Search Results (121,157)

The article presents the results of research conducted at Bydgoszcz University of Science and Technology concerning the abundance of a serious maize pest—the European corn borer-Ostrinia nubilalis Hbn (O. nubilalis). The study focused on the actual occurrence of this pest in maize stubble grown after different forecrop and among different maize cultivars. Our research fits perfectly within the framework of Integrated Pest Management (IPM) and is important because feeding by the European corn borer increasingly leads to substantial yield losses. From a sustainability perspective, knowledge of larval abundance and vertical distribution may support the development of targeted non-chemical control measures that complement existing Integrated Pest Management strategies. For the purposes of this study, maize was harvested for green fodder in September and for grain in November 2025. It was found that up to three pest larvae were present in some stubbles. On average, for every 25 plants analyzed at stubble grain harvest, over 30 larvae were found inside the stems—some contained as few as three larvae. It was found that the forecrop did not significantly affect larval abundance, whereas cultivar characteristics were the key factor differentiating their occurrence. The results of this study indicate that maize growers should pay increasing attention to this serious pest and strive for its more effective control, particularly through the use of the numerous available non-chemical methods for regulating its population. A patented design solution developed by the authors for the mechanical reduction in O. nubilalis abundance may also be helpful in this regard. Full article

Ovarian cancer (OC) is the second most common gynecological malignancy and remains one of the leading causes of gynecological cancer-related mortality worldwide. A major clinical challenge is the lack of an accurate and widely applicable strategy for identifying patients at high risk of malignancy at an early stage. In this context, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic performance. Among AI technologies, large language models (LLMs) have recently shown considerable potential in healthcare applications. In this study, we evaluated the diagnostic performance of ChatGPT (GPT-5) in classifying 300 adnexal masses as benign or malignant and compared its performance with that of the IOTA Simple Rules, the ADNEX model, and expert subjective assessment. We also assessed ChatGPT’s ability to predict the most likely histological diagnosis for each lesion. All adnexal masses were described using the International Ovarian Tumor Analysis (IOTA) terminology, and histopathological examination served as the reference standard. Our findings showed that expert subjective assessment achieved the highest overall diagnostic performance for both benign/malignant classification (accuracy 87.3%; 95% CI, 83.0–90.9%) and prediction of the presumed histological diagnosis. ChatGPT A and ChatGPT B reached a sensitivity of 72.3% and 73.5%, a specificity of 74.5% and 75.9%, a positive predictive value of 75.2% and 76.5%, and a negative predictive value of 71.5% and 72.8%, respectively (inconclusive responses counted as misclassifications), with an overall accuracy of 73.3% and 74.7%. After adequate validation, large language models might complement existing decision-support tools for less experienced examiners, without replacing expert evaluation. Their ease of use and reliance on standardized ultrasound descriptors make them accessible to ultrasonographers with varying levels of expertise. Full article

This study investigated the distribution of ⁴⁰K, ¹³⁷Cs and ⁹⁰Sr during the processing of cow milk into cottage cheese from central Bosnia and Herzegovina and Livno cheese from the southwestern production area, with particular emphasis on physicochemical composition and regional differences. Raw milk (n = 15 per group), corresponding cheeses (n = 15 per group), and composite whey samples (one per cheese type) were analyzed for protein, fat, dry matter, water content, ⁴⁰K, and ¹³⁷Cs, as well as titratable acidity in milk samples. In a random second-stage subset, because of the cost and complexity of the radiochemical method, ⁹⁰Sr was determined. Mean activity concentrations in milk used for cottage and Livno cheese production were 42.31 and 40.77 Bq/L for ⁴⁰K, 0.030 and 0.057 Bq/L for ¹³⁷Cs, and 0.015 and 0.030 Bq/L for ⁹⁰Sr, respectively. Corresponding cheese means were 26.45 and 28.23 Bq/kg for ⁴⁰K, 0.038 and 0.087 Bq/kg for ¹³⁷Cs, and 0.073 and 0.369 Bq/kg for ⁹⁰Sr. Processing retention factors showed limited transfer of ⁴⁰K and ¹³⁷Cs into cheese, whereas ⁹⁰Sr was retained mostly in the curd. Significant regional differences were observed for ¹³⁷Cs in both milk and cheese, whereas ⁴⁰K did not differ significantly between regions. Differences between regions for ⁹⁰Sr were also indicated, but these results should be interpreted cautiously because it was analyzed only in a limited second-stage subset. These findings show that radionuclide behavior in artisanal dairy systems is jointly shaped by radionuclide chemistry, cheese technology, and local environment, highlighting the need for site-specific food-chain assessment. Full article

Operational technology (OT) systems, such as power-grid controls and factory automation, are replacing cryptographic mechanisms vulnerable to future quantum computers with post-quantum (PQ) cryptography. However, older backups, standby systems, trust stores, and failover paths may retain weaker cryptography after the active system is upgraded. Legitimate recovery can reactivate these weaker states and allow them to regain privileged authority, reducing achieved protection. Existing work addresses PQ deployment, crypto-agility, secure recovery, rollback protection, attestation, and continuous authorization, but these mechanisms do not by themselves determine whether legitimate recovery can restore weaker cryptographic states that may regain privileged authority. We introduce PQReach-OT, which analyzes recovery paths before failure, keeps the required cryptographic protection level separate from the recoverable state, and requires fresh evidence before privileged authority is restored. We conducted a controlled mechanism-validation study using 570 deterministic recovery variants across 19 specified recovery scenario families. Seven configured mechanisms were exercised on the same variants, yielding 3990 primary records. The purpose of this matrix is to demonstrate and distinguish the registered recovery-security properties under controlled same-input cases, but it does not estimate the comparative effectiveness or weakness prevalence in operational OT deployments. Within these controlled cases, both PQReach-OT and the strong reactive experimental control satisfied post-transition grant safety. PQReach-OT additionally exercised the recovery-closure functions defined by the model: it identified all 510 current-compliant but recovery-unsafe variants before failure, selected a compliant alternative in all 390 applicable cases, rejected all 30 historical-floor replays, and detected all 30 exposures reachable only through multi-step recovery. Inventory completeness is an explicit assurance boundary: in the registered additive inventory-completion mutations, adding a compliant recovery state preserved Recovery-Closed Migration Coverage (RCMC) at 1.0, whereas adding a previously unrepresented below-floor state capable of regaining protected authority reduced RCMC from 1.0 to 0.0. Thus, RCMC is explicitly conditional on the represented recovery reachability: additive inventory completion can preserve the existing closure assessment or reveal an additional violation, but it cannot strengthen that assessment solely by enlarging the represented recovery space. These outcomes demonstrate the behavior and separability of the proposed recovery-closure mechanisms within the defined recovery semantics; evaluation in operational OT environments addresses the complementary question of external generalizability. Full article

Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed fieldbus and wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main contribution is a conceptual decision framework derived from the literature synthesis, linking service adaptation, communication-path feasibility and coordination scope to the placement of sensing, processing and inference functions. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures. Full article

AI-based writing support systems are increasingly being explored as tools for supporting English writing in educational contexts. In English as a Foreign Language (EFL) settings, learners often need support in generating ideas, organizing content, receiving feedback, and revising written texts. However, AI-based writing tools should be carefully designed and evaluated to ensure their usability, reliability, and educational relevance before being implemented in authentic learning contexts. This study aimed to design and evaluate the educational usability of WriteOn, an AI-based writing support system for EFL writing tasks that integrates AI and natural language processing technologies, including GPT-API, NLTK, spaCy, and BERT. Although the system was initially designed to support English diary writing in EFL contexts, its feedback structure and system architecture were intended to be extensible to broader writing tasks. A formative usability evaluation was conducted with eight experts in South Korea with backgrounds in EFL instruction, educational technology, instructional design, and human–computer interaction. A structured questionnaire was used to assess the system’s effectiveness, efficiency, learnability, satisfaction, memorability, error prevention, and cognitive load minimization. Based on the expert evaluation results, the system was refined through an iterative design process. The findings provided preliminary evidence of WriteOn’s overall usability while identifying several areas for improvement, including clearer feedback, more consistent design features, and enhanced language support in grammar correction modules. In response, the system was refined to include topic and genre selection, rubric-based evaluation, and a more consistent interface. These results suggest that integrating generative AI and natural language processing technologies into EFL writing support can support the development of reliable, scalable, and learner-centered writing tools. The study also highlights the importance of iterative and formative usability evaluation in developing AI-driven writing support systems that promote instructional efficiency, feedback transparency, and learner-centered revision. Full article

Educational institutions in developing countries face increasing electricity demand, frequent power outages, and rising operational costs due to their heavy reliance on utility grids and diesel generators. This study evaluates a grid-connected hybrid energy system (HES) for the Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Pakistan, to enhance energy reliability, reduce costs, and mitigate carbon emissions. Two scenarios are considered: (i) the existing utility grid and diesel generator system and (ii) a proposed hybrid configuration integrating solar photovoltaic (PV), a battery energy storage system (BESS), and the utility grid. The system is modeled and optimized using HOMER Pro based on a detailed campus load profile developed from institutional data. The proposed HES achieves a Net Present Cost (NPC) of PKR 223 million and a Cost of Energy (COE) of PKR 13.06/kWh while reducing CO₂ emissions by approximately 70% compared with the existing system. Moreover, the investment analysis demonstrates strong financial viability, yielding an NPV of PKR 1855.56 million, an ROI of 179.63%, an IRR of 180.34%, and simple and discounted payback periods of 0.55 and 0.59 years, respectively. Sensitivity analysis further demonstrates that a 100 kWh BESS provides the preferred configuration based on the trade-off between economic performance and reliability under varying solar irradiance, inflation rates, and discount rates. The findings highlight the potential of HESs to support sustainable campus electrification and offer practical insights for energy planning in educational institutions across developing countries. Full article

Against the background of coordinated digital and green transformation, this study uses data on Shanghai- and Shenzhen-listed A-share firms from 2010 to 2024. The first year in which both policies take effect in a firm’s city is taken as the coordinated policy shock year. Using a staggered difference-in-differences (DID) model and double machine learning (DML), this study evaluates how public-data openness and green data center policies affect corporate sustainability performance. The results indicate that entering dual-policy status significantly improves corporate sustainability performance. Further restricted-sample analysis documents a positive incremental dual-policy effect beyond a single-policy implementation, which provides suggestive evidence for theoretical policy complementarity. Mechanism tests reveal that dual-policy status is associated with enhanced credit availability, greater ambidextrous green innovation, reduced agency costs, and optimized human-capital allocation. These patterns provide suggestive evidence consistent with the four theoretically proposed transmission channels, though formal causal mediation is not established in the current empirical setup. Analysis based on the technology–organization–environment (TOE) framework shows that technological foundations, organizational capabilities, and the external green institutional environment all strengthen the policy synergy effect. Further analysis has found that each policy has a positive effect when implemented separately; compared with a single-pilot status, the dual-pilot status produces a significant positive net effect. Both implementation sequences generate positive synergy effects; based on the point estimates, the synergy effect is larger when green data centers are built before public-data openness is promoted. From a policy-combination perspective, this study reveals the complementary mechanism between data element supply and green computing capacity and provides firm-level evidence for the coordinated advancement of digitalization and greening. Full article

Fuel cell electric buses (FCEBs) are widely recognized as a promising low-carbon solution for long-distance public transportation. However, their adoption in intercity coach services remains limited, particularly at the demonstration stage, where multiple technological, economic, policy, and infrastructure factors interact. Prior research has predominantly examined these factors in isolation, with limited attention to their interrelationships. To address this gap, the present study proposes a hybrid multi-criteria decision-making (MCDM) framework that integrates a modified Delphi-based expert screening method, interval type-2 fuzzy sets (IT2FS), and the DEMATEL-based Analytic Network Process (DANP). Using the STEEP framework, twenty candidate criteria derived from the literature were screened by ten experts and refined to fifteen criteria relevant to the Taiwanese intercity coach context. A second panel of twelve experts then assessed the perceived directional influence relationships among the retained criteria using linguistic judgments modeled with IT2FS, and DANP was applied to derive network-adjusted priority weights. Expert assessments indicate that policy stability and long-term commitment, financial incentives and subsidy schemes, and infrastructure investment benefits received the highest priority weights in the current Taiwanese context. The perceived influence structure further indicates that the policy and technology dimensions primarily function as net influencing dimensions, whereas the economic and social dimensions are more strongly positioned as net-receiving dimensions within the expert-judgment network. These findings should be interpreted as context-specific, expert-based assessments rather than objectively established or statistically causal determinants of FCEB adoption. The results provide a structured decision-support perspective for governments, coach operators, and hydrogen supply-chain stakeholders when evaluating priorities for FCEB deployment in Taiwan and comparable demonstration-stage contexts. Full article

This study investigates employee perceptions of artificial intelligence (AI) in the field of recruitment and selection in Saudi Arabian companies. While the use of recruitment systems based on AI is growing, their acceptance by organisations is influenced by whether employees consider these systems to be useful, transparent, and fair, and how this affects their professional judgement. This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Socio-Technical Theory (STT) and explores a two-path explanatory model. Perceived usefulness is linked to perceived AI effectiveness in the AI-related pathway and is associated with transparency of AI, trust in AI systems, and organisational support for AI use. On the HR pathway, there is a negative association between technostress and HR support and readiness, while clarity of integration of AI is positively associated with HR support and readiness. The sample comprised 450 purposively selected respondents from Jeddah, Riyadh and Dammam, and data were analysed using Structural Equation Modelling (SEM) in AMOS. The perceived effectiveness of AI and HR’s support/readiness for AI was positively and significantly correlated with employee perceptions of AI in recruitment and selection. The relationships of these data should be interpreted as structural relationships; since the data are cross-sectional, they cannot be interpreted as causation or mediation. Further methodological issues include common method bias since all constructs were assessed via self-report and in the same questionnaire. Full article

Postharvest losses (PHLs) present a critical bottleneck to global food security, with fresh fruit losses reaching 23–40% due to rapid physiological deterioration, mechanical stress, and microbial decay. This review evaluates the metabolic cascades, including respiration kinetics, transpiration, and hormone-mediated factors, that govern fruit degradation. We reviewed thermal and non-thermal treatments for affordability, including thermal interventions such as hot water treatment (HWT), microwave treatment, and radio frequency treatment, alongside emerging non-thermal preservation modalities such as edible coatings, cold plasma atmospheric treatments, and ultrasound processing. The review places particular emphasis on climacteric tropical fruits such as mango, papaya, banana, and citrus, for which postharvest losses and preservation challenges are especially acute due to their climacteric nature. To address the quality tracking highlighted by modern commercial chains, this review integrates an assessment of non-destructive quality evaluation technologies, focusing on near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and electronic noses (E-noses). Overall, this review seeks to deliver a unified blueprint, arguing that sustainable postharvest preservation can benefit from context-specific hurdle-technology concepts that align accessible postharvest treatments with monitoring networks. We synthesise current evidence on individual thermal, non-thermal, and sensing technologies and use this to motivate potential hurdle strategies and treatment–monitoring pairings for diverse fruit supply chains. Full article

The Nirvana platform in Cave No. 47 of the Kizil Grottoes preserves an important example of early Buddhist earthen plaster and ground layer manufacture along the Silk Roads. This study investigated the material composition and stratigraphic sequence of its earthen plaster and ground layer using optical microscopy (OM), micro-Raman spectroscopy, Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray diffraction (XRD), micro-Fourier transform infrared spectroscopy (µ-FTIR), laser-diffraction particle-size analysis (LD-PSA), indirect enzyme-linked immunosorbent assay (ELISA), and archaeobotanical observation. The results reveal a differentiated multilayer system consisting of a coarse clay layer, a fine clay layer, an anhydrite-dominated white ground layer, and a hematite-bearing, anhydrite-rich red ground layer. The coarse clay layer exhibited a broader particle-size distribution and contained abundant chopped straw and barley husks. The fine clay layer was more homogeneous and contained numerous keratinous animal hairs, compatible with a provisional attribution to cattle. A weakly positive ELISA response suggests the possible presence of an ovalbumin-containing material within the earthen plaster. The results demonstrate systematic differentiation of mineral fractions and organic inclusions across successive layers. The identified sequence reflects local technological choices within broader Asian traditions and provides a material basis for understanding and conserving the Nirvana platform. Full article

The transition towards low-carbon energy systems is fundamentally reshaping the relationships between energy security, affordability, and environmental sustainability. Although the energy trilemma has become a widely adopted framework for analysing these objectives, existing studies often examine individual dimensions or bilateral trade-offs in isolation, providing limited understanding of how interactions evolve throughout the transition process. This review synthesises recent evidence on the energy trilemma in the context of the low-carbon transition and develops a dynamic, context-sensitive interpretation of interactions between its three dimensions. A structured integrative literature review with thematic synthesis was conducted using systematic searches of the Scopus and Web of Science databases, followed by multi-stage screening and qualitative synthesis. Rather than evaluating individual studies separately, the review integrates evidence across complementary research streams to identify recurring interaction patterns, trade-offs, synergies, and the conditions under which they emerge. The findings demonstrate that interactions within the energy trilemma are neither static nor universally characterised by unavoidable trade-offs. Energy security increasingly depends on electricity system resilience, critical mineral supply chains, and institutional capacity rather than fossil fuel availability alone. Affordability is shaped primarily by distributional processes, adaptive capacity, and equitable access to low-carbon transition opportunities rather than average energy prices. Security–affordability interactions evolve across different risk regimes, highlighting the economic value of resilience under geopolitical uncertainty and market disruption. Across all three dimensions, technological innovation, infrastructure development, governance quality, temporal dynamics, and socio-economic context determine whether policy interventions generate trade-offs or mutually reinforcing outcomes. Building on these findings, the review reconceptualises the energy trilemma as a dynamic and context-sensitive analytical framework rather than a static balancing exercise. The proposed framework integrates technological, institutional, temporal, distributional, and geopolitical perspectives to explain how interactions among energy security, affordability, and environmental sustainability evolve under different transition conditions. Full article

The design of modern railway vehicles is increasingly inseparable from advanced computation and programming. As engineering challenges become more complex, sophisticated numerical algorithms and powerful computational tools enable designers to solve problems that would otherwise be difficult, time-consuming, or even impossible to address using conventional methods. The global trend toward algorithm-based design and analysis is rapidly transforming the railway industry. Virtual prototypes and computer simulations have become essential elements of high-quality railway vehicle development. These simulations support a wide range of analyses, including statics, kinematics, dynamics, strength, durability, and reliability, covering both complete vehicles and their individual subsystems. Modern simulation tools can therefore provide comprehensive insight into the mechanical behavior of railway vehicles while also accounting for aspects related to technology, materials, operation, and other key engineering requirements. Against this background, the main objective of this study is to provide a comprehensive overview of computational optimization methods used in railway vehicle design. This study reviews the most widely applied numerical optimization procedures that have established a significant position in contemporary engineering practice and are particularly relevant to railway vehicle designers. The presented work describes the fundamental principles of the optimization process in railway vehicle design and provides a mathematical formulation of optimization problems. Particular attention is given to the optimization of the modal and spectral properties of railway vehicles, as well as to topology optimization, which offers new possibilities for developing lightweight and structurally efficient components. Three representative optimization problems are investigated to demonstrate the practical potential of these methods: the optimization of a corrugated sheet-metal structure, the main load-bearing rectangular profile of an open wagon, and a strut structure supporting the roof of a hopper wagon. In all three cases, the optimization objective was to minimize structural mass while preserving the required functional and mechanical properties. The results demonstrate the considerable potential of computational optimization in railway vehicle design. When an appropriate optimization method is selected and correctly applied, significant reductions in material consumption—and consequently in production costs—can be achieved without compromising the structural performance required for safe and reliable long-term operation. These findings highlight the important role of computational optimization as a powerful tool for developing lighter, more economical, and more efficient railway vehicles. Full article

Meat is an important element of a balanced diet and can be a significant source of valuable nutrients. Harmful compounds such as polycyclic aromatic hydrocarbons (PAHs), heterocyclic aromatic amines (HAAs), and nitrosamines (NAs) can be formed in meat products during heat treatment and processing. However, there are certain technological options for reducing them. Based on the review, it was concluded that reducing PAHs in smoked products can be achieved primarily by controlling the smoking process, including the smoke generation temperature, the method of generation, and the duration of contact with the smoke. PAHs in grilled products can be reduced by using an indirect grilling method or by using physical barriers and reducing fat content. HAAs content in products can be reduced by reducing the heating temperature and time as much as possible and by using plant ingredients with antioxidants. When reducing NAs, a key strategy is to reduce the amount of potential precursors (biogenic amines and nitrites) in the heated raw material. The technological initiatives presented in this study can serve as a basis for creating new products and developing new and improving existing good manufacturing practices aimed at reducing the content of harmful compounds in meat products. Creating new products and semi-finished products using literature data demonstrating the potential for reducing harmful compounds, designed to provide consumers with a high level of health security, is crucial for consumer health safety and further development opportunities for the meat industry. Full article