Search Results (1,942)

Organizations face a persistent tension between the need to hold employees accountable for results and the need to grant them the autonomy that fuels motivation, learning, and innovation. This conceptual paper theoretically develops and formalizes the tight–loose–tight (TLT) framework, a sequenced and cyclical approach to resolving this tension at the level of the individual employee. In the first tight phase, leaders and employees establish clear goals, expectations, and metrics that connect individual aspirations to organizational objectives. In the loose phase, leaders grant employees autonomy over how the work is accomplished, consistent with self-determination theory. In the final tight phase, leaders inspect what they expect through regular observation, feedback, and coaching, which then feeds forward into renewed clarity at the start of the next cycle. The framework integrates goal-setting theory, self-determination theory, and feedback and coaching research into a single dynamic process model, and it is distinguished from prior tight and loose constructs in the literature on organizational excellence, participative leadership, and national culture. Five research propositions are developed, together with guidance on measurement and research design, a rule that fixes each phase transition at the opening of the cycle, criteria for scoring cycle fidelity, and the condition under which the framework would be refuted. The framework’s application in an AI-enabled workplace is also considered. Implications for managers, limitations, and future research are discussed. Full article

The distributional specification in Markov-switching GARCH (MS-GARCH) models has historically been driven by empirical convention. This paper derives the regime-conditional Student-t innovation distribution from the Tsallis Maximum Entropy Principle, providing an information-theoretic foundation for the choice of heavy-tailed innovations. The GARCH variance dynamics and Markov-switching structure are standard modelling choices adopted independently of the MaxEnt derivation. The framework is applied to five major cryptocurrencies over January 2017 to March 2026, comprising 15,824 daily observations. Three principal findings emerge. First, Tsallis entropy maximisation under a variance constraint yields the q-Gaussian density, which coincides with the Student-$t_{{\nu }_k}$ distribution for $q_k=({\nu }_k+3)/({\nu }_k+1)$, with degrees of freedom determined endogenously from the empirical excess kurtosis. Second, calm-regime half-lives ${\tau }_C\in [1.21,2.37]$ days and stationary turbulent probabilities ${\pi }_T\in [0.254,0.437]$ confirm that both regimes are economically active across all assets; a Francq–Zakoïan stationarity verification confirms global ergodicity. Third, near-unity turbulent GARCH persistence suppresses the point-forecast advantage of regime-switching, consistent with a Fano-type Forecasting Irreversibility Bound; HAR-RV achieves the lowest QLIKE loss for three of five assets. Value-at-Risk backtests confirm adequate tail-risk calibration for four of five assets at the 1% and 5% levels, outperforming single-regime benchmarks. An empirical assessment of the VolShock extension identifies asset-class boundary conditions, motivating a proportional specification for future work. Full article

The European Union’s climate-neutrality objective is reshaping coal-dependent regions, but transition pathways differ according to historical coal dependence, institutional capacity, national energy systems, and regional economic structures. Coal phase-outs represent not merely a sector-specific energy shift, but a systemic socio-spatial transformation that reconfigures regional planning and territorial dynamics. This study examines how governance coordination, phase-out clarity, labour-market instruments, innovation strategies, and post-mining land reuse operate across three major Central European coal regions: the Ruhr Area (Germany), Upper Silesia (Poland), and the Moravian–Silesian Region (Czechia). It applies a qualitative, structured comparative review based on academic literature, policy and strategic documents, official statistics, and three flagship redevelopment cases within the framework of the EU RFCS-funded RAISING project. The comparison shows that the Ruhr Area’s long-term, corporatist coordination has facilitated structural diversification and land reuse over several decades, though challenges regarding job quality and socio-spatial inequalities persist. In Upper Silesia, negotiated agreements foster regional consensus, while the multi-decade transition timeline reflects national energy-security priorities, supply-security constraints, and institutional complexity. In the Moravian–Silesian Region, early reactive restructuring in the 1990s contrasts with a more coordinated contemporary phase leveraging EU transition funding and strategic investment. Across all three cases, transition outcomes are mediated by place-specific conditions rather than formal closure speed alone. The study demonstrates that while core principles of early planning, multi-level coordination, and integrated asset reuse offer valuable insights, their application remains context-dependent, highlighting the necessity for place-sensitive European and national transition policies. Full article

Feline coronaviruses (FCoVs) are evolutionarily dynamic alphacoronaviruses whose diversification has been driven by extensive genetic exchange and long-term adaptation. However, how genome plasticity is balanced with the evolutionary constraints required to maintain viral fitness remains poorly understood. Here, we analyzed 127 complete FCoV genomes collected worldwide over more than five decades to investigate the evolutionary processes governing long-term FCoV diversification. Genome-wide phylogenetic analyses resolved three major evolutionary lineages and demonstrated that the two recombinant lineages originated independently through recombination between feline and canine coronaviruses, highlighting repeated interspecies recombination as a recurrent source of genomic innovation. In contrast, gene-level evolutionary analyses revealed substantial discordance with whole-genome phylogenies, indicating that individual genomic regions have followed distinct evolutionary trajectories. Recombination and positive selection were concentrated within a limited subset of genes, particularly NSP3, NSP12, NSP2, and the spike gene, suggesting that evolutionary innovation preferentially targets proteins involved in virus–host interactions. Despite extensive genome restructuring and adaptive diversification, overall dinucleotide composition remained highly conserved across all lineages, with persistent CpG suppression and continued CpG depletion during the long-term evolution of the predominant GI lineage, revealing strong compositional constraints acting on FCoV genomes. Together, these findings support a multi-scale model of coronavirus evolution in which recombination reorganizes genome architecture, adaptive evolution refines genes at the virus–host interface, and long-term genomic constraints preserve fundamental compositional features. This work provides a conceptual framework for understanding how genome plasticity and evolutionary constraint are balanced during the long-term evolution of FCoVs and offers broader insights into the evolutionary organization of alphacoronavirus genomes. Full article

The alarming rise of antimicrobial resistance constitutes a critical global health challenge, threatening the efficacy of conventional antibiotic therapies. Despite this crisis, abandoning antibiotics is premature; instead, a deliberate integration of traditional and emerging antimicrobial approaches is urgently required. The worldwide proliferation of resistant pathogens reflects decades of monotherapy, inadequate stewardship, and the persistent expectation that each new drug class would resolve resistance crises. This review provides a comprehensive overview of the fundamental mechanisms underlying antibiotic action and bacterial resistance, including reduced membrane permeability, efflux pump activity, target modification, and enzymatic inactivation. Building on this foundation, a broad spectrum of innovative antimicrobial strategies designed to complement or enhance antibiotic efficacy was critically examined. These approaches include natural products with intrinsic antimicrobial properties, immunotherapy to modulate host defenses, and antimicrobial photodynamic therapy (aPDT), which utilizes light-activated photosensitizers to generate reactive oxygen species and achieve localized microbial destruction. Notably, aPDT can overcome bacterial resistance, thereby restoring antibiotic effectiveness. Light delivery to infection sites is feasible across nearly all anatomical locations, whether by direct application, fiber optics, or endoscopic methods. Additional promising modalities, such as bacteriophage therapy, metallic and metal-oxide nanoparticles, and antimicrobial peptides (AMPs), are discussed with respect to their mechanisms, advantages, and translational potential. Collectively, these strategies represent a paradigm shift from antibiotic replacement to antibiotic revival and potentiation. By integrating multidisciplinary approaches, it is possible to extend the lifespan of current antibiotics while developing more robust, resistance-resilient therapies. The evidence presented confirms that antibiotic therapy remains an essential component of antimicrobial treatment, provided that it is supported by innovative, complementary technologies. Abandoning antibiotic therapy at this stage is unwarranted. Full article

Background: Metabolic bone diseases represent prevalent global disorders characterized by disrupted bone homeostasis and chronic inflammation. This review utilizes a broad working definition encompassing osteoporosis and other classic metabolic bone disorders, including osteoarthritis, a primarily degenerative joint disease marked by significant metabolic-driven subchondral bone remodeling. Conventional diagnostic modalities prove inadequate for early-stage disease screening, while classical pharmacological interventions face limitations due to unsatisfactory targeting performance and severe long-term adverse reactions. An urgent clinical need persists for innovative diagnostic biomarkers and targeted therapeutic strategies. Exosomes, a sub-population of small extracellular vesicles (sEVs), are natural nanoscale intercellular vesicles derived from multivesicular bodies, playing critical regulatory roles in bone metabolism and offering substantial clinical translational potential. This review systematically elaborates the regulatory networks associated with exosomes, the biomarker potential of exosomal non-coding RNAs, and advanced engineering modifications for bone-targeted delivery in metabolic bone diseases. Methods: This narrative review involved a literature search conducted across PubMed, Web of Science, Scopus, and Embase from inception to December 2025, focusing on exosome biology, metabolic bone disorders, non-coding RNA biomarkers, and engineered bone-targeted delivery. Inclusion criteria encompassed peer-reviewed English original and review articles, while conference abstracts, case reports, editorials, and non-English literature were excluded. Eighty-two eligible studies underwent thematic analysis without a formal systematic search protocol or preregistration. Results: Exosomes orchestrate osteogenic and osteoclastogenic processes through established bone-metabolic signaling cascades. Exosomal microRNAs and circular RNAs demonstrate promising diagnostic performance in exploratory observational cohorts for early-stage screening and prognostic assessment of metabolic bone diseases; however, large-scale multi-center clinical validation remains insufficient. Engineered exosomes facilitate precise local bone intervention in preclinical animal models, overcoming significant drawbacks of conventional therapies in experimental settings. Nevertheless, clinical translation is obstructed by non-standardized production workflows and unresolved biosafety risks. Conclusions: Exosomes present promising dual diagnostic and therapeutic potential to enhance precision and individualized management of metabolic bone diseases. Further technical refinement in exosome preparation and modification will expedite orthopedic clinical translation and improve patient outcomes in metabolic bone disorders. Full article

Dermatology has historically evolved from the observation of visible skin lesions to a complex medical specialty shaped by scientific, institutional, technological, and social transformations. This article aims to analyze the historical trajectory of dermatology through the milestones that transformed practices of care, considering the relationships between skin, subject, environment, and care. This is a historical–reflective study based on a narrative literature review conducted in PubMed/MEDLINE, SciELO, LILACS, and Google Scholar, complemented by primary treatises and institutional documentary sources, covering indexed publications issued between 1950 and 2026 in English, Portuguese and Spanish, addressing the development of dermatological knowledge from ancient civilizations to contemporary practice. The analytical scope was deliberately delimited to the European and Brazilian trajectories, and other traditions are referred to only where they establish contrast. The analysis shows that dermatology was progressively structured through clinical observation, morphological classification, standardized terminology, clinicopathological correlation, hospital-based teaching, scientific societies, specialized journals, and technological innovations such as medical photography, dermoscopy, non-invasive imaging techniques, teledermatology, and artificial intelligence. In Brazil, the specialty developed through the expansion of medical education, specialized services, scientific production, professional certification, continuing education, and public health campaigns. From a systemic perspective, these milestones reveal a shift from a mechanistic view centered on cutaneous disease to a broader understanding of the individual within biological, subjective, social, environmental, technological, and healthcare contexts. This trajectory was, however, neither linear nor uniformly beneficial: stigmatization, unequal access, and dependence on technology accompanied it and persist in certain contexts. Dermatology’s history is therefore not only a history of diseases and techniques, but also of changing ways of observing, interpreting, and caring. Full article

Forest-based innovation is increasingly important for Europe’s transition towards a sustainable and circular bioeconomy, yet the extent to which macro-regional research and innovation priorities in Central-Eastern and South-Eastern Europe (CEE/SEE) align with broader European agendas remains insufficiently understood. This study examines strategic alignment and perceived implementation capacity by comparing the forestry-related priorities of the BIOEAST Strategic Research and Innovation Agenda (SRIA) with the pan-European EUFORE SRIA and complementing this document analysis with semi-structured expert interviews conducted in Slovakia, Croatia and Czechia. Strategic priorities were compared using a structured qualitative alignment matrix, while interview data were analysed thematically to identify perceived innovation priorities, barriers, opportunities, and future needs. The comparison reveals substantial strategic alignment, particularly in forest resilience, sustainable forest management, wood value chains, circular bioeconomy and knowledge transfer. However, the interviews identify persistent constraints related to administrative complexity, fragmented support systems, weak innovation culture, technology and knowledge transfer, and the limited implementation capacity of some actor groups, particularly smaller businesses and private forest owners. The findings therefore suggest that the principal challenge is less a conflict between strategic priorities than a gap in perceived implementation capacity. Strengthening macro-regional coordination, innovation-support mechanisms, knowledge transfer, and actor-specific capacity may facilitate more effective participation of the three studied national contexts in European forest research and innovation. Full article

This article examines how health data, system interoperability and Artificial Intelligence (AI) can help reduce regional inequalities in healthcare systems, with particular attention to the Portuguese National Health Service (NHS). Methodologically, the study adopts a qualitative conceptual synthesis of purposively selected academic literature and authoritative policy and regulatory sources across three intersecting domains: territorial inequalities in health systems; digital health, interoperability and data governance; and AI and health equity. The synthesis identifies recurring mechanisms linking territorial disparities to data fragmentation, institutional capacity and AI adoption, which inform the authors’ synthesis tables and five-layer systemic framework. The evidence shows that disparities in access, service availability, coordination and outcomes remain structurally embedded across Europe and persist in Portugal despite universal coverage. The study argues that data infrastructures and interoperability are not merely technical enablers, but core determinants of governance capacity and equity. It situates this argument within the European Health Data Space (EHDS) and HealthData@PT, distinguishing between primary use of health data for care delivery and secondary use for research, innovation, policy evaluation and system learning. The analysis highlights AI’s dual potential: poorly governed, it may reinforce inequalities through biased data, uneven adoption and institutional fragmentation; properly governed, it can support predictive planning, coordination, waiting-list management, equitable triage, value-based outcome measurement and disparity monitoring. The proposed framework integrates territorial equity governance, data infrastructure and interoperability, analytical and AI capability, organisational integration, and Continuous Evaluation and Equity Monitoring. The study concludes that digital transformation may contribute to reducing regional inequalities, but such effects should not be assumed: they depend on alignment with territorial need, equity-oriented governance, secure interoperable infrastructures, transparency, citizen trust and institutional capacity, and require empirical evaluation. Full article

Glioblastoma multiforme (GBM) is an extremely aggressive and lethal brain tumor, characterized by marked molecular heterogeneity, the persistence of glioma stem cells (GSCs), and the limited permeability of the blood–brain barrier (BBB), which collectively hinder therapeutic efficacy. To address these barriers, nucleic acid aptamers, short single-stranded oligonucleotides with high affinity and specificity for molecular targets, have emerged as a promising therapeutic platform. Early unmodified aptamers, such as AS1411 and U2, demonstrated target engagement but showed limited performance due to instability and rapid systemic clearance. Chemical modifications, including 2′-fluoro substitutions and PEGylation, resulted in improved stability, specificity, and pharmacokinetic properties, enabling the development of innovative aptamer drug conjugates (ApDCs) for targeted delivery to GBM cells. In parallel, multivalent aptamer architectures, such as bispecific aptamer targeting entities (BATEs) and aptamer guided nanostructures, have been designed to enhance binding avidity, address tumor heterogeneity, and facilitate BBB transcytosis. More recently, computational strategies ranging from machine learning-guided sequence optimization to structure prediction and generative AI have accelerated the rational design of aptamers tailored to GBM specific challenges. This review examines these advances, the remaining pharmacological limitations, and the potential of computational tools to reshape the future of aptamer based GBM therapeutics. Full article

The Internet of Things has increasingly enabled advancing real-time environmental monitoring through the integration of Low-Power Wide-Area Networks. Among these technologies, LoRa (Long Range) has emerged as a prominent communication technology due to its combination of long-range communication, low energy consumption, and affordability, making it particularly suitable for remote and infrastructure-limited environments. Its adaptability is further enhanced through the use of open-source hardware, renewable energy sources, and intelligent algorithms. Despite LoRa’s limitations in bandwidth and data rate, recent innovations enabled increasingly data-intensive applications, including image transmission. This review critically examines recent advances in image transmission over LoRa networks, synthesising approaches across four interconnected strategies: image compression, packetisation and reliability, communication optimisation, and application-specific techniques. The analysis evaluates trade-offs among image size, transmission latency, energy consumption, coverage, and reconstructed image quality. These considerations are particularly relevant for environmental sensing applications, including water quality assessment, air pollution monitoring, wildlife tracking, and underground mining. This review synthesises recent advances in LoRa-based environmental and visual sensing and highlights persistent challenges, including duty-cycle restrictions, limited throughput, and energy constraints, that must be addressed for broader adoption in data-intensive sensing applications. By analysing current strategies and proposing future directions, including adaptive encoding, lightweight encryption, and energy-aware scheduling, the review demonstrates the potential of LoRa to play an increasingly important role in enabling sustainable, scalable, and accessible Internet of Things solutions across diverse environmental settings. Full article

Background: Adolescents and young adults in Jordan experience high levels of psychological distress amid persistent stigma, limited mental health literacy, and shortages of specialist services. Although task-shared interventions have expanded access, their dependence on in-person delivery and sustained human resources constrains scalability. Guided digital interventions may extend reach, but evidence regarding their system integration and sustainability in low- and middle-income countries remains limited. Purpose: This study explored how the WHO-developed Scalable Technology for Adolescents and youth to Reduce Stress (STARS), a non-generative, rule-based chatbot intervention supported by trained e-helpers, was experienced and implemented in Jordan. It also examined the system-level tensions shaping its potential for sustainable scale-up. Methods: We conducted a qualitative multiple-case study nested within a randomized controlled trial of STARS in Jordan. Semi-structured interviews were conducted with 21 individuals, including STARS completers and non-completers, trained non-specialist e-helpers, a clinical supervisor, and policy and implementation stakeholders. Data were analyzed using a hybrid approach combining inductive thematic analysis with deductive coding informed by a system-innovation perspective and the culture–structure–practice framework. Results: Participants described STARS as culturally resonant, accessible, and adaptable to everyday life. Four recurring system-level tensions emerged: individualized support versus standardized protocols; privacy and anonymity versus institutional and parental trust; non-specialist support versus expectations for professionally qualified providers; and innovation within research settings versus integration into routine service systems. These tensions reflected competing priorities shaping implementation and perceptions of sustainable scale-up. Conclusions: STARS shows promise as a scalable guided digital mental health intervention for young people in Jordan. However, scale-up will require navigating recurring tensions across cultural, structural, and practice domains rather than addressing implementation barriers in isolation. This will require policy alignment, sustained investment in digital infrastructure and supervision, and cross-sector collaboration. Full article

Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm⁻³ in the 1970s to 1.8–2.5 kWhm⁻³ today. However, fouling caused by organics, inorganics, microorganisms, and colloids remains the major operational challenge, accounting for ≈25% of RO costs and over USD 15 billion annually. This review synthesises fouling mechanisms and mitigation strategies in renewable energy (RE)-driven RO wastewater-treatment systems, where intermittency exacerbates fouling through start–stop cycles and low-shear conditions. Analysis of recent literature highlights that mixed fouling reduces flux by 10–30%, increases transmembrane pressure, and deteriorates permeate quality. Advances in pretreatment (coagulation, MF/UF), antifouling membranes (hydrophilic coatings, zwitterionic surfaces), and cleaning protocols (osmotic backwashing, nanobubbles) have improved performance, yet complete prevention remains elusive. Persistent gaps include predictive fouling models, standardised performance metrics, and scalable green chemistries for silica and combined fouling control. Future directions emphasise integrated solutions combining advanced materials, AI-driven monitoring, and renewable-aware operational strategies, alongside circular economy approaches for brine valorisation. These innovations are critical for achieving sustainable, low-carbon RO systems for global water security. Full article

The rapid expansion of artificial intelligence is tightening the interdependence between digital infrastructure and energy systems, making the linkages between technology and energy increasingly important for understanding energy security. This study examines how major events reshape risk spillovers between China’s technology and energy sectors, with particular attention to the asymmetric effects of policy shocks, external technological shocks, and domestic technological breakthroughs. Focusing on four events, namely the announcement of the carbon peaking and carbon neutrality goals, the launch of the East Data and West Computing project, the release of ChatGPT 3.5, and the release of DeepSeek R1, the study explores whether different shocks alter risk transmission between sectors in distinct ways. Using interval data for 16 representative listed firms from 2021 to 2025, the analysis combines firm-level spillover networks with sector-level robustness tests. The results suggest that policy-driven events are associated with stronger systemic connectedness and changes in the direction of predictive spillovers between the technology and energy sectors, although the Dual Carbon result is sensitive to the selected event window, whereas ChatGPT 3.5 is associated with short-term risk decoupling under the baseline specification, although this result also shows some sensitivity to the event horizon. DeepSeek R1 shows the largest baseline increase in overall spillovers and a relatively stable positive pattern across alternative event windows. The precision of the estimated changes also varies across events. The findings suggest that domestic advances in large language models are not only innovation milestones, but also potential sources of persistent pressure on energy security. Full article

This study examines the dynamic interactions among gross domestic product (GDP), external public debt, and domestic public debt in Iraq and discusses their implications for fiscal sustainability. The observed annual data cover the period 2005–2024, while the values for 2025 are based on IMF projections. Because continuous official monthly observations were unavailable, the annual series were temporally disaggregated using cubic-spline interpolation to construct an analytical monthly series. These interpolated values do not represent additional independent observations; therefore, the empirical findings are interpreted as exploratory. A recursive Structural Vector Autoregression (SVAR) model was estimated using Cholesky identification with the ordering GDP, external debt, and domestic debt. The variables were transformed into second differences in their natural logarithms, and a three-lag specification was employed. Impulse-response functions and forecast-error variance decomposition were used to examine the transmission and relative importance of the identified innovations. At the 24-month forecast horizon, GDP shocks explained 93.3% of GDP variation, while external debt was predominantly explained by its own shocks (86.3%). Domestic-debt variation was explained by its own shocks (47.3%), GDP shocks (44.1%), and external-debt shocks (8.6%). These results suggest that domestic debt is more closely associated with changes in domestic economic activity, whereas external debt follows a comparatively persistent path. The findings emphasize the importance of debt composition, non-oil revenue diversification, expenditure management, and coordination between domestic and external borrowing. Because oil revenues and government expenditure are not included as separate endogenous variables, the model does not directly identify oil-revenue or government-spending shocks. Future research should employ genuinely observed quarterly or monthly data and incorporate these fiscal variables explicitly. Full article