Browse Articles

Comparative analysis of fractional thermoelastic vibrations of a nonlocal nanobeam exposed to travelling and static thermal loads

Abstract Present innovative investigation comprises a new approach to analyse the fractional order vibration behaviour of thermo-mechanical waves in a non-localized Nano scale beam affected from a travelling thermal load with constant velocity and ramp type thermal load dependent on time within the framework of nonlocal theory of elasticity. Recently developed Moore-Gibson Thompson (MGT) heat transport model coupled with fractional order thermo-elasticity is utilized to evaluate the analytical results of significant physical fields—temperature, lateral deflection, displacement, cross-sectional elastic moment and thermal stress. Inclusion of the theories of nonlocal elasticity and factional order thermoelasticity in thermal conduction model enables it to capture the size-dependent effects and memory effects in heat conduction at the Nano scale. Laplace transform algorithm is facilitated to determine the closed-form solutions. Depth analysis of graphical results characterizes and analyses the impacts of the important quantities such as fractional order parameter, velocity of the dynamic load, time relaxation quantity and non-local parameter on the field variables. Quantitative results reveal that fractional order quantity and nonlocal effect prominently affect the amplitude, frequency and stability of thermo-elastic vibrations. Significance of MGT heat conduction model is observed by comparing the computational outcomes to the results obtained under previous established heat transfer models – GreenNaghdi- II (GN-II), GreenNaghdi - III (GN-III), Lord and Shulman (LS model) and classical theory (CL). Results determined under MGT model express more finite and stable characteristics of thermo-elastic waves inside the beam compared to the other theories of heat transfer. This study emphasises the significance of applied research in revealing the important properties of Nano scale structures that are observed to be especially advantageous in industry and mechanical engineering.

A new method of off-site inverse carbon accounting and its application in agriculture carbon measurement

This research introduces an innovative agricultural carbon accounting approach for straw burning that combines stochastic process modeling with LSTM neural networks. Traditional methods face limitations including high uncertainty, fragmented data, and prohibitive real-time monitoring costs. Our off-site inverse carbon accounting methodology employs three-dimensional Brownian motion to simulate carbon molecular diffusion patterns, incorporating horizontally drifted motion influenced by wind speed and vertically truncated motion dominated by thermal activity. The framework utilizes LSTM-based time-series predictions to generate virtual diffusion path samples for dynamic model calibration. By quantifying the probability density function of carbon molecular diffusion, we inversely derive carbon emission rates from particle arrival probabilities at observation points. Validation through a straw-burning case demonstrates an average carbon emission rate of 0.0049 tons/second with error margins below 10%, confirming the method’s accuracy. This approach overcomes limitations of traditional emission factor methods while providing cost-effective real-time carbon monitoring for agricultural contexts. Future research could integrate multi-physics models, remote sensing data, and advanced computational techniques like quantum computing to enhance scalability and precision. This work establishes a foundation for data-driven carbon governance in agricultural supply chains, supporting global carbon neutrality efforts.

Thermoregulatory adaptations to cold in C3H/HeJ mice are independent of ADRB3 signaling

Abstract Housing conditions and mouse strain significantly influence metabolic phenotypes, affecting the translational relevance of preclinical studies. Although C57BL/6J (C57) mice are widely used, their thermogenic and adrenergic profiles may not fully reflect human physiology. This study compared thermogenic responses to cold exposure between male C57 and C3H/HeJ (C3H) mice. Animals were housed in a Promethion indirect calorimetry system and exposed to varying ambient temperatures. Thermoneutral points during the light phase were nearly identical (C57: 29.26 ± 0.28 °C; C3H: 29.46 ± 0.17 °C), yet C3H mice exhibited significantly higher energy expenditure (EE) during both acute and chronic cold exposure. Gene expression analysis revealed a stronger induction of thermogenic genes in brown adipose tissue (BAT) of C3H mice. Notably, β3-adrenergic receptor ( Adrb3 ) expression was minimal in BAT and white adipose tissue (WAT) of C3H mice and unaffected by cold exposure. Consistent with impaired β3 signaling, the β3 agonist CL 316,243 markedly increased EE in C57 mice but had only modest effects in C3H mice. In contrast, norepinephrine elicited EE responses in both strains, and propranolol pretreatment (a β1/β2 antagonist) abolished these strain differences, suggesting that C3H mice depend on β1/β2 or non-canonical pathways. In conclusion, C3H mice exhibit enhanced cold-induced thermogenesis through ADRB3-independent mechanisms. Despite similar thermoneutral point, C3H and C57 mice display distinct metabolic and adrenergic adaptations, underscoring the importance of strain selection in metabolic research. C3H mice may represent a model to study alternative thermogenic mechanisms applicable to human physiology.

MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure

Proteins are fundamental to the execution of biological activities, and the accurate prediction of their functions is of paramount importance for protein research. Recent advancements in deep learning, particularly those based on Graph Neural Networks (GNNs), have demonstrated promising results by integrating protein graph features with sequence information. However, traditional GNN methods exhibit limitations in their feature representation capabilities, failing to capture long-range dependencies within sequences and lacking incorporation of inter-annotation relationships. To address these challenges, we propose a method, MAGIN-GO, which combines Graph Isomorphism Network (GIN) and Graph Convolutional Network (GCN) with Graph Convolutional Self-Attention Network (GMSA) to extract multi-source protein information and integrates Gene Ontology (GO) annotation embeddings. Our method effectively combines protein sequence features with protein-protein interaction (PPI) graph node features, extracts topological and contextual information through GIN and GMSA, and integrates pre-trained GO term embeddings into a multi-label classification framework. Comprehensive experiments on the UniProtKB/Swiss-Prot dataset demonstrate that MAGIN-GO outperforms existing methods, achieving AUPR values of 0.569, 0.434, and 0.754 for Molecular Function (MF), Biological Process (BP), and Cellular Component (CC) domains, respectively, with corresponding Fmax scores of 0.568, 0.458, and 0.752, Smin scores of 11.297, 37.709, and 8.079, and AUC scores of 0.896, 0.897, and 0.940. The experimental results showed that the performance of MAGIN-GO was good and superior to the existing methods.

Geochemical, radiological, and heat-production characteristics of the ElGara granitoids (Southwestern Desert)

Abstract This study provides an integrated geochemical, petrographic, and radiological assessment of the El Gara El Hamra and El Gara El Soda granitoids in Egypt’s Southwestern Desert. Whole-rock major, trace, and REE geochemistry, combined with tectonic discrimination diagrams, reveals that the granitoids belong to ferroan A-type suites and comprise both peraluminous and peralkaline varieties. These contrasting chemistries reflect heterogenous crustal sources and within-plate magmatic processes associated with late Neoproterozoic post-collisional extension. Elemental ratios (e.g., Nb/Yb, Ga/Al) and HFSE enrichments support an anhydrous, oxidized, high-temperature melt regime consistent with the regional evolution of the Arabian–Nubian Shield. High-resolution gamma spectrometry was used to quantify primordial radionuclides ( 238 U, 232 Th, 40 K). Thorium and potassium show pronounced enrichment in the peralkaline samples, whereas uranium displays moderate variability across the granitoid suites. Calculated radiological parameters—including absorbed dose rate (D γ ), annual effective dose (E_annual), radium equivalent activity (Ra eq ), and hazard indices—exceed global crustal averages but remain within ranges typical of A-type granites worldwide. Radiogenic heat production (RHP) varies significantly between the peraluminous and peralkaline groups, reaching up to 9.99 µW/m 3 , indicating favorable potential for shallow-crust geothermal exploration. Organ-specific dose modeling (ICRP-based) identifies the bone marrow and lungs as the most impacted tissues under hypothetical prolonged exposure scenarios. Although some samples exceed recommended limits for unrestricted building use, actual public exposure would depend on rock utilization and exposure geometry rather than intrinsic radionuclide concentrations alone. Overall, the El Gara granitoids represent a compositionally diverse A-type system with elevated heat-producing elements and moderate radiological significance. These findings highlight the need for site-specific radiological evaluation before large-scale quarrying or use as construction materials, and underscore their potential relevance for geothermal energy assessments.

Correlation between follicular fluid hormonal levels in PCOS women and embryo development in ART cycles

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder characterized by ovulatory dysfunction. Fertility outcomes in PCOS patients are often suboptimal, potentially owing to alterations in the follicular fluid (FF) microenvironment. However, the differences in FF hormone levels between PCOS and non-PCOS patients, as well as their correlation with assisted reproductive technology (ART) outcomes, remain unclear. This prospective study included 18 PCOS patients and 18 infertile women without PCOS (control group) undergoing intracytoplasmic sperm injection at the Division of Reproductive Medicine, Ramathibodi Hospital. The primary objective was to compare ART outcomes between the groups. Furthermore, FF testosterone, dehydroepiandrosterone sulfate, and luteinizing hormone levels were evaluated to assess their correlation with these outcomes. The number of retrieved oocytes was significantly higher in the PCOS group; however, the rates of metaphase II oocyte formation, fertilization, blastocyst formation, and high-quality blastocyst formation were comparable between the groups. Although FF testosterone and FF luteinizing hormone levels were higher in the PCOS group than in the control group, the differences were not statistically significant. Spearman correlation analysis showed that FF testosterone levels were negatively correlated with fertilization rate (r = −0.3496, p = 0.0366). These findings suggest that increased FF testosterone levels may negatively correlation with fertilization rates, which may reflect one of the contributing factors to the suboptimal ART outcomes observed in PCOS patients.

Home gardening and fruit and vegetable intake in rural settlements in Northeast Hungary

Abstract Several studies have found that home gardening can impact fruit and vegetable intake. In Hungary, where fruit and vegetable consumption is among the lowest in the European Union (EU), poor diet is the main behavioral risk factor contributing to mortality. Therefore, this study explored the associations between home gardening and fruit and vegetable intake, as well as other health-related factors, in two rural settlements in Northeast Hungary. Participants for the cross-sectional study were recruited from two small rural towns ( n  = 269). The online survey collected demographic data, dietary habits, physical activity, and health status. We used multivariable logistic regression analysis to examine the associations between home gardening and the odds of meeting fruit and vegetable intake recommendations. Almost two-thirds of the respondents grew fruit and vegetables at home. Most gardeners were women, highly educated, married, and had children under the age of 18. One-quarter of participants (24.9%) met the dietary recommendation for daily fruit and vegetable consumption, and 86.6% of them had a home garden. Participants with a home garden were more than four times more likely to meet the guidelines for fruit and vegetable intake than those without one (AOR = 4.49, 95% CI 1.95–10.18). This study provides evidence of a strong positive association between home gardening and meeting fruit and vegetable intake recommendations in rural Northeast Hungary. By focusing on an under-researched population with low baseline consumption, the findings extend prior research and support the potential role of home gardening as a context-specific public health strategy to improve dietary behaviors.

Associations among health literacy, anxiety symptoms, and health-related quality of life in Korean adults: A cross-sectional study with age-stratified analyses

Background Health literacy (HL) is a key determinant of physical and mental health outcomes; however, the relationships among HL, anxiety symptoms, and health-related quality of life (HRQoL) remain unclear, and whether the effects of HL vary by age is unknown. We aimed to examine the associations among HL, anxiety symptoms, and HRQoL in Korean adults and assessed age-related differences in these associations. Methods In this cross-sectional study, we analyzed data from the 2023 Korea National Health and Nutrition Examination Survey, including 5,017 adults aged ≥ 19 years. HL was assessed using a validated 10-item instrument (score range: 10–40) and categorized as low, middle, or high. Anxiety symptoms and HRQoL were measured using the 7-item Generalized Anxiety Disorder Scale and the 8-item Health-related Quality of Life Instrument, respectively. Multivariable logistic regression models adjusted for potential confounders were used to estimate associations between HL and anxiety symptoms and between HL and good HRQoL. Age-stratified analyses were conducted for participants aged 19–39, 40–64, and ≥ 65 years. Results The low (odds ratio [OR]: 1.93; 95% confidence interval [CI]: 1.52–2.46; p < 0.001) and middle HL (OR: 1.30; 95% CI: 1.04–1.62; p = 0.024) groups had higher odds of anxiety symptoms than the high HL group. Lower HL was associated with a reduced likelihood of good HRQoL (OR: 0.49; 95% CI: 0.36–0.66; p < 0.001), whereas the middle HL group showed a non-significant trend toward poorer HRQoL (OR: 0.77; 95% CI: 0.56–1.06). HL was associated with anxiety symptoms in young and middle-aged adults, and with HRQoL in young and older adults. Conclusion Low HL was significantly associated with increased anxiety symptoms and poor HRQoL, with a significant impact among young adults. These findings highlight the need for age-specific public health strategies to improve HL.

Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization

Abstract This study presents a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike traditional polymer informatics approaches that treat polymers as single units or use one-hot encoding for reaction components, we decompose each PET-RAFT system into its individual parts: monomer, RAFT agent, and photocatalyst. Next, each element was separately encoded using 2D molecular descriptors derived from SMILES. Using a literature-sourced dataset of 152 PET-RAFT systems, we systematically trained (with fivefold cross-validation, CV) and evaluated 10 ML algorithms. CatBoost showed greater stability across CV-folds (SD = ± 0.07) and was identified as the top performer for monomer conversion prediction (R 2  = 0.84; RMSE = 10.04 pps; MAE = 8.16 pps). SHapley Additive exPlanations (SHAP) analysis revealed mechanistically interpretable structure–property-performance relationships, highlighting that monomer topological complexity, electronic polarization, and molecular weight together account for over 60% of the model’s predictive power. External validation confirmed CatBoost’s ability to generalize to unseen (meth)acrylates and (meth)acrylamides (MAE = 8.03), with comparable performance to that of the training set. In practice, the learned descriptor-conversion mapping enables fast in silico screening and component ranking, highlighting actionable descriptor ranges and potentially accelerating design-build-test cycles for high-conversion PET-RAFT.

Regional disparities in subjective wellbeing across Europe: A fuzzy hybrid TOPSIS approach

The study analyses differences in subjective well-being (SWB) across European regions. Through the Fuzzy-Hybrid TOPSIS approach, we analyse SWB at the country and regional levels. Additionally, a quantile regression model is employed to investigate the impact of socio-economic factors on SWB. The International Social Survey Programme (ISSP) dataset from 2017 is used for seven countries: Denmark, Germany, Spain, France, Finland, Hungary and Slovenia. The synthetic indicator is derived from four indicators: happiness, life satisfaction, goal achievement, and family pressure. At the country level, Germany achieves the highest SWB score (0.69), while Hungary records the lowest (0.51). Regional analysis shows German regions (particularly Saarland and Schleswig-Holstein) and Spanish regions (notably La Rioja and Baleares) occupy top positions in the SWB rankings. Quantile regression results confirm that age, education level, and income significantly influence SWB, with older individuals, those with intermediate education levels, and higher-income earners showing consistently higher SWB values.

Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation

Sex and rank in public service hierarchies: Rank distribution in Ghana’s health and security services

This study analyses leadership patterns in Ghana’s health and security institutions since 1992, with a particular emphasis on the sex composition of senior positions in the Ghana Health Service, the Armed Forces, and the Police Service. A mixed-methods approach was employed, comprising a qualitative literature review, quantitative analysis of the Ghana Police Service rank hierarchies, assessment of the Military Occupational Physical Assessment Test in relation to Military Occupational Specialties, and content analysis of relevant sections of the Affirmative Action Act (2024). In 2025, women represented 39% of doctors (5,068/12,900), 30% of police officers (12,945/43,968), and 15% of soldiers (2,400/16,000) in Ghana. Leadership in the Ghana Armed Forces remained male-dominated, with fewer than five female Generals among 115 in the forces and only one female Inspector-General of Police since 1992. Statistical analysis of police rank distribution showed a significant association between sex and rank (χ², p < 0.05), indicating persistent disparities in career progression. Findings highlight systemic institutional barriers affecting women’s advancement in Ghana’s health and security sectors. Targeted institutional reforms aligned with the requirements and merit-based principles of the Affirmative Action Act (2024) are necessary to address the disparities and strengthen equitable representation.

Seasonal groundwater quality assessment and irrigation suitability in coastal aquifers of Puri District, Odisha, India

Abstract Assessing seasonal groundwater quality in coastal aquifers is critical for ensuring sustainable drinking water supply and irrigation management under increasing anthropogenic and climate-induced pressures. This study evaluates the spatio-temporal variation of groundwater quality across four hydrological seasons winter (December-February), pre-monsoon (March-May), monsoon (June–September), and post-monsoon (October-November) in the coastal aquifers of Puri district, Odisha, India, during 2021–2022. Groundwater samples collected from twelve representative monitoring locations were analyzed for major physicochemical parameters, including pH, EC, TDS, total hardness, major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), and major anions (Cl⁻, SO₄²⁻, HCO₃⁻, NO₃⁻, and F⁻). Drinking water suitability was assessed using BIS (2012) and updated WHO (2022) guidelines, while irrigation suitability was evaluated using Sodium Adsorption Ratio (SAR), Kelly’s Ratio, EC, TDS, and USSL classification. The Water Quality Index (WQI) was employed to integrate multiple parameters into a single indicator of potability. Results reveal that groundwater quality ranges from good to excellent across most seasons, with localized deterioration during pre-monsoon and monsoon periods due to salinity enrichment and anthropogenic inputs. Irrigation indices indicate predominantly low to moderate sodium and salinity hazards, confirming suitability for agricultural use. The study provides a comprehensive seasonal and multi-index assessment of groundwater quality in a sensitive coastal aquifer system and contributes to SDG-6 (Clean Water and Sanitation) and SDG-13 (Climate Action) by supporting evidence-based groundwater management and climate-resilient water planning.

Stakeholder perspectives on depression management: A design thinking exploration for person-centered digital health

Introduction eHealth has the potential for managing depression and enhancing quality of life. Identifying end user needs and employing participatory methodologies that actively engage all stakeholders can improve user experience, usability and effectiveness. Materials and Methods Six face-to-face empathy workshops were conducted in three Spanish autonomous communities (Catalonia, Andalusia and Canary Islands) using design thinking methodologies, involving individuals with depression and mental health professionals. Data were analyzed using an iterative and inductive analysis approach. Objective To explore the perspectives of people diagnosed with depression and healthcare professionals involved in its management, using a design thinking methodology. Results Thirteen individuals with depression (10 women, average age 49.15, SD: 18.10) and 17 mental health professionals (11 women, average age 40.21, SD: 12.15) participated in empathy workshops. Three key themes emerged: the daily experience of depression, the potential of technology in managing depression, and emerging challenges to address. Discussion The intensity and daily experience of depressive episodes were influenced by various factors. Technologies, when used as a complement to face-to-face care, showed potential for managing depression. However, there were associated risks and challenges that need to be addressed. Conclusion It is essential to identify and understand the needs of end users and incorporate the perspectives of all stakeholders in the design and development of digital health interventions.

Job satisfaction as a catalyst mechanism transforming knowledge competence and intrinsic motivation into sustained lecturer performance in higher education

IGM: Integrated Gene-expression Modeling for multi-condition flux-preserving genome-scale metabolic models

Genome-scale metabolic models (GEMs) are powerful tools for studying cellular metabolism, but conventional approaches such as Flux Balance Analysis (FBA) often yield ambiguous results due to the lack of consistent integration of condition-specific data across multiple experimental contexts. Existing methods for incorporating gene expression data into GEMs are typically limited to single-condition analyses, rely on arbitrary thresholds, or compromise the interpretability of flux predictions. Here, we present IGM: Integrated Gene-expression Modeling, a novel mixed-integer linear programming (MILP) framework that integrates gene expression data across multiple conditions into GEMs without binarization, thereby preserving flux units and enhancing biological relevance. IGM employs flux variability analysis (FVA) to define feasible flux ranges, integrates relative gene expression through gene–protein–reaction (GPR) rules, and minimizes the difference between fluxes and corresponding gene expression mappings. Evaluation on Escherichia coli ( E. coli ) metabolic models demonstrates that IGM significantly improves correlation with experimentally measured fluxes, reduces flux solution ambiguity, and provides higher predictive consistency across multiple conditions. Among its variants, IGM with L1 norm regularization achieves the highest accuracy. We also evaluated gene expression integration by comparing relative gene expression values with model-derived gene expression variables. Visual inspection and genome-scale correlation analysis revealed strong concordance across all genes, confirming that IGM effectively preserves transcriptomic patterns while filtering out genes irrelevant to flux-carrying reactions, thereby enhancing biological interpretability. Furthermore, we applied IGM to flux change analysis, where subsystem-level fluxes revealed distinct metabolic states. This analysis highlights IGM’s ability to integrate transcriptomic data into metabolic modeling in a condition-specific yet consistent manner, enabling biologically grounded predictions of metabolic adaptation. By capturing dynamic metabolic changes and improving predictive accuracy, IGM provides a robust framework for consistent, comparative, and multi-condition metabolic studies.

Comprehensive phytochemical, anatomical and biological evaluation of Ziziphora clinopodioides Lam. (Lamiaceae), used as a traditional tea from Türkiye

Abstract This study provides a comprehensive investigation of Ziziphora clinopodioides Lam. (Lamiaceae) from Türkiye, focusing on its phytochemical composition, biological activities, and anatomical characteristics relevant to its potential as a functional food ingredient. Methanol and aqueous extractions yielded 28.09% and 30.91%, respectively, calculated based on the dry weight of the plant material. Essential oil analysis identified 98.3% of the total composition, with oxygenated monoterpenes, particularly 1,8-cineole (28.2%), terpinen-4-ol (12.0%), and pulegone (7.9%), as dominant constituents. Enzyme inhibition assays revealed α-amylase inhibition of 29.3% by the essential oil, calculated based on the tested sample concentration, and moderate cholinesterase inhibition by the methanolic extract; however, all activities were lower than those of the reference drugs. Morphological analysis confirmed the plant as a mat-forming perennial with dense hirsuteness and capitate inflorescences. Leaf anatomy showed amphistomatic, bifacial structure with diverse trichome types and a well-organized vascular system. The stem anatomy exhibited a quadrangular structure with varied glandular and non-glandular trichomes, developed collenchyma, and a distinct endodermis. The findings of this research could shed light on the medicinal value of Z. clinopodioides , and open up new possibilities for its use in various food products or therapeutic applications.

Voices from the emergency department: A theoretical framework analysis on patient experiences of care in emergency departments of Newfoundland and Labrador, Canada

Background Patient experience in the emergency department (ED) encompasses different aspects of care, such as respect, communication, timeliness, shared decision-making, and care transition. The inherently stressful ED environment presents additional challenges for providers in ensuring a positive patient experience. Studying patient experience allows health systems to recognize areas of care that need improvement and introduce strategies to improve patient-centred care. Objectives To explore the patient experience of emergency department care in Newfoundland and Labrador (NL), Canada. Methods This qualitative study collected data from patients who visited two urban and two rural EDs in NL via telephone surveys and semi-structured interviews. Five researchers used a theoretical framework, symbolic interactionism, to analyze open-ended survey responses and semi-structured interviews. Patient research partners were consulted to ensure the themes reflected their lived experiences. Results A total of 836 responses were analyzed (831 survey responses, five semi-structured interviews), leading to six key themes on patient experience. They are: (1) Mutual respect and trust in providers, (2) Timeliness of care, (3) Communication, (4) Comfort and accommodations, (5) Information sharing and decision-making, and (6) Continuity of care. Conclusion Our findings show an immediate need to improve the patient experience of ED care and highlight areas within these themes that could be targeted for improvement. We recommend regular training programs for healthcare providers to improve their interpersonal skills, the addition of patient navigators, and modifications to the triage system for vulnerable populations to improve patient experience and provide patient-centred care.

AntiPan: a genome-informed in silico pipeline for advancing subunit vaccine discovery against Staphylococcus aureus

Recycled polymer shot as sustainable additive for concrete: Mechanical, thermal, and environmental assessment

The exponential growth of plastic production has led to a dramatic increase in plastic waste, creating significant environmental and public health challenges. Among the available disposal methods, recycling is widely regarded as the most environmentally and economically advantageous. The development of effective strategies for recycling plastic waste is therefore a critical area of research, particularly in the construction industry. Incorporating plastic waste into concrete not only minimizes landfill disposal, but also has the potential to improve the performance of the material. This study investigates the effect of recycled polymer shot, incorporated at levels of 5% and 10% by cement mass, on the physical, mechanical, and microstructural properties of concrete. Comprehensive tests were performed, including measurements of density, thermal conductivity, slump, slip resistance, compressive, flexural, and tensile strength, as well as elastic modulus. The results demonstrate that the use of polymer shot significantly improves key properties of concrete, with increases of up to 45% in flexural strength and 62% in tensile strength at the higher addition level. Image-based microstructural analysis further revealed modifications in porosity and fracture surface complexity, supporting the observed performance enhancements. Overall, the incorporation of recycled polymer shot in concrete provides a promising approach for valorizing plastic waste, improving concrete performance, and supporting sustainable construction and resource management strategies.