Search Results (2,164)

Biotechnological research increasingly focuses on developing new drugs to counter the rise of antibiotic-resistant strains in hospitals. This study aimed to create bacterial lysates from antibiotic-resistant pathogens isolated from patients and medical instruments across hospital departments. Identification was performed based on morphological, cultural, and biochemical characteristics, as well as 16S rRNA gene sequencing using the BLAST algorithm. Strain viability was assessed using the Miles and Misra method, while sensitivity to eight antibacterial drug groups and biosafety between cultures were evaluated using agar diffusion. From 15 clinical sources, 25 pure isolates were obtained, and their phenotypic and genotypic properties were studied. Carbohydrate fermentation testing confirmed that the isolates belonged to the genera Escherichia, Citrobacter, Klebsiella, Acinetobacter, Pseudomonas, Staphylococcus, Haemophilus, and Streptococcus. The cultures exhibited good viability (10⁹–10¹⁰ CFU/mL) and compatibility with each other. Based on prevalence and clinical significance, three predominant hospital pathogens (Klebsiella pneumoniae 12 BL, Pseudomonas aeruginosa 3 BL, and Acinetobacter baumannii 24 BL) were selected to develop a bacterial lysate consortium. Lysates were prepared with physical disruption using a French press homogenizer. The resulting product holds industrial value and may stimulate the immune system to combat respiratory pathogens prevalent in Kazakhstan’s healthcare settings. Full article

The preparation and use of iron oxide magnetic nanoparticles for water remediation is a widely investigated research field. To improve the efficacy of such nanomaterials, different synthetic processes and functionalization methods have been developed in the framework of green chemistry to exploit their magnetic properties and adsorption capacity in a sustainable way. In this work, iron oxide magnetic nanoparticles embedded in cross-linked sodium alginate beads designed to clean water from metal ions were magnetically characterized. In particular, the effect of copper adsorption on their magnetic properties was investigated. The magnetic characterization in a DC field of the beads before adsorption showed the presence of a superparamagnetic state at 300 K—a state that was also preserved after copper adsorption. The main differences in terms of magnetic properties before and after Cu²⁺ adsorption were the reduction of the magnetic signal (observed by comparing the saturation magnetization) and a different shape of the blocking temperature distribution obtained by magnetization versus temperature measurements. The evaluation of the reduction in magnetization can be important from the application perspective since it can affect the efficiency of the beads’ removal from the water medium after treatment. Full article

Chainsaws are widely used in various occupational settings, including forestry, landscaping, farming, and by homeowners for tasks like tree felling, brush clearing, and firewood cutting. However, the use of chainsaws poses significant risks to operators and bystanders. This research quantified and compared occupational and nonoccupational injuries caused by contact with chainsaws and related objects during the period from 2018 to 2022. The emergency department and OSHA (Occupational Safety and Health Administration) data were used to characterize the cause and nature of the injuries. Results suggest that for this five-year period an estimated 127,944 people were treated in U.S. emergency departments for chainsaw-related injuries. More than 200 non-fatal and 57 fatal occupational chainsaw-involved injuries were found during the same period. Landscaping and forestry were the two industries where most of the occupational victims were employed. Upper and lower extremities were the most likely injured body parts, with open wounds from cuts being the most common injury type. The majority of fatal injuries were caused by falling objects such as trees and tree limbs while using a chainsaw. Our suggestions to reduce injuries include proper training and wearing personal protective equipment, as well as making sure any bystanders are kept in a safety zone away from trees being cut. Full article

Furunculosis caused by Aeromonas salmonicida poses a significant challenge to the sustainable production of maraena whitefish (Coregonus maraena). This case report outlines a multi-year disease management strategy at a European whitefish facility with two production departments, each specialising in different life-cycle stages. Recurrent outbreaks of A. salmonicida necessitated the development of effective vaccination protocols. Herd-specific immersion vaccines failed to confer protection, while injectable formulations with plant-based adjuvants caused severe adverse reactions and mortality rates exceeding 30%. In contrast, the bivalent vaccine Alpha Ject 3000, containing inactivated A. salmonicida and Vibrio anguillarum with a mineral oil adjuvant, yielded high tolerability and durable protection in over one million whitefish. Post-vaccination mortality remained low (3.3%), aligning with industry benchmarks, and furunculosis-related losses were fully prevented in both departments. Transcriptomic profiling of immune-relevant tissues revealed distinct gene expression signatures depending on vaccine type and time post-vaccination. Both the herd-specific vaccine and Alpha Ject 3000 induced the expression of immunoglobulin and inflammatory markers in the spleen, contrasted by reduced immunoglobulin transcript levels in the gills and head kidney together with the downregulated expression of B-cell markers. These results demonstrate that an optimised injectable vaccination strategy can significantly improve health outcomes and disease resilience in maraena whitefish aquaculture. Full article

In an ever-evolving digital environment, organizations must adopt advanced technologies for real-time big data processing to maintain their competitiveness and growth. However, selecting appropriate technologies is a challenge, particularly for small and medium-sized enterprises (SMEs). This study develops a literature review to analyze the methodologies used in the selection of technologies, with a special focus on those associated with the Industry 4.0. Knowledge Development Process-Constructivist (ProKnow-C) method, which was used to build a bibliographic portfolio, examining approximately 3400 articles published between 2005 and 2024, from which 80 were selected for a detailed analysis. The main methodological contributions come from research articles, the ScienceDirect database, the Expert Systems with Applications Journal, studies conducted in Turkey, and publications from the year 2023. The results highlight the predominant use of multi-criteria techniques, emphasizing hybrid approaches that combine various decision-making methodologies. In particular, the analytic hierarchy process (AHP) and TOPSIS methods were employed in 51.25% of the analyzed cases, either individually or in combination. It is concluded that technology selection should be based on flexible and adaptive approaches tailored to the organizational context, aligning long-term strategic objectives to ensure business sustainability and success. Full article

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment. Full article

The increasing need for sustainable valorization of fossil-based and waste-derived materials has gained interest in converting complex organic matrices such as kerogen into valuable chemicals. This study explores a two-step oxidative strategy to decompose and valorize kerogen-rich oil shale, aiming to develop a locally based source of aliphatic dicarboxylic acids (DCAs). The method combines air oxidation with subsequent nitric acid treatment to enable selective breakdown of the organic structure under milder conditions. Air oxidation was conducted at 165–175 °C using 1% KOH as an alkaline promoter and 40 bar oxygen pressure (or alternatively 185 °C at 30 bar), targeting 30–40% carbon conversion. The resulting material was then subjected to nitric acid oxidation using an 8% HNO₃ solution. This approach yielded up to 23% DCAs, with pre-oxidation allowing a twofold reduction in acid dosage while maintaining efficiency. However, two-step oxidation was still accompanied by substantial degradation of the structure, resulting in elevated CO₂ formation, highlighting the need to balance conversion and carbon retention. The process offers a possible route for transforming solid fossil residues into useful chemical precursors and supports the advancement of regionally sourced, sustainable DCA production from unconventional raw materials. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports. Full article

Growing environmental concerns have driven increased interest in solid-state thermal technologies based on the elastocaloric properties of shape memory alloys (SMA). This work examines the elastocaloric effect (eCE) in Ni-Ti SMA sheets subjected to cyclic bending, providing quantitative thermal characterization of their behavior under controlled loading conditions. The experimental investigation employed passive thermography to analyze the thermal response of Ni-Ti sheets under two deflection configurations at 1800 rpm loading. Testing revealed consistent adiabatic temperature variations (ΔT_ad) of 4.14 °C and 4.26 °C for the respective deflections during heating cycles, while cooling phases demonstrated efficient thermal homogenization with temperature gradients decreasing from 4.13 °C to 0.13 °C and 4.43 °C to 0.68 °C over 60 s. These findings provide systematic thermal documentation of elastocaloric behavior in bending-loaded Ni-Ti sheet elements and quantitative data on the relationship between mechanical loading parameters and thermal gradients, enhancing the experimental understanding of elastocaloric phenomena in this configuration. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article

This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty of the present contribution lies in the gamification of this learning method. A popular game, Trivial Pursuit, was adapted to enable students to acquire knowledge in a playful manner while preparing for upcoming technical challenges. Various technical subjects were chosen to create new cards for the game. A total of 180 questions and their answers were created. The colored tokens were then used to trace manufactured products. This teaching experiment was conducted as part of a project-based learning program with third-year Bachelor students (Electrical Engineering and Industrial Computing Department). The game components associated with the challenge proposed to the students comprised six key elements: objectives, challenges, mechanics, components, rules, and environment. Within the framework of the Industry 4.0 concept, this pedagogical activity focused on the knowledge, understanding, development, and application of an RFID (Radio Frequency Identification) system demonstrating the capabilities of this technology. This contribution outlines the various stages of the work assigned to the students. An industrial partner was also involved in this work. Full article

Educational facilities are the physical environment that supports the academic process for a better education. The quality of offices as workspaces is crucial in creating a supportive environment to enhance the staff and students’ experience. This paper aims to study the concept of space personalization and its impact on faculty members’ productivity, performance, and satisfaction in universities’ educational facilities. To achieve this aim, the research applied the qualitative research method of semi-structured interviews to gather comprehensive data about user experience. Approaching 39 faculty members within Al Yamamah University across three departments within the College of Engineering (Architecture, Industrial, and Computer). Data were analyzed using thematic analysis for qualitative insights, focusing on environmental aspects (such as: natural lighting, ventilation, noise control, etc.), psychological factors (such as: privacy, aesthetic appeal, etc.), and architectural settings (such as: area, space layout, materials, etc.). The research proposes a methodological framework for design considerations for office spaces in universities, fostering more flexible and personalized designs for enhancing sense of ownership and well-being. Findings indicate that personalized office spaces significantly enhance faculty satisfaction and productivity. Qualitative data highlighted that a lack of privacy in standardized offices adds stress and an overwhelming environment. These findings suggest that universities should consider flexible office designs to optimize academic work environments. Full article

The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions. A comprehensive search of five academic databases (Scopus, the Web of Science, IEEE Xplore, PubMed, and EMBASE) identified 89 peer-reviewed studies published between 2019 and 2025. PRISMA 2020 guidelines were implemented, resulting in a final dataset of 32 studies. In addition to analyzing applications, this study identifies the AI/ML grouped into five main categories, providing a clearer understanding of their impact on PSC resilience. The findings reveal that despite AI/ML’s promise, significant research gaps persist. Particularly, AI/ML-driven regulatory compliance and real-time supplier collaboration remain underexplored. Over 59.3% of studies fail to address regulatory frameworks and ethical considerations. In addition, major challenges emerge such as the limited real-world deployment of AI/ML-driven solutions and the lack of managerial impacts on PSC resilience. This study emphasizes the need for stronger regulatory frameworks, broader empirical validation, and AI/ML-driven predictive modeling. This study proposes recommendations for future research to foster more efficient, transparent and ethical PSCs capable of navigating the complexities of global healthcare. Full article