2017–2018 season. National estimates suggested a substantial burden, with influenza-associated hospitalizations totaling 17,678 in 2017–2018, 7,683 in 2018–2019, and 13,982 in 2022–2023. The flutool analysis estimated annual influenza-associated deaths ranging from 30 to 4,441 and mild/moderate cases reaching up to 6.3 million in the most severe season. Conclusions This study demonstrates a significant burden of influenza-associated SARI hospitalizations in Saudi Arabia, with the highest rates observed in the elderly and young children. Seasonal variation was evident, highlighting the urgent need to enhance influenza vaccination coverage, particularly among high-risk groups such as the elderly and young children, to reduce severe outcomes. Expanding sentinel surveillance to more regions and incorporating detailed clinical and economic data are recommended to better inform public health policies. Strengthening pandemic preparedness and tailoring vaccination campaigns based on seasonality and age-specific risk will be critical for mitigating the influenza burden in Saudi Arabia.

Background Sinusitis is an inflammation of the paranasal sinuses and is commonly treated with antibiotics. The widely used antibiotics for this condition are macrolides, especially azithromycin. However, its effectiveness and side effects are still questionable compared to the other antibiotics. Therefore, this systematic review and meta-analysis assessed the efficacy and safety profile of azithromycin in sinusitis. Methods We adhered to PRISMA guidelines. A comprehensive literature review was performed to find out about randomized controlled trials concerning azithromycin compared with other antibiotics in sinusitis treatment. The main outcomes were the cure rate, pathogen eradication rate, and relapse rate. The secondary outcome was the adverse events rate. Results Fourteen trials were considered for review, with a sample size of 4201 patients. The pooled analysis for included studies indicated a high cure rate (70.86%) and pathogen eradication rate (74.55%), as well as a low relapse rate (4.82%) and adverse events rate (14.33%) for azithromycin in treating sinusitis patients. The quality of the included studies was considered to be moderate. In a meta-analysis, azithromycin demonstrated superiority in the cure rate to other antibiotics in the study but no difference in pathogen eradication rate, relapse rate, or adverse events rate. Conclusion Our results showed promising efficacy and safety of azithromycin in the management of sinusitis patients. However, moderate heterogeneity among studies and a 14.33% rate of adverse effects, primarily gastrointestinal, indicate the importance of individualized treatment decisions. Further research is needed to address variability and optimize its clinical application.

The urge need for innovative integration between Electromagnetic Waves (EMWs) and nanotechnology offers exciting possibilities for improving antimicrobial treatments to combat antibacterial resistant bacterial infections. This study explores how EMWs at range below 300 Hz can enhance the antibacterial efficacy of Graphene Oxide Nanoparticles (GONPs) against Pseudomonas aeruginosa, a significant pathogen in antibiotic resistance. EMWs at range below 300 Hz, interact with bacterial cell membranes to affect ion channels, permeability, and cellular signaling, offering a non-invasive method to amplify antimicrobial effects. GONPs synthesized through glucose pyrolysis and characterized by X-ray diffraction, UV-visible spectroscopy, high-resolution transmission electron microscopy, and Fourier-transform infrared spectroscopy, exhibit potent antibacterial properties due to their sharp edges, large surface area, and ability to generate Reactive Oxygen Species (ROS). These nanoparticles disrupt bacterial membranes, form biofilms, and damage cellular components through oxidative stress. The study examines how those EMWs can enhance GONP penetration into bacterial cells, increase ROS production, and disrupt biofilms. By optimizing EMWs parameters such as frequency, intensity, and duration this research aims to develop new, non-invasive antibacterial therapies. The results could lead to advanced antimicrobial strategies, integrating nanotechnology with electromagnetic field exposure, offering innovative solutions to address antibiotic-resistant infections and improve treatment efficacy. This approach represents a significant step toward more effective, targeted antibacterial therapies.

Currently, chemical attacks, including acid attacks and sulphate attacks, pose a significant problem for the long-term durability of concrete infrastructures that encounter many types of water, including swamp water, marine water, sewage water, drinkable water, and groundwater. Therefore, the intention of this work is to enhance the durability and resistance of concrete against chemical attack by blending titanium dioxide (TiO2) as nanoparticles into designed cementitious composites. The purpose of current study is to obtain an appropriate TiO2 based on the cement’s weight and polyvinyl alcohol (PVA) fiber in composites using multi-objective optimisation. Thirteen mixtures comprising diverse combinations of variables (TiO2: 1–2%, PVA: 1–2%) were formulated utilising RSM modelling. Seven responses were assessed for these mixtures, including weight loss, compressive strength, expansion, a rapid chloride permeability test (RCPT) and a pH test. Analysis of variance, on the other hand, was utilised to construct and assess eight response models (one linear and six quadratics in nature). The R² values for models spanning from 88 to 99%. The multi-objective optimisation generated optimal response values and ideal variable values (1% PVA and 1.5% TiO2). Experimental verification revealed that the predicted values correlated exceedingly well with the experimental data, with an error rate of less than 5%. The outcomes indicate that a 30% rise in compressive strength was noted when 1.5% TiO2 nanomaterial was incorporated into ECC. Furthermore, the expansion caused by sulphate attack decreases when TiO2 used as a nanomaterial increases in composites. Besides, when the concentration of TiO2 in ECC increased, the pH value, and weight loss caused by acid attack reduced. In addition, the RCPT is recorded reducing when the content of TiO2 increases but it increases with addition of PVA fiber. It has been shown that including 1.5% TiO2 and 1% PVA fiber yields the optimal results for the building sector.

As a result of globalization and the belief that proficiency in English is essential for progress, English as the medium of instruction (EMI) has become prevalent in the Gulf states. While there is little debate over the advantages, recent research indicates that allowing English to dominate tertiary education can have adverse outcomes. The United Arab Emirates (UAE), which adopted EMI earlier than the Kingdom of Saudi Arabia (KSA), has experienced unfavorable consequences. A comparative analysis of the KSA and the UAE indicates that longer exposure to EMI is linked to heightened concerns about domain loss in the Arabic language, in Arab identity, and in learning outcomes. This study presents and analyzes data to assess the extent to which EMI influences students’ ability to acquire subject knowledge. Employing a rhizomatic and complex theory framework for analysis, the findings indicate that stakeholders should critically examine their perceptions and assumptions regarding EMI.

The global COVID-19 outbreak made our society aware of the significant role that mask-wearing plays in the prevention of viral transmission. Almost everywhere, world health authorities have been recommending the use of face masks in public spaces, with some even making it mandatory. Therefore, a significant rush is underway to develop automated face mask detection systems for surveillance purposes in areas such as transportation systems, shopping malls, and educational institutions, with the aim of monitoring the implementation of face mask policies. This work introduces a unique approach to enhance face mask detection by combining an Active Learning (AL) system with a Convolutional Neural Network (CNN), and fine-tuning the CNN's hyperparameters using a Genetic Algorithm (GA). We use the AL framework to query the most informative data samples, which not only minimizes the labelling cost but also achieves high model accuracy. To improve CNN's performance, hyperparameter optimization uses a genetic algorithm to optimally select the network parameters. The study leverages transfer learning and pruning on the CNN model to improve results. Pruning simplifies the network for faster inference, while transfer learning increases accuracy by leveraging the weights and biases of previously trained models. Benchmark datasets assess the proposed method, demonstrating its superior performance in face mask detection with higher accuracy and robustness compared to previous methods. According to the experiment, there are different levels of accuracy in training different active learning sampling strategies that use the transfer of learnt CNN pruned. The entropy sampling method outperforms all other methods, achieving an accuracy of 98%. We compared the transfer-learned pruned CNN model with the Corona mask two-stage CNN model and the fine-tuned Yolov6 model for real-time face mask recognition.

This study reports the development of two green ionic liquids (ILs), namely, choline tyrosinate and choline prolinate, abbreviated as ChoTyr and ChoPro, respectively, as eco-friendly corrosion inhibitors for mild steel (MS) in acidic media. The obtained ILs were characterized by ¹H-NMR and FT-IR spectroscopy. The anti-corrosive performance of the synthesized ILs was investigated by the weight loss and electrochemical methods (PDP, EIS) in 5% HCl solution at different immersion times of 24, 48, and 72 h at 313 K. The results indicated inhibition efficiencies of 96.9% for ChoTyr, 92.9% for ChoPro in static conditions, 95.5% for ChoTyr, and 91.5% for ChoPro in dynamic conditions after 72 h immersion. The inhibition performance increases as immersion time increases due to the enhanced adsorption of ILs molecules onto the steel surface, forming a protective film. SEM analyses exhibited a smooth surface without corrosion when ILs were present, while untreated steel showed extensive degradation. FT-IR and UV-vis spectroscopic analyses confirmed ILs adsorption. At the same time, theoretical calculations employed using density functional theory corroborated the experimental observations, suggesting stronger adsorption and a higher inhibitory potential of ChoTyr versus ChoPro. These findings proved ChoTyr and ChoPro ILs to be sustainable corrosion inhibitors, providing valuable implications for industry applications in acid pickling and cleaning areas.

Superbugs, as bacterial resistance species, resist known antibiotics. In this study, twenty novel 1,2,4‐triazole‐functionalized piperazine‐stearic acid derivatives 6a‐p were designed, synthesized, and characterized via spectroscopic analyses, evaluating their antimicrobial potential against clinically relevant bacterial and fungal strains. Notably, five derivatives (6a, 6c, 6m, 6n, and 6o) demonstrated superior antimicrobial efficacy compared to gentamicin standard, with compound 6n showing the most potent activity (MIC = 3.125 μg/mL against S. aureus). Molecular docking simulations further revealed strong binding affinities of these compounds to microbial target proteins (ΔG = ‐8.2 to ‐9.7 kcal/mol). These findings present promising antimicrobial candidates of 1,2,4‐triazole‐appended piperazine‐stearic moiety derivatives.

BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, including its structure, utilization in different NLP tasks, and the further development of its design via modifications. The study thoroughly analyses the methodological aspects, conducting a comprehensive analysis of the planning process, the implemented procedures, and the criteria used to decide which data to include or exclude in the evaluation framework. In addition, the study thoroughly examines the influence of BERT on several NLP tasks, such as Sentence Boundary Detection, Tokenization, Grammatical Error Detection and Correction, Dependency Parsing, Named Entity Recognition, Part of Speech Tagging, Question Answering Systems, Machine Translation, Sentiment analysis, fake review detection and Cross-lingual transfer learning. The review study adds to the current literature by integrating ideas from multiple sources, explicitly emphasizing the problems and prospects in BERT-based models. The objective is to comprehensively comprehend BERT and its implementations, targeting both experienced researchers and novices in the domain of NLP. Consequently, the present study is expected to inspire more research endeavors, promote innovative adaptations of BERT, and deepen comprehension of its extensive capabilities in various NLP applications. The results presented in this research are anticipated to influence the advancement of future language models and add to the ongoing discourse on enhancing technology for understanding natural language.

Cigarette smoking remains a significant public health concern, particularly among adolescents. This study aimed to assess adolescents' knowledge of the risks associated with smoking in Scotland and explore the factors influencing this knowledge. A cross-sectional analysis was conducted using data from the 2018 Scottish Schools Adolescent Lifestyle and Substance Use Survey. The sample included 23 365 adolescents from Secondary 2 (n ¼ 12 558) and Secondary 4 (n ¼ 10 807). Demographic, behavioural, contextual, and health-related factors were examined as predictors of smoking risk knowledge, with a cumulative knowledge score based on responses to seven smoking-related questions. Poisson regression was used to examine associations between predictors and cumulative knowledge scores, and adjusted incidence rate ratios (IRRs) with 95% confidence intervals (CIs) were reported. Overall, 38.2% (n ¼ 8928) of participants answered all questions correctly, with a median score of 6 (IQR: 5-7). While 83.6% (n ¼ 19 540) scored at least 5, knowledge gaps remained, particularly concerning the risks of light smoking. Boys had lower scores than girls (IRR ¼ 0.97, 95% CI: 0.96-0.97, P < .001), and adolescents from less deprived areas scored higher than those from more deprived areas (IRR ¼ 1.02, 95% CI: 1.01-1.03, P ¼ .006). Non-smokers had higher scores than smokers (IRR ¼ 1.08, 95% CI: 1.05-1.10, P < .001), and adolescents confident in accessing health information scored significantly higher (IRR ¼ 1.13, 95% CI: 1.11-1.15, P < .001). Peer influence was evident, as adolescents with friends who smoked had lower scores (IRR ¼ 0.96, 95% CI: 0.94-0.98, P < .001). Adolescents in Scotland generally understand the risks of cigarette smoking, but notable gaps persist, especially regarding dangers of light smoking.

The aim of this study is to assess the impacts of chitosan (CH) coating with oregano essential oil (OEO) and thyme essential oil (TEO) (0.5%–1.0%; v/w ) on the foodborne pathogens and physicochemical parameters of beef burger during refrigerated storage. Preliminary experiment ( in vitro ) demonstrated that 0.5% OEO and TEO were inhibited all or some of S. aureus , S. Typhimurium, and E. coli O157:H7 . On day 30, the E. coli O157:H7 of burger coated with CH + OEO and TEO (1%; w/v ) declined by 4 and 5 log 10 CFU g ⁻¹ , respectively, S. Typhimurium and S. aureus decreases (4,5-6 log 10 CFU g ⁻¹ ) when compared to the control sample. The quality parameters of beef burger were also enhanced after the coating treatment of CH and essential oils (EOs), including pH value, TBARS, and TVB-N in burger during storage (4 °C/30 d). Besides, CH + EOs coating also reduced the deterioration of the sensory attributes of beef burger, including color, odor, and overall acceptability. The chitosan coatings with EOs have superior mechanical qualities than the control sample, also, the structure of the films was evaluated by SEM. In conclusion, CH coating with EOs (OEO, ETO; 1%) regarded as a successful strategy to improve the quality and prolong the shelf life of beef burger.

Twelve thiazole‐pyrazole analogues 4 , 6 , and 8 were synthesized by introducing various pyrazole systems into the core, 2‐((4‐acetylphenyl)amino)‐4‐methylthiazole ( 2 ), through many synthetic approaches. The density functional theory (DFT) study of the synthesized analogues revealed coincided configurations of their highest occupied and lowest unoccupied molecular orbitals (HOMO and LUMO), except for the nitro derivatives, in which the intramolecular charge‐transfer (CT) may be denoted as π → π* and n → π*. In addition, the in vitro antiproliferative efficacy towards some cancer cell lines was examined (Panc‐1, HT‐29, MCF‐7) and the non‐cancerous (WI‐38), using Dasatinib (Reference). The analogues 4c and 4d demonstrated the most potent anticancer effect, particularly against Panc‐1 and MCF‐7 cells. Moreover, the antiviral activity against H5N1, using a plaque reduction assay, showed that analogue 6a exhibited the most potent antiviral activity (100% inhibition and TC 50 = 61 μg/μL), comparable to the reference drug amantadine (TC 50 = 72 μg/μL, 100% inhibition). Furthermore, the molecular docking disclosed that the analogues exhibited a range of interactions, such as H‐bonding and π‐π stacking, with binding affinities between −4.8558 and − 8.3673 kcal/mol. Additionally, the SwissADME predictions indicated that the synthesized analogues possess promising drug‐like characteristics, but analogues 4a–d and 8c demonstrated inadequate solubility and bioavailability, which restricts their use as viable oral medications.

Zingiberaceae has long been used medicinally, culinarily, and cosmetically, especially in tropical and subtropical regions. However, despite attracting substantial funding and research interest, they remain unexplored from a bibliometric perspective. Using the Scopus database, this study summarizes the global research output on Zingiberaceae from 1943 to 2022. The Scopus search resulted in 3589 Englishlanguage journal articles and conference proceedings. The bibliometric networks were visualized with the VOSviewer software. The analysis revealed that the most published author is J.K. Hwang affiliated with Yonsei University (South Korea), while the Chinese Academy of Sciences (China) holds the largest number of publications among the institutions. The works on Zingiberaceae cover multiple topics from 27 disciplines, with agricultural and biological sciences contributing the most (23.5 %). Other key research areas and subjects include ethnobotany, traditional knowledge, botanical and taxonomic studies, essential oils, pure chemicals, and individual species. India emerged as the most collaborative country, and S. Nayak from Siksha O Anusandhan University (India) stands out as the most collaborative researcher. The Journal of Ethnopharmacology leads in the publication and citation counts. The above results define the current status and future hotspots of the research on Zingiberaceae plants.

Background Medical students receive foundational knowledge in clinical pharmacology, bridging the gap between pharmacology and clinical practice. While several studies have investigated clinical pharmacology teaching methodologies, few describe the teaching and learning of clinical pharmacology in Saudi Arabia. This study aimed to explore medical students’ preferences for teaching and learning methods in clinical pharmacology, identify current strengths and challenges, and provide suggestions for improvement. Methods In April 2024, a cross-sectional online survey was distributed via WhatsApp to second and third-year medical students at the University of Jeddah, KSA. The survey invitation explained the purpose, voluntary participation, and anonymity of responses, with informed consent obtained. A 24-item English questionnaire, including a Likert scale and open-ended questions, was developed and pilot-tested by five medical students. Data were analyzed using Minitab 17, employing descriptive statistics and Chi-square (χ2) tests to explore variable relationships. Results Ninety out of 395 medical students completed the questionnaire, resulting in a 22.8% response rate. Students (43.3%, n = 39) found the curriculum comprehensive and relevant for medication management but lacked cohesiveness. Significant challenges included understanding course content (56.7%, n = 51) and passing exams (43.3%, n = 39). Additionally, 56.7% (n = 51) felt overwhelmed by the volume of information and struggled to apply pharmacological knowledge in practice. In terms of interactive teaching methods, such as case-based discussions and simulations, were found inadequate, and hands-on experience opportunities needed to be improved. While 43.3% (n = 39) expressed satisfaction with their clinical pharmacology education, 36.7% (n = 33) remained neutral, indicating uncertainty about the quality and effectiveness of the teaching they have received. Issues raised by students included feeling overwhelmed by the significant content in the curriculum (n = 62, 69.7%), and understanding the content (n = 51, 56.7%), demanding more case-based learning exercises with real-world patient scenarios (n = 47, 53.4%). For learning modalities, online modules and multimedia resources for self-paced learning were ranked highest (n = 37, 41%), followed by small-group discussions and case-based learning activities (n = 42, 46%). Conclusions Findings suggest the need for more cohesive clinical pharmacology curricula, enhanced interactive teaching methods, and increased use of technology and practical applications to improve learning outcomes.

This investigation aimed to examine the synergistic effects of fresh royal jelly and local wild Artemisia monosperma leaf extract as antibacterial, antioxidant, antibiofilm, and anti-Alzheimer activity. Gas chromatography-mass spectrometry (GC–MS) identified 16 compounds in Artemisia monosperma, including tricosadiynoic acid, hexadecenoic acid, octadecenoic acid. In contrast, fresh royal jelly contained 13 compounds, including dodecanoic acid, octadecynoic acid, hexadecenoic acid, heptatriacotanol, and their derivatives. The Artemisia monosperma extract exhibited significant antioxidant activity in the DPPH assay, with IC50 value of 5.48 ± 0.002 µg/mL. Fresh royal jelly exhibited an IC50 value of 14.56 ± 0.004 µg/mL. Both substances exhibited significant antibacterial activity in comparison to gentamycin. The Synergistic combination (1:1) effectively suppressed the growth of multidrug-resistant bacterial species, including Bacillus subtilis (ATCC 6633), Enterococcus faecalis (ATCC 10541), Staphylococcus aureus (ATCC 6538), Klebsiella pneumoniae (ATCC 13883), Salmonella typhi (ATCC 6539), and Pseudomonas aeruginosa (ATCC 90274), and decreased biofilm activity. Additionally, in vitro the of inhibition activity (IC50) of the Butyrylcholinesterase enzyme (BChE) for the plant extract, royal jelly and the Synergistic combination were 4.35 ± 0.002 µg/mL, 4.9 ± 0.002 µg/mL, and 3.55 ± 0.002 µg/mL, respectively while the IC50 of rivastigmine (positive control) was 3.9 ± 0.002 µg/mL. in silico analysis reported that the bioactive compounds demonstrated high binding affinities, between − 6.6 and − 10.3 kcal/mol, against the human acetylcholinesterase protein, beside ADMET analysis exhibited high gastrointestinal absorption and potential inhibitory effects on CYP1A2 and CYP2C9 enzymes. Our study indicated that the synergistic effect of Artemisia monosperma and royal jelly bioactive compounds exhibited a promising antibacterial, antioxidant, antibiofilm, and acetylcholinesterase inhibitory activities.

The primary objective of the paper is exploring the Ulam stability $(\mathcal{US})$ of a Caputo q-fractional Langevin differential equation $(\mathcal{FLDE})$ under q-fractional integral boundary conditions $(\mathcal{FIBC}s)$. The novelty of this work stands out for its broader generality compared to the existing research focused on the Caputo q-fractional derivative. We apply the Banach contraction principle $(\mathcal{BCP})$ for checking the existence and uniqueness of solutions of Caputo $q-\mathcal {FLD}$ equations. The framework of the study integrates fundamental principles from both fractional calculus and quantum calculus. Additionally, we discuss various forms of Ulam stability, namely $\mathcal{UHS}$, $\mathcal{GUHS}$, $\mathcal{UHRS}$, and $\mathcal{GUHRS}$. We validate our theoretical findings through illustrative examples.

Baited traps are routinely used in many ecological and agricultural applications, in particular when information about pest insects is required. However, interpretation of trap counts is challenging, as consistent methods or algorithms relating trap counts to the population abundance in the area around the trap are largely missing. Thus, interpretation of trap counts is usually relative rather than absolute, i.e., a larger average trap count is regarded as an indication of a larger population. In this paper, we challenge this assumption. We show that the key missing point is the animal movement behaviour, which is known to be modified in the presence of attractant (bait), in particular being dependent on the attractant strength. Using an individual-based simulation model of animal movement, we show that an increase in trap counts can happen simply because of changes in the animal movement behaviour even when the population size is constant or even decreasing. Our simulation results are in good qualitative agreement with some available field data. We conclude that, unless reliable biological information about the dependence of animal movement pattern on the type and strength of attractant is available, an increase in trap counts can send a grossly misleading message, resulting in wrong conclusions about the pest population dynamics and hence inadequate conservation or pest management decisions.