Search Results (1,794)

Background and Objectives: Robotic-assisted total knee arthroplasty (rTKA) is being increasingly used to improve surgical precision, soft-tissue balancing, and functional recovery. However, evidence comparing rTKA with conventional manual TKA (mTKA) across functional, patient-reported, neuromotor, and safety outcomes remains heterogeneous. Materials and Methods: This narrative (non-systematic) review synthesises studies evaluating functional outcomes, patient-reported outcome measures (PROMs), joint awareness, range of motion (ROM), neuromotor recovery, and complications following rTKA versus mTKA. Study inclusion was based on author judgement and data accessibility. The reviewed evidence included five randomised controlled trials, 9 retrospective studies, six prospective non-randomised studies, two meta-analyses, one cross-sectional study, and one umbrella review, covering CT-based and imageless robotic platforms, including semi-active and active systems such as MAKO, NAVIO, CORI, ROSA, ROBODOC, CUVIS Joint, SkyWalker, TSolution One, AKEC, JIANJIA, and YUANHUA. Results: rTKA consistently demonstrated outcomes comparable to mTKA in PROMs (OKS, KOOS, WOMAC, KSS), with some studies reporting modest early improvements in pain and function. Joint awareness and patient satisfaction showed the most consistent early advantages for rTKA. Early postoperative ROM and neuromotor recovery, including balance and gait symmetry, were improved with rTKA, likely due to enhanced alignment and soft-tissue balancing; however, mid- and long-term outcomes were similar. Complication rates were low and comparable, with robotic-specific issues being rare and self-limited. Conclusions: rTKA provides small but reproducible early benefits in joint awareness, neuromotor function, and patient satisfaction, without clear long-term superiority. These early advantages may translate into meaningful population-level benefits, including faster recovery and potential healthcare cost reduction. Further high-quality studies are needed to assess long-term clinical and economic outcomes. Full article

The leaf miner Leucoptera coffeella Guérin-Méneville & Perrottet, 1842 (Lepidoptera: Lyonetiidae), is a major pest of coffee plants (Coffea spp.) worldwide. It is recognized for its widespread occurrence in coffee plantations and the quantitative and economic losses it causes to coffee production, especially in regions with higher temperatures and greater water deficits. We evaluated historical and current research approaches to leaf miner management, establishing current knowledge through a systematic review of research on biological control agents (BCAs), plant extracts and cover crops or intercropping for L. coffeella control, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conducted a systematic review of research findings from 1980 to 2025, developing a set of a priori criteria for subsequent replication of the review process. This review covers 130 publications, of which 28 met the inclusion criteria. The selected studies were conducted in five countries, although 75.00% of the studies were from Brazil. The BCAs accounted for 60.71%, followed by plant extracts (32.17%) and cover crops or intercropping (7.15%). Field studies were predominant, prioritizing diagnostic studies, surveys, studies on alternative control methods, and finally studies on BCAs, providing an effective solution. The limitations and prospects for their management were analyzed, and we highlighted recommendations that will improve future studies. Full article

Spent coffee grounds (SCG) are generated in millions of tonnes annually due to rising global coffee consumption, posing significant challenges, including greenhouse gas emissions, waste-disposal problems, and the loss of valuable compounds like caffeine, dietary fibre, phenolics, antioxidants, proteins, and lipids, offering prospects for potential valorization. Its composition is influenced by several factors. This review focuses on recent advancements in the valorization of SCG across sectors such as food, nutraceuticals, bioenergy, and packaging. The emphasis is on pretreatment, extraction, and bioconversion methods, as well as current research gaps, limitations, and future directions. SCG valorization is oriented toward integrated, multi-product biorefinery systems based on green extraction and bioconversion technologies to recover high-value compounds in both the food and non-food sectors. Nonetheless, industrial scalability is limited by composition variability, energy-intensive processing, techno-economic constraints, and safety and regulatory issues that remain unresolved. The shortcomings, such as inadequate standardized characterization, toxicological validation, and pilot-scale studies, are critical gaps. Scalable, energy-efficient processes, AI-assisted optimization, and regulatory alignment development should be a priority in future research, so that sustainable and commercial deployment is possible. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being applied across the infectious-disease pathway, from syndromic surveillance and imaging triage to etiologic support, antimicrobial stewardship, and prognostication. However, the maturity of evidence differs considerably across use cases, and apparent technical performance does not always translate into real-world clinical utility. Methods: This structured narrative review synthesizes current evidence on the principal clinical and public-health applications of AI in infectious diseases, with particular attention to external validation, workflow integration, economic implications, and governance. Results: The strongest near-term evidence supports narrow-AI applications linked to constrained workflows, especially tuberculosis chest-radiograph triage, selected host-response and antimicrobial-resistance prediction tools, and clinician-facing stewardship aids. By contrast, sepsis prediction illustrates how internal model performance may deteriorate on external validation and generate substantial alert burden when implemented in routine care. Economic evaluations are promising but remain predominantly model-based and context-dependent. Evidence for generative AI and large language models is still in an early phase, consisting largely of vignette studies, retrospective comparisons, and small single-center pilots rather than prospective outcome-based evaluations. Conclusions: Overall, the most realistic clinical role of AI in infectious diseases is augmentation rather than replacement: prioritizing scarce diagnostic capacity, shortening time to action, and improving antibiotic selection. Safe translation into practice requires, in order, external validation with local calibration, prospective impact assessment, and governance frameworks that address drift, accountability, transparency, and human oversight. Full article

We examine the association between Environmental, Social, and Governance (ESG) initiatives and corporate performance using a sample of 360 S&P 500 firms from 2010 to 2018. Employing MSCI ESG ratings and controlling for industry and time effects, we find that environmental initiatives positively associate with current profitability (ROA), while gender diversity correlates with long-term growth prospects (Tobin’s Q). This study moves beyond aggregated ESG metrics by providing a disaggregated analysis, revealing that different ESG dimensions affect performance through distinct financial mechanisms. To address common endogeneity concerns, we implement a rigorous empirical identification strategy, including propensity score matching, Heckman selection models, and instrumental variable approaches using industry-average instruments. Our results quantify the economic magnitude of these effects, demonstrating that a one-standard-deviation increase in environmental performance corresponds to a 0.92 percentage point increase in ROA, representing approximately $176 million in additional annual net income for the median firm. These findings provide theoretical advancement for the resource-based view and stakeholder theory by showing that specific ESG capabilities serve as valuable, inimitable resources. Ultimately, the study contributes standardized, high-resolution evidence on how specific ESG dimensions drive superior corporate performance. Through mechanism analysis, we show that environmental effects operate primarily via operational cost reduction and risk mitigation, while gender diversity creates value through enhanced innovation findings, which has direct implications for corporate ESG strategy. Full article

Italian “Agricultural Engineering”, while evolving toward the broader, interdisciplinary field of “Biosystems Engineering” (which also includes the study of biomasses/biomaterials, field and forest mechanization in difficult contexts and advanced post-harvest agri-food technologies), suffers from a structural critical issue due to its historical academic placement within the Agricultural rather than the Engineering departments. This positioning limits the depth of the technical subjects proposed to the students and does not facilitate the necessary collaboration with core engineering disciplines in research and didactics activities, thereby potentially slowing innovation in crucial fields like agro-bio-energies, precision agriculture and field robotics. To address this misalignment and foster inter-departmental synergy, this study proposes adopting the Contamination Lab (C-Lab) model as the archetype of a possible framework of academic and professional networking involving and centered on Agricultural Engineering. C-Labs (transdisciplinary platforms proposed by the Italian Ministry of University and Research) function as experiential laboratories, gathering students from Engineering, Agronomy, Computer Science, and Economics to collaboratively develop solutions to real-world challenges posed by industry stakeholders. The integration of a permanent, thematic C-Lab focused on agri-forestry and food machinery, supported by methodologies for enhancing creativity in technical fields, such as design thinking, represents an effective (and necessary) strategy to give Agricultural Engineering greater visibility in the Italian (and international) scenario and, prospectively, relocate it to the center of any research involving the technological and technical aspects of agriculture, forestry and food production. It is concluded that this initiative can serve as an institutional bridge for hybrid training, which is essential for aligning academic competencies with the growing demands for innovation and multidisciplinary professionalism in the national agri-food tech sector. Full article

The global transition to electric mobility is crucial for reducing transportation-related emissions, although there is a scarcity of empirical research on customer adoption psychology in transition economies in Central Asia. This study investigates the economic and structural drivers of electric vehicle purchase intention in the Republic of Uzbekistan. Data collected from prospective customers across large city hubs were analyzed using a dual hierarchical multiple linear regression model, supported by an empirical bootstrapping procedure with 2000 resamples, based on the rational choice theory and bounded rationality. The structural model shows that baseline socio-demographics explain insignificant initial variance ($R^2$ = 0.105); however, the integration of primary theoretical constructs yields a significant incremental variance change ($\Delta R^2$ = 0.096), explaining 20.1% of the total variance. Inferential tracking confirms that government incentives are the only statistically significant driver of the purchase intention (p = 0.009). Conversely, purchase cost (p = 0.251) and charging infrastructure (p = 0.475) lack direct significance. However, partial collinearity and infrastructure expectation effects systematically change these localized contact points. The study concludes that consumer intent in this emerging marketplace is primarily anchored to macro-level institutional policy signaling rather than immediate vehicle-specific characteristics or current physical network constraints. Full article

Understanding how consumers perceive the value of travel packages is essential for pricing and product design. Grounded in behavioral economics frameworks, such as Prospect Theory and Mental Accounting, this study analyses satisfaction across three progressive travel packages before and after explicit price disclosure, exploring multi-attribute service valuation and the moderating influence of traveller profiles. Using a quantitative approach with 387 higher education participants, expected satisfaction was measured through a two-phase price disclosure design. Inferential statistical analyses revealed that satisfaction levels decreased significantly for all packages once prices were revealed, with the sharpest decline occurring in the highly comprehensive, all-inclusive option, validating a psychological threshold of value saturation. Packages comprising only essential elements (flights, accommodation with breakfast and insurance) yielded the highest consistent post-price satisfaction, with these core structural components identified as the absolute most valued attributes. Findings suggest that explicit price disclosure acts as a negative moderator of expected satisfaction, triggering an immediate psychological pain of paying, particularly among independent travellers who exhibit higher price sensitivity and remain more analytical of bundled configurations than users of physical travel agencies. This study provides a framework for stakeholders to avoid over-bundling and optimize product efficiency. Furthermore, it contributes to academic discourse on generational consumer behaviour by highlighting how individual travel organization profiles within an emerging European cohort shape the perceived utility and fairness of tourism pricing. Full article

Selective dry cow therapy (SDCT) has emerged as a key strategy to reduce antimicrobial use in dairy production while maintaining udder health. This study aimed to evaluate the feasibility and impact of implementing SDCT in Romanian dairy farms by comparing low-risk cows treated with internal teat sealant only (ITS) at dry-off with low-risk cows treated with intramammary antibiotics at dry-off. A prospective field study was conducted on two commercial dairy herds, including 87 cows classified based on somatic cell count (SCC) and differential SCC (DSCC), and compared with a historical cohort of 37 cows. Udder health parameters, milk yield during the first 100 days in milk (DIM), antimicrobial use, and economic outcomes were evaluated. No significant differences were observed between groups in terms of postpartum intramammary infections, somatic cell score, DSCC, or clinical mastitis incidence. Milk yield during early lactation was also not affected by treatment. The ITS-only strategy resulted in a substantial reduction in antimicrobial use (−88.8% per cow) without significant differences in total economic costs. Farm-related differences highlighted the influence of management conditions on outcomes. These findings indicate that, in low-risk cows, SDCT using ITS alone is a safe and effective alternative to antibiotic treatment and support the feasibility of implementing SDCT under Romanian dairy production conditions as a sustainable strategy to promote the targeted and prudent use of antimicrobials while reducing unnecessary antibiotic exposure in dairy herds. However, given the limited number of herds and animals included, further studies are needed to confirm these promising findings under a broader range of production conditions. Full article

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects. Full article

The corrosion of metal components leads to substantial economic losses and poses serious safety hazards. While organic coatings are regarded as an effective countermeasure, conventional epoxy resins (EPs) often exhibit high brittleness and insufficient corrosion resistance after curing. To overcome these limitations, this study proposes a novel modification strategy. A multilayer graphene-reinforced epoxy composite coating was fabricated via a layer-by-layer spraying process, employing uniformly dispersed modified aramid nanofibers (ANFs) and low-defect graphene as functional fillers. Polydopamine (PDA) was utilized to improve the dispersion of graphene oxide (GO), mitigate defect-associated permeation pathways, and enhance the interfacial bonding between the graphene layer and the epoxy matrix, thereby ensuring coating integrity. Tannic acid (TA) effectively improves the dispersion of ANF within the epoxy, preventing stress concentration. The corrosion resistance and mechanical properties of the composite coating were systematically evaluated. Results demonstrate that the coating achieves a low-frequency impedance of 1.98 × 10¹⁰ Ω·cm². With the incorporation of 0.05% TA-modified ANFs, the elongation at break increases to 68.79%, and the impact resistance is significantly enhanced, with the impact height reaching 50 cm. The composite coating preparation strategy in this work offers a novel approach for constructing multifunctional composite coatings, demonstrating broad application prospects. Full article

Background: The evolution of digital dentistry has enabled the development of more predictable and less invasive protocols for bone augmentation and prosthetic rehabilitation. This case report introduces an integrated digital workflow combining the Digital CarroTrack technique with the “One-Step Crown” concept to optimize clinical outcomes and efficiency. Case Description: A patient requiring alveolar ridge reconstruction and implant therapy was treated using the Digital CarroTrack technique. This approach utilized precise digital planning to simultaneously harvest an autogenous bone cylinder from the surgical site and reposition it for augmentation, eliminating the need for a secondary donor site. Following implant placement, a “one-step crown” protocol was implemented. A provisional restoration served as a template for soft tissue contouring, and its emergence profile was directly replicated in a pre-designed definitive crown. The bone cylinder fixation screw was retrieved during the final prosthetic delivery, ensuring a streamlined workflow. Results: In this case, the integrated digital approach facilitated accurate bone cylinder placement and implant positioning. At the 2-year follow-up, clinical and radiographic examinations confirmed excellent stability of both hard and soft tissues, with no marginal bone loss or soft tissue recession. The procedure reduced the number of clinical stages, treatment time, and patient morbidity compared to traditional methods. Conclusions: This case report suggests that the combination of the Digital CarroTrack Technique and the One-Step Crown concept may represent a promising, minimally invasive, and time-efficient approach for complex implant-prosthetic cases. Digital planning appeared to support procedural accuracy while reducing surgical invasiveness and the number of clinical stages. Further prospective studies with larger patient groups, objective volumetric measurements, longer follow-up, and dedicated cost-effectiveness analyses are needed to confirm the predictability, clinical effectiveness, and potential economic benefits of this approach. Full article

This paper examines changes in the macroeconomic indicators of the member countries of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) following their accession to the agreement. This study aims to identify shifts in the structural comparability of national economies and to assess the processes of macroeconomic convergence in the context of sustainable development. To achieve this objective, reference pools of CPTPP member countries are constructed, and their digital profiles are developed based on key macroeconomic indicators and grouped into three blocks: (1) indicators of economic growth and the state of the real sector, including GDP (constant 2015 US$), GDP growth, annual %, gross capital formation, % of GDP, unemployment, total % of total labor force, and national estimate; (2) indicators of foreign economic activity and trade openness, including exports of goods and services, % of GDP, imports of goods and services, % of GDP, external balance on goods and services (% of GDP), foreign direct investment, net inflows, % of GDP, and trade, and % of GDP; (3) indicators of financial and macroeconomic stability including inflation, consumer prices, annual %, central government debt, % of GDP, and gross savings, and % of GDP. Based on the digital profiles, similarities/differences in the economies were examined by applying linear discriminant analysis (LDA). The empirical framework covers two periods: (1) 2013–2017 (pre-accession) years and (2) 2019–2023 (post-accession) years. The results indicate that the economies of member countries in 2013–2017 exhibited a high degree of heterogeneity. In contrast, the 2019–2023 period demonstrates a tendency toward partial convergence of macroeconomic parameters, as evidenced by a reduction in distances between country profiles in the discriminant space. While interpreting the results, it is acknowledged that the 2019–2023 period coincided with the effects of the global crisis caused by the COVID-19 pandemic, which significantly impacted international trade dynamics. For most countries, this period was characterized by a decline in several macroeconomic indicators and investment activity, an increase in debt burdens, and enhanced heterogeneity in economic dynamics, which was taken into account when interpreting macroeconomic convergence processes within the CPTPP. The scientific novelty of the study lies in its application of an approach based on the analysis of the structural similarity of the macroeconomic profiles of CPTPP countries, which complements traditional assessments of the effects of economic and trade integration. The practical significance of the findings is associated with their potential use in evaluating the prospects for CPTPP expansion and in modeling alternative scenarios of participation and sustainable development within international trade agreements under conditions of global economic transformation. Full article

This paper examines the strategic incentives to adopt sustainability measures within the positioning perspective on competitive advantage. While much of the existing literature emphasizes “win–win” opportunities, suggesting that a commitment to sustainability can simultaneously enhance economic, social and environmental performance, this study adopts a more critical stance. Drawing on the logic of trade-offs inherent in competitive strategy, it argues that the internalization of environmental and social externalities often entails costs that must be justified through price premiums. Integrating insights from strategic management and consumer research, the paper analyzes how demand-side conditions shape the viability of sustainability as a basis for differentiation, with particular attention to consumer involvement and information transparency across niche and mass markets. To capture these dynamics, the paper develops a conceptual 2 × 2 framework identifying how varying levels of involvement and transparency shape firms’ incentives for sustainability differentiation and greenwashing. The analysis suggests that sustainability is most viable as a differentiation strategy in niche markets characterized by high involvement and transparency, whereas its prospects in mass markets remain limited due to price sensitivity, low engagement, and imperfect information. These findings challenge optimistic assumptions about the scalability of sustainability through competitive market mechanisms and highlight the structural constraints that favor cost-based competition and greenwashing. Full article

Drought stress is one of the most severe constraints affecting wheat production worldwide. Under these conditions, the development of sustainable and economically viable strategies, such as seed priming, is essential to improve wheat performance and drought resilience. The present study carried out a greenhouse experiment on four Mediterranean durum wheat cultivars (Triticum turgidum ssp. durum Desf), i.e., Karim (Kr) and Khiar (Kh) from Tunisia and Espelta (Esp) and Mocho (Mo) from Spain, subjected to drought stress conditions, and using primed abscisic acid (ABA), indole-3-acetic acid (IAA), melatonin (Mlt), and salicylic acid (SA), and non-primed seeds. In order to assess the physio-biochemical responses of durum wheat, such as plant growth, chlorophyll, relative water content (RWC), water potential (Ψw), osmotic potential (Ψs), proline, soluble sugars, starch, glycine betaine, hydrogen peroxide, malondialdehyde, and antioxidant enzyme activities. The results showed that water stress significantly reduced plant growth, SPAD index, RWC, Ψw, and Ψs, while upregulating H₂O₂ and MDA levels, depending on the wheat cultivars. Soluble sugars decreased, whereas starch, glycine betaine, and proline accumulated in all cultivars. Superoxide dismutase activity was reduced (24–37%) under water stress as compared to the control condition, while APX, CAT, and POD activities significantly increased. Among the cultivars, Esp exhibited the greatest plasticity in response to water deficit, whereas Kh appeared to be most sensitive. Furthermore, the present results revealed that the priming durum wheat seeds with ABA, IAA, Mlt, and SA improved leaf hydration, particularly through soluble sugar accumulation. Seed priming also alleviated oxidative stress by reducing H₂O₂ and MDA levels and stimulating APX, CAT, POD, and SOD activities. Plants grown from non-primed seeds of Spanish and Tunisian cultivars exhibited differential responses to drought stress, and those derived from primed seeds showed varying degrees of enhanced drought tolerance. Espelta demonstrated a high potential for stress tolerance and responsiveness to priming, followed by Karim, whereas Khiar was the most sensitive cultivar. Overall, the cultivars can be ranked in decreasing order of stress tolerance as Esp > Kr > Mo > Kh. These findings highlight the potential of phytohormone-based seed priming as an efficient and practical approach to enhance drought resilience in durum wheat, offering promising prospects for improving crop performance and stability under increasingly water-limited conditions in the era of climate change. Full article