The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been thought as a universal dogma controlling developmental and pathological processes. More than half a century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. Moreover, EMT and MET effects should not be a cause, but rather a consequence of or an accompanying effect during developmental and pathological processes. EMT and MET represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

Peritoneal dialysis (PD) is a home-based efficacious modality for replacement of renal function in end-stage kidney failure patients but is still under-prescribed. A major limitation is the durability of the dialytic technique. Continuous exposure of the peritoneum to bioincompatible conventional glucose-based solutions is thought to be the main cause of long-term morpho-functional peritoneal changes eventually resulting in ultrafiltration failure. Poor PD solution biocompatibility is primarily related to the high glucose content, which is not only detrimental to the peritoneal membrane but has many potential metabolic side effects. To improve the clinical outcome and prolong the survival of the treatment, PD-related bioincompatibility urgently needs to be overcome. However, combining dialytic and osmotic efficacy with a satisfactory biocompatible profile proves quite difficult. New approaches targeting the composition of the PD solution include replacement of glucose with other osmotic agents and the addition of cytoprotective or osmo-metabolic compounds. Other strategies include infusion of mesenchymal cells or the administration of orally active agents. In the present article we review the current evidence on efforts to improve the biocompatible and functional performance of PD, focusing on studies performed in vivo (animal models of PD, human subjects on PD).

COVID-19 infection, caused by SARS-CoV-2, is inequitably distributed and more lethal among populations with lower socioeconomic status. Direct contact with contaminated surfaces has been one of the virus sources, as it remains infective up to days. Several disinfectants have been shown to inactivate SARS-CoV-2 but they rapidly evaporate, are flammable or toxic and may be scarce or inexistent for the vulnerable populations. Therefore, we are proposing a simple, easy to prepare, low-cost and efficient antiviral films, made with wide available dishwasher detergent, which can be spread in hands and inanimate surfaces and maintains virucidal activity for longer periods than the current sanitizers. Avian coronavirus (ACoV) was used as model of challenge to test the antivirus efficacy of proposed films. Polystyrene microplates were covered with a thin layer of detergent formula. After drying, the films were exposed to different virus doses for 10 minutes and virus infectivity were determined using embryonated chicken eggs and RNA virus quantification in allantoic fluids by RT-qPCR. The films showed to inactive the ACoV (ranging from 103.66 to 106.66 EID50), which is chemically and morphologically similar to SARSCoV-2 and may constitute an excellent alternative to minimize the spread of Covid-19.

Motivation: there is a paradox at the heart of informatics where practical implementation generally fails to understand the socio-technical impact of novel technologies and disruptive innovation when adopted in `real-world’ systems. This phenomenon, termed technological determinism, is manifested in a time-lag between the adoption of novel technologies and an understanding of the underlying theory which develops following research into their adoption. Methods: we consider informatics theory as it relates to: social informatics and how humans’ function in society, the relationship between society and technology, information systems, information systems design, and human-computer interactions. The challenges posed by novel technologies and disruptive innovation are considered as they relate to information systems and information systems design. Open research questions with directions for future research are discussed with an introduction to and our proposed approach to socio-technical information system design. Significance: we conclude that the adoption of disruptive innovation presents both opportunities and threats for all stakeholders in computerised systems. However, determinism is a topic requiring research to generate a suitable level of understanding and technological determinism remains a significant challenge.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Cystic echinococcosis (CE), caused by the larval stage of Echinococcus granulosus, is significantly underreported in Mongolia due to geographical remoteness, lack of early diagnostics, and poor clinical management. This study aimed to provide a more accurate estimate of CE in Mongolia by comparing data of surgical (reported) and diagnosed (unreported) cases, and assessing the challenges faced by rural doctors in disease management and surveillance. We collected data on surgical cases hospitalized between 2006-2016 and newly diagnosed CE cases in 2016 from eight provinces. Using a quasi-Poisson regression model of the collected data, we used extrapolation to estimate the number of diagnosed cases for the entire country. Additionally, fourty health professionals from all 21 provinces rated local clinical management for CE through a questionnaire. Results reveal that surgical cases (2.2 per year) represent only one-eighth of diagnosed cases (15.9 per year). The laboratory facilities, disease reporting, and cyst classification usage scored below 2. These results highlight the significant underreporting of CE in Mongolia and urges human and animal health experts, along with policymakers, to invest into combating CE, particularly in remote provincial areas. It also emphasizes the need for standard clinical management involving cyst classification according to WHO-IWGE, seamless integration of CE reporting and monitoring mechanisms, which can significantly contribute to the national and global burden estimation of CE.

Over the past decade, our knowledge of the $\gamma$-ray sky has been revolutionized by ground- and space-based observatories by detecting photons up to several hundreds of tera-electron volt (TeV) energies. A major population of the $\gamma$-ray bright objects are active galactic nuclei (AGN) with their relativistic jets pointed along our line-of-sight. Gamma-ray emission is also detected from nearby mis-aligned AGN such as radio galaxies. While the TeV-detected radio galaxies ($TeVRad$) only form a small fraction of the $\gamma$-ray detected AGN, their multi-wavelength study offers a unique opportunity to probe and pinpoint the high-energy emission processes and sites. Even in the absence of substantial Doppler beaming $TeVRad$ are extremely bright objects in the TeV sky (luminosities detected up to $10^{45}~erg~s^{-1}$), and exhibit flux variations on timescales shorter than the event-horizon scales (flux doubling timescale less than 5 minutes). Thanks to the recent advancement in the imaging capabilities of high-resolution radio interferometry (millimeter very long baseline interferometry, mm-VLBI), one can probe the scales down to less than 10 gravitational radii in $TeVRad$, making it possible not only to test jet launching models but also to pinpoint the high-energy emission sites and to unravel the emission mechanisms. This review provides an overview of the high-energy observations of $TeVRad$ with a focus on the emitting sites and radiation processes. Some recent approaches in simulations are also sketched. Observations by the near-future facilities like Cherenkov Telescope Array, short millimeter-VLBI, and high-energy polarimetry instruments will be crucial for discriminating the competing high-energy emission models.

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented with the aim of highlighting strengths and deficiencies of current Artificial Intelligence approaches while sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities.

The increasing adoption of student-centred learning environs by the institutions of higher education in business, has led to new mechanisms and methodologies such as the Simulation-Based Learning (SBL), Problem-Based Learning (PBL) and Challenge-Based Learning (CBL). Literature has shown evidence of raising interest in these methodologies towards a more experiential paradigm with regard to upcoming events from the surrounding real-world context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the integration and application of these innovative and collaborative methodologies in higher education. It aimed to identify its relevant advantages, challenges, and consequences for the future of business education. Bibliographic databases were searched for documents published up to May 2024 to categorize key topics discussed in the literature on SBL, PBL and CBL. The review process identified 89 empirical and non-empirical papers on SBL, PBL and CBL, business related paperwork, which resulted from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. PRISMA is a framework with evidence-based data designed to help authors perform a systematic literature review. Data analysis revealed diverse subthemes for those methodologies uses in business higher education institutions. The research questions “What are the advantages, challenges and implications of SBL, PBL and CBL altogether, in the training of business students?” guided the study. The central output of the research is the discovery of the varying uses of SBL, PBL and CBL overall, to cope with diverse demands arising within environmental context. Future research directions are suggested.

Thanks to its valuable assessment possibilities (subjective complaints and changes in nasal patency in the course of the examination), nasal provocation test may serve as an alternative tool for oral food challenges in the future. However, this test requires successive attempts to regulate its methodology in order to develop a standardized lyophilisate form and determine the threshold dose for a positive result. The study objective was to present the methodological foundation for nasal food allergen provocation tests induced by freeze-dried powdered chicken egg white. A control group of 25 individuals with no history of allergy to chicken eggs or any other allergy was included in the study. Optical rhinometry and visual analog scale were used to assess the response of nasal mucosa to local allergen challenge. Minor variations in nasal flows as measured by optical rhinometry were observed in the provocation tests. The mean optical density measurements (as measured regardless of the allergen dose used) varied from positive to negative values and vice versa; e.g., amounting to 0.018 OD (standard deviation 0,095) at 15 minutes and −0.011 OD (standard deviation 0,090) at 30 minutes. No significant differences were observed with regard to the perceived nasal discomfort using the visual analog scale. Due to the absence of nasal mucosal reactivity, nasal challenge is an excellent methodological tool for implementing food allergen tests.

In recent decades, there has been a startling rise in the number of cancer patients worldwide, which has led to an amazing upsurge in the development of novel anticancer treatment candidates. On a positive note, arylpiperazines have garnered attention in cancer research due to their potential as scaffolds for developing anticancer agents. These compounds exhibit a diverse array of biological activities, including cytotoxic effects against cancer cells. Indeed, one of the key advantages of aryl piperazines lies in their ability to interact with various molecular targets implicated in cancer pathogenesis. Here, we focus on the chemical structures of several arylpiperazine derivatives, highlighting their anti-proliferative activity in different tumor cell lines. The modular structure, diverse biological activities, and potential for combination therapies of arylpiperazine com-pounds make them valuable candidates for further preclinical and clinical investigations in the fight against cancer. This review, providing a careful analysis of different arylpiperazine deriva-tives and their biological applications, allows researchers to refine the chemical structures to improve potency, selectivity, and pharmacokinetic properties, thus advancing their therapeutic potential in oncology.

Abstract: Background: The optimal strategy for the microelimination of HCV within community settings remains ambiguous. We evaluated the percentage of participants who achieved linkage to care (LTC) following the conclusion of the screening campaign, and examined the diverse factors influencing LTC among these individuals. The effectiveness of recall intervention for the non-LTC population and its barriers were analyzed. Methods: We initiated an HCV patients recall program to identify the HCV participants who might not be treated after the HCV screening campaign. A program staff recalled HCV participants who lost to follow-up through the telephone from March 2019 to June 2019. They were informed of HCV treatment's importance, efficacy, availability, and safety. Result: Among 185 HCV-infected participants, 109 (58.9%) HCV-infected participants had obtained LTC. Compared with those who had LTC, those without LTC were older, lower education levels, were less aware of their HCV infection, less frequently lived in urban areas, and had less health insurance. At the end of the recall program, 125 (67.6 %) persons had liked to care. The proportion of LTC increased by 8.7 %. 119 persons had an HCV RNA test, and 82 (68.9%) had viremia. Of 82 patients with viremia, 78 (95.1%) received antiviral therapy. 76 (97.4 %) of them achieved a sustained virological response. Conclusion: After a community screening campaign, 59 % of participants with anti-HCV positive had LTC. The recall program can increase by 9 %. However, there were still 32% of HCV participants who couldn't be linked to care. Outreach care for non-LTC patients is a way worth trying to achieve micro-elimination of HCV in the rural community.

Cirrhosis is a chronic disease with multiple etiologies, multiple manifestations, and multi-organ involvement. Affects about 200 million people worldwide. Its main complication is Portal hypertension (PHT). Cirrhotic cardiomyopathy is a consequence of PHT, with heart failure (HF) being the main manifestation. On the other hand, for people with cardiopathy due to other reasons beyond cirrhosis, the presence of the last could act as a trigger to acute HF. Finally, when HF and cirrhosis coexist, the renal system suffers the most, originating from type-5 cardio-renal syndrome, a complex entity with many challenges in diagnosis and treatment. This entity was recently defined and there is a lack of data on the epidemiology of this syndrome. A 77-year-old woman, with cardiovascular risk factors; heart failure with reduced ejection fraction (HFrEF), secondary ischemic and valvular cardiopathy, chronic Kidney disease (CKD), and stable decompensated cirrhosis, went to the emergency department after progressive abdominal perimeter augmentation, legs’ edema and hematochezia. It was assumed acute decompensated cirrhosis and acute decompensated chronic kidney disease, had triggered digestive bleeding and the patient stayed hospitalized. Intravenous diuretic therapy was started but, the ascites and kidney function got worse, the Nt pro-BNP grew until 1125000 pg/ml, and a transthoracic echocardiography showed important signs of congestion, isolated right ventricular dysfunction, with normal cardiac output. After teamwork’s discussion, a type-5 cardio-renal syndrome was assumed deciding not to act on tricuspid regurgitation due to the maladaptive RV remodeling. Hepatorenal syndrome treatment was begun (oral terlipressin and intravenous albumin), followed by decongestion therapy, resulting in a marked improvement in clinical presentation. The kidney, heart, and liver recovered to these basal states. With this clinical case, the authors want to show how multidisciplinary management is important to face tricuspid regurgitation and right ventricular dysfunction.

A patient in her early 20s presented with constant and progressive lower abdominal and back pain, mainly on the right side of the abdomen, purulent vaginal discharge and pyrexia. Radio-logical assessment revealed a possible tuboovarian abscess and incidental diagnosis of ipsilateral renal agenesis. The patient was treated for pelvic inflammatory disease (PID), however after antibiotic administration and since the symptoms were not resolving, an abdominal MRI was requested which revealed a uterus didelphys with two cervices, an obstructed haemivagina and evidence of haematocolpos. The diagnosis of OHVIRA syndrome was confirmed and the patient underwent an excision of the vaginal septum, drainage of the haematopyocolpos and laparoscopic drainage of the tuboovarian abscess. She achieved a good recovery.

The quantum Randi challenge is an invitation to violate Bell’s inequality with a programmable (transparent and reproducible) sequence of local events. This is widely perceived as impossible, given Bell’s Theorem. However, mathematical proofs can be correct without universal validity, as seen in the case of Euclidean geometry. Is Bell’s Locality Criterion inclusive enough to account for all the local phenomena? It is well known that Bell-type inequalities apply to jointly distributed variables, meaning that they hold for events that can be recorded at the same time. Yet, some physical qualities are mutually exclusive. They only become observable at different times. Accordingly, pairwise detections in a narrow window of coincidence – but not at the same exact time – allow for maximal Bell violations (S=4 for the CHSH inequality), without room for doubt about the local nature of the process.

The impact that new technologies have on all aspects of our lives, work, and businesses is significant and growing over time. Blockchain and artificial intelligence, among other innovative technologies, are having a profound effect on practically all business functions, including most human resources (HR) tasks. The HR sector is currently facing a variety of challenges as HR departments invest many hours in vetting candidates' applications and authenticating records to decrease the chances of imperfect recruiting. Nowadays, more technologically based processes are utilised for talent search, selection and management, as well as for accumulating, retaining, and integrating new talents into a company. These HR practices are far more effective compared to traditional methods but can also be more expensive if the transactional costs are considered. This paper consists of two parts. Following an introduction, the first part discusses the trust and confidentiality that can be attained, while the second part examines security, fraud prevention and the productivity gained from using blockchain technology in HR activities. Finally, there is a concluding section, discussing the implications of blockchain technology for firms who choose to employ it.

Parkinson's disease (PD) is a degenerative disease that affects approximately 6.1 million and is primarily caused by the loss of dopaminergic neurons. Naphthoquinones have several biological activities explored in the literature, including neuroprotective effects. Therefore, this review shows an overview of natural and synthetic naphthoquinones with neuroprotective effects and with potential for the development of molecules for the treatment of PD. Many examples of natural naphthoquinones, such as shikonin, plumbagin and vitamin K, exerted a neuroprotective effect by preventing oxidative stress, in addition to multiple mechanisms. Synthetic naphthoquinones with inhibitory activity on the P2X7 receptor were also found, leading to a neuroprotective effect on Neuro-2a cells. It was found that naphthazarin can act as inhibitors of the MAO-B enzyme. Vitamin K and synthetic naphthoquinones hybrids with tryptophan or dopamine showed inhibition of the aggregation of α-synuclein. A library of synthetic derivatives of juglone and naphthazarin were able to protect Neuro-2a cells against neurodegenerative effects of neurotoxins. We report many examples of naphthoquinones that act in therapeutic pathways for the treatment of PD. In addition, routes for producing synthetic derivatives were also discussed. With the data presented, 1,4-naphthoquinones can be considered a promising class in the treatment of PD.

Perhaps the greatest challenge in the graft copolymerization of acrylic acid onto starch initiated by Fenton’s is the selectivity of the grafting reaction. Low selectivity means that too much of unwanted homopolymer polyacrylic acid is produced. In this paper, it has been analyzed why with this reaction system, involving a water soluble monomer and indirect initiation, the graft selectivity is such a challenge. Several other grafting reactions, especially with monomers of low water solubility or when direct initiation is applied, show a better grafting performance. A scheme is presented based on reaction engineering principles, that supports an explanation of the observed differences. A more selective activation of starch is a factor, but perhaps even more important is a low monomer to starch ratio at the starting sites of graft reactions. Based on these insights, several methods to improve the graft selectivity are evaluated, with the help of literature data but mostly by experimental explorations in our laboratory. Most promising appears to be a dedicated dosage method, to add monomer in portions over time, tested in a batch reactor with gelatinized cassava starch. This way, both grafting percentage and graft selectivity could be improved. Such could not be realized with the more conventional dosage, which is to add all monomer before the start of the reaction. This can be considered a principal breaktrough. Still, more research and development would be needed to refine the method and to implement the idea also in a continuous reactor at larger scale.

In past years, lithium-ion batteries (LIBs) can be found in every aspect of life, and batteries, as energy storage systems (ESSs), need to offer electric vehicles (EVs) more competition to be accepted in markets for automobiles. Thick electrode design could reduce the use of non-active materials in batteries that its energy density would be improved and its cost would be cut. However, thick electrodes are limited by their weak mechanical stability and poor electrochemical performance, these limitations could be classified as the critical cracking thickness (CCT) and the limited penetration depth (LPD). The understanding of the CCT and the LPD have been proposed and the recent works on breaking the CCT and improving the LPD are listed in this article. By comprising these attempts, some thick electrodes could not offer higher mass loading or higher accessible areal capacity that would defeat the purpose.

Eukaryotic plasma membrane (PM) transporters face critical challenges that are not widely present in prokaryotes. The two most important issues are proper subcellular traffic and targeting to the PM, and regulated endocytosis in response to physiological, developmental or stress signals. Sorting of transporters from their site of synthesis, the Endoplasmic Reticulum (ER), to the PM has been long thought, but not formally shown, to occur via the conventional Golgi-dependent vesicular secretory pathway. Endocytosis of specific eukaryotic transporters has been studied more systematically and shown to involve ubiquitination, internalization, and sorting to early endosomes, followed by turnover in the MVB/lysosomes/vacuole system. In specific cases internalized transporters have been shown to recycle back to the PM. However, the mechanisms of transporter forward trafficking and turnover have been overturned recently through systematic work in the model fungus Aspergillus nidulans. In this review we present evidence that shows that transporter traffic to the PM takes place through Golgi-bypass and transporter endocytosis operates via a mechanism that is distinct from that of recycling membrane cargoes essential for fungal growth. We discuss these findings in relation to adaptation to challenges imposed by cell polarity in fungi as well as in other eukaryotes and provide a rationale why transporters and possibly other housekeeping membrane proteins ‘avoid’ routes of polar trafficking.

The term "sustainable urbanism" has been criticized over its inconsistent and imprecise definition, leading to challenges in implementing actionable reforms and achieving urban sustainability goals. A clearer approach may be to identify its opposite: specifically, forms of urbanism that cause an unacceptable buildup of toxic or climate-altering emissions, deplete resources beyond sustainable levels, progressively destroy critical ecologies, and cause other identifiable sources of potentially catastrophic harm to human and urban welfare. Here we present a model of such an “unsustainable urbanism,” and we observe further that it is in fact the dominant model of urban structure to this day. Its features include an over-reliance on low-occupancy vehicular transport; inefficient envelope, size, orientation and adaptability of buildings; ecologically destructive infrastructure systems for handling water, energy and other resources; and – under-appreciated but fundamentally important, as we will explore – decline of a well-connected, walkable, functionally and aesthetically appealing public realm. This model remains dominant in spite of the many goals, agenda and declarations on sustainable urbanism at the highest policy level. We observe that the lack of progress is in large part the result of system “lock-in” – economic and professional incentives and disincentives, standards, laws, codes, and other forms of feedback that reinforce “business as usual,” and create barriers to reform. Therefore, the agenda ahead must address the specific levers of change to overcome this systemic lock-in, drawing insights from economics, technology and the social sciences to do so. We present the outlines of this agenda, and make conclusions for needed steps ahead.

Tick-borne diseases are among the most common vector-borne diseases. These include various infections caused by viruses, bacteria, rickettsiae, spirochetes and protozoa. The aim of our review is to present the relationship between the clinico-epidemiological aspect of some of the emergency tick-borne diseases and their diagnosis, treatment and prevention. We will review three diseases- anaplasmosis, babesiosis and ehrlichiosis. Anaplasmosis and ehrlichiosis are caused by small gram-negative obligate intracellular alphaproteobacteria in the order Rickettsiales, family Anaplasmataceae. The prognosis of disease depends on early diagnosis and proper treatment. The symptoms of anaplasmosis largely overlap with those of ehrlichiosis. Babesiosis is a malaria-like disease that is caused by intraerythrocytic protozoa. Like malaria, the disease proceeds with hemolysis and its severity is determined by the parasitemia. Treatment of babesiosis involves the use of antimalarial agents and antibiotics, most commonly in a combination. Understanding the clinical, epidemiological and laboratory characteristics of tick-borne diseases is crucial for their prevention, diagnosis and treatment.

Within the oligometastatic state, oligo-recurrent lymph node disease in prostate cancer represents an interesting clinical entity characterized by a relatively indolent biology that makes it unique: it can be treated radically, and its treatment is usually associated with a long period of control and excellent survival. Additionally it is an emergent situation that we are facing more frequently, mainly due to: a) the incorporation into clinical practice of the PSMA PET that provides strikingly increase superior images in comparison to conventional imaging, with higher sensitivity and specificity; b) the higher detection rates of bone and node disease with extremely low levels of PSA; and c) the availability of high-precision technology in radiotherapy treatments with the incorporation of stereotaxic body radiotherapy (SBRT) or stereotaxic ablative radiotherapy (SABR) technology that allows the safe administration of high doses of radiation in a very limited number of fractions with low toxicity and excellent tolerance. This approach of new image-guided patient management is compelling for doctors and patients since it can potentially contribute to improve the clinical outcome. In this work we discuss the available evidence, areas of debate, and potential future directions concerning the utilization of new imaging guided SBRT for the treatment of nodal recurrence in prostate cancer.

This article works on the three questions: “How can libraries make an effective contribution to resolving the sustainability challenges we are collectively facing?”; “When are libraries truly sustainable?”; and “How can library management support this shift?”. Looking across libraries and their history of the last decades, the author discerns different stages of development leading to sustainability. In line with the work of Dyllick and Muff (2016) the author describes Sustainability Levels 0.0 to 3.0. The highest level requires a quantum leap and shifts from thinking inside-out to outside-in. This article addresses the need that there is virtually no academic management literature on the topic of sustainability in libraries. It shows that whilst there are many examples of individual projects or activities, there is a serious lack of methodology senior management level.

Caesarean scar ectopic pregnancy (CSEP) is an uncommon form of ectopic pregnancy. This review defines types of CSEP, discusses pathophysiology, clinical presentation and diagnosis and compares common treatment options and outcomes. The pathophysiology is not yet fully understood. One third of patient are asymptomatic. In symptomatic patients vaginal bleeding and pelvic pain which is usually misdiagnosed as threatened or missed abortion. The main principles of treatment for CSEP are early diagnose, quick management, and maintain reproductive function as much as possible. Up to date; no consensus or guidelines for the treatment and management of CSEP. Management of CSEP depends on the clinical presentation and gestational age. Significant controversy exists regarding management, for this reason, several factors should be considered including age of the patient, myometrial thickness, clinical symptoms, hemodynamic status, fertility preservation.

The metastatic disease is a complex and sequential process that involves the migration of tumor cells from the primary site to distant areas. This metastatic pathway is not always predictable. Therefore, this paper presents three rare cases of unusual metastases, due to their primary site: two metastases of a clear cell renal cell carcinoma, one gingival and one nasal, as well as a mandibular metastasis of a hepatocellular carcinoma. In all cases, an incisional biopsy was performed in order to sign out the diagnosis. After microscopical examination of morphological Hematoxylin and Eosin stained slides, for all cases, immunohistochemical reactions were performed to support the primary tumor site. Two cases had a previous histopathological diagnosis of a primary tumor, while for the third case, the metastatic lesion represented the first manifestation of the neoplastic disease, with an unfavorable prognosis.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Although a slightly radioactive element, thorium is considered very toxic because its various species, which reach the environment, can constitute an important problem for the health of the population. The present paper aims to expand the possibilities of using membrane processes in the removal, recovery and recycling of thorium from industrial residues reaching the municipal waste processing platforms. The paper includes a short introduction on the interest shown for this element, a weak radioactive metal, followed by highlighting of some common (domestic) uses. In a distinct but concise part, the bio-medical impact of thorium is presented. The classic technologies for obtaining thorium are concentrated in a single schema, and then the speciation of thorium is presented with an emphasis on the formation of hydroxo-complexes and complexes with common organic reagents. The determination of thorium has been highlighted both on the basis of its radioactivity, but especially through methods that call for extraction followed by an established electrochemical, spectral or chromatographic method. Membrane processes are presented based on the electrochemical potential difference, rapidly presenting barro-membrane processes, electrodialysis, liquid membranes and hybrid processes. A separate sub-chapter is devoted to proposals and recommendations for the use of membranes in order to achieve some progress in urban mining for the valorization of thorium.

The fact that Thailand’s energy policy has set a new renewable energy target of 30% of total final energy consumption by 2036. It also has the potential of solar energy and community demands in remote areas. However, most of the renewable energy technology will still be able to achieve renewable energy goals, similar to the case of the national policy that promotes Solar Home System (SHS) in remote areas, lack of good handling. Therefore, achieving the goal of the renewable energy policy should be in position using the right strategy. This article presents the result of a case study in the Akha upland community, northern Thailand, where we used the mixing method and factor analysis to analyze strategies for SHS related criteria. The key scopes and challenge included bottom-up planning concepts and subsidies from expert persons, while contributions to factors have an impact on developing sustainable SHS, include the creating approval of SHS technologies, developing of SHS management, promoting of SHS technologies, and supporting of SHS policies, respectively. Mainly, social factors provide positive effects, which thus influence the sustainable development of process SHS in terms of the creation of approval. Furthermore, there should be managed appropriately for each community, for the positive imagery of solar power.

Despite the advancements in evidence-based medicine, many hospitals still maintain high rates of patients undergoing prolonged preoperative fasting. The goal of this study is to determine the prevalence of prolonged fasting time among patients undergoing elective surgeries at a Brazilian university hospital and its associations with clinical and sociodemographic variables. This cross-sectional study was conducted between May and November 2019 and included patients of all ages undergoing elective surgeries. Out of the 293 patients analyzed, 64.2% were male, with ages ranging from 1 to 85 years, and 93.9% were classified as ASA I or II. The prevalence of preoperative fasting exceeding 8 hours was 89.4%. An average fasting time exceeding 11 hours was observed across all age groups. Patients who underwent procedures in the afternoon had an average fasting time that was 16% longer than those in the morning period (13.84 vs. 11.92, p<0.001). Surgery time (r=0.134, p<0.03) and anesthesia times (r=0.121, p<0.04) demonstrated a weak correlation with fasting time. Despite the international consensus on preoperative fasting time, our study demonstrated low adherence to current recommendations. Therefore, healthcare professionals should ensure the comprehension of fasting guidelines, and further studies should aim to identify effective solutions to mitigate prolonged fasting.

Today, enterococci (mainly Enterococcus faecalis) are one of the main causes of infective endocarditis in the world, generally affecting an elderly and fragile population, with a high mortality rate. enterococci are intrinsically resistant to many commonly used antimicrobial agents. All enterococci exhibit decreased susceptibility to penicillin and ampicillin, as well as high-level resistance to most cephalosporins and all semi-synthetic penicillins, as the result of expression of low-affinity penicillin-binding proteins, that precludes an unacceptable number of therapeutic failures with monotherapy with these drugs. For years, the synergistic combination of penicillins and aminoglycosides was the cornerstone of treatment, but the emergence of strains with high resistance to aminoglycosides led to the search for new alternatives, such as dual beta-lactam therapy. The development of multi-drug resistant strains of Enterococcus faecium is a matter of considerable concern due to its probable spread to E. faecalis and have forced the search of new alternatives with the combination of daptomycin, fosfomycin or tigecycline. Some of them have scarce clinical experience and others are still under investigation and will be analyzed in this review. In addition, the need for prolonged treatment (6-8 weeks) to avoid relapses has led to the consideration of viable options such as outpatient parenteral therapies or long-acting administrations with the new lipoglycopeptides (dalbavancin or oritavancin), and sequential oral treatments, which will also be discussed.

Carbon capture and storage has gotten increased attention during the last decade, and several full-scale projects are currently being planned. This requires transportation of large quantities of CO2 from the capturing plant to the end point. From economic and public acceptance point of view it is important to ensure that the transportation system is operated in a safe manner. Thus, avoiding threats like corrosion or formation of particles are important. It is therefore required to monitor that the transported CO2 fulfils the required specifications, and in practice this means that the impurity content of the CO2 must be analysed. CO2 will in most cases be transported in the liquid or supercritical state (high pressure), which makes the practicalities around chemical analysis more difficult. Phase transition from liquid or supercritical state to gaseous state may also introduce several physiochemical effects that may affect the analysis. This paper discusses technical and practical challenges with such types of analysis. Most of this work is based on experience that was gained during development of analytical system for dense phase CO2 in a joint industry project that studied corrosion and chemical reactions in a simulated CO2 transport system.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Alveolar Rhabdomyosarcoma (ARMS), an invasive subtype of Rhabdomyosarcoma (RMS), is associated with chromosomal translocation events resulting in one of two oncogenic fusion genes PAX3-FOXO1 or PAX7-FOXO1. ARMS patients exhibit an overexpression of the pleiotropic cytokine Transforming growth factor beta (TGF-β). This overexpression of TGF-β1 causes an increased expression of a downstream transcription factor called SNAIL, which promotes Epithelial to Mesenchymal Transition (EMT). Overexpression of TGF-β also inhibits myogenic differentiation, making ARMS patients highly resistant to chemotherapy. In this review, we first describe different types of RMS and then focus on ARMS and the impact of TGF-β in this tumor type. We next highlight current chemotherapy strategies including a combination of FDA approved drugs vincristine, actinomycin D, and cyclophosphamide (VAC), Cabozantinib, Bortezomib, Vinorelbine, AZD 1775 and cisplatin. Lastly, we discuss chemotherapy agents that target the differentiation of tumor cells in ARMS which include all trans retinoic acid (ATRA) and 5-Azacytidine. Improving our understanding of the role of signaling pathways, such as TGF-β1, in the development of ARMS tumor cells differentiation will help inform more tailored drug administration in the future.

Historical legacy of Eastern European and Balkans’ health systems was mutually interdependent and shaped by local socioeconomic circumstances. Three distinctive systems of risk sharing and health financing to develop since the late XIX century were the Bismarck, Beveridge, and Semashko systems. Modern day healthcare systems in these countries are challenged by population ageing, accelerated innovation in medical technology, growing purchasing power and rising demand for healthcare services. Supply side changes contribute to demand side efficiency bottlenecks in financing, driving the costs of the already expensive medical care up. All of the nations have a large share of citizens experiencing difficulty with affordability and access to medical care, particularly in rural and remote areas. Network of Health technology assessment agencies have mushroomed over the past three decades. Principles of health economics theory and cost-effective resource allocation are slowly gaining ground in governing authorities’ mindset and decision-making process. For many years to come, pharmaceuticals and medical services will remain dependent on out-of-pocket spending. Currently accelerating and spreading 4.0 Industrial Revolution, together with the Belt and Road Initiative, are likely to substantially impact the further economic development of this vast region. Post-Pandemic ‘Green’ Recovery strategies adopted by many of the Eastern European governments shall also make this transition towards sustainable development more difficult and challenging given the large dependency of all these economies upon traditional carbon fuels.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Refractoriness to antiseizure medications is still a major concern in the pharmacotherapy of epilepsy. For this reason, we decided to evaluate the combination of levetiracetam and cannabidiol, administered at a subthreshold dose, to limit the possible adverse effects of this phytocannabinoid. We administered levetiracetam (300 mg/kg/day, via osmotic minipumps), cannabidiol (120 mg/kg/day, injected once a day subcutaneously), or their combination for one week in epileptic rats. Saline-treated epileptic rats were the control group. Animals were monitored with video electroencephalography the week before and after the treatment. No changes were found in the controls. Levetiracetam did not significantly reduce the total seizure number or the overall seizure duration. Still, the overall number of seizures (p&lt;0.001, Duncan’s new multiple range test) and their total duration (p&lt;0.01) increased in the week following treatment withdrawal. Cannabidiol did not change seizures when administered as a single drug. Instead, levetiracetam combined with cannabidiol resulted in a significant reduction in the overall number and duration of seizures (p&lt;0.05), when comparing values measured during treatment with both pre- and post-treatment values. These findings depended on changes in convulsive seizures, while non-convulsive seizures were stable. These results suggest cannabidiol determined a remarkable potentiation of levetiracetam antiseizure effects at a subthreshold dose.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Recent data highlight similarities between neurodegenerative diseases including PD and type 2 diabetes mellitus (T2DM) suggesting a crucial interplay between the gut–brain axis. GLP-1R agonists, known for their use in T2DM treatment, are currently extensively studied as novel PD modifying agents. For this narrative review article, we searched PubMed and Scopus databases for peer-reviewed research, review articles and clinical trials regarding GLP-1R agonists and PD, published in the English language with no time restrictions. We also screened the references of the selected articles for possible additional articles in order to include most of the key recent evidence. Our research was conducted between February 2023 and February 2024. We used the terms “GLP-1”, “GLP-1 receptor”, “GLP-1R agonists”, “PD”, “T2DM”, “insulin”, “neurodegenerative disease”, “glucose”, “animal models”, “clinical trials” in different combinations. Many data on animal models and preclinical studies show that GLP1-R agonists can restore dopamine levels, inhibit dopaminergic loss, attenuate neuronal degeneration and alleviate motor and non-motor features of PD. Evidence from clinical studies are also very promising enhancing the possibility of adding GLP1-R agonists in the current armamentarium of drugs available for PD treatment.

New insights into the causes and mechanisms of congenital heart disease (CHD) have been gained through the use of next-generation sequencing. Examination of the whole exome sequence detects detrimental gene variations modifying single or contiguous nucleotides, that are characterised as pathogenicity based on statistical assessments of families. and correlates with congenital heart disease, elevated expression during heart development, and reduction of harmful protein-coding mutations in the general population. Patients with CHD and extracardiac abnormalities enriched for gene classes meeting these criteria. CHDs and extracardiac defects, supporting a common set of pathways in the organogenesis of CHDs. Single-cell transcriptomics data reveal the expression of genes associated with CHD in specific cell lineages, and emerging evidence suggests that genetic variants disrupt multicellular genes essential for cardiogenesis. Metrics and units are being tracked in whole genome sequence studies.

Balance requires constant adjustments in muscle activation to attain force steadiness. Creating appropriate training can be challenging. The purpose of this study was to examine the effects of two weeks front squat instability training using a water-filled training tube on force steadiness during an instability challenge. Control (CON, n= 13) and experimental (EXP, n=17) subjects completed pre and post testing for EMG variability by completing one set of 10 repetitions with a stable and unstable training tube. Electrodes were placed bilaterally on the anterior deltoid, paraspinal and vastus lateralis muscles. CON subjects completed 2 weeks of training using a stable training tube, while EXP subjects trained with a water-filled instability tube. EMG data were integrated for each contraction and force steadiness computed using the natural log of coefficient of variation. CON results showed no changes in force steadiness for any condition. EXP showed significant reductions in EMG activation variability across all muscles. These results indicate a significant training effect in reducing muscle activation variability in subjects training with a water-filled instability training device. Improvements seen in these healthy subjects supports development of training implements for a more clinical population to help improve force steadiness.

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas-liquid two-phase flow drift-flux model and heat transfer model. Key features include handling heat transfer between the for-mation and the wellbore, addressing the slip relationship between gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite differ-ence method, and the highly nonlinear system is solved using the Newton-Raphson method. Numerical simulation, validation, and comparison with existing models demonstrate the en-hanced accuracy of this model. In our tests, the model achieved high accuracy in calculating bottom hole pressure and temperature, with Mean Relative Errors (MRE) significantly lower than those of other models. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geo-thermal energy development.

Crimean-Congo hemorrhagic fever virus (CCHFV) is the causative agent of a tick-borne infection with significant mortality rate of up to 40% in the endemic areas, with evidence for geographical expansion. Lacking effective therapeutics and control measures, the development of protective CCHFV vaccine remains a crucial public health task. This manuscript describes, for the first time, a Bovine herpesvirus type 4 (BoHV-4) based viral vector (BoHV4-∆TK-CCHFV-N) and its immunogenicity and protection potential in BALB/c and IFNAR^-/- mice models in comparison with Adenovirus type 5 (Ad5-N) and pCDNA3.1 myc/His A (pCD-N1), two widely used vaccine platforms. All constructs expressing viral nucleocapsid (N) protein successfully elicited cytokine and total/specific antibody responses in BALB/c mice. BoHV4-∆TK-CCHFV-N and Ad5-N constructs further produced 100% protection in IFNAR^-/- mice during CCHFV Ank-2 strain lethal challenge. Despite elevated specific antibody responses in both animal models, the produced antibodies were unable to neutralize the virus in vitro. A comparison of delivery platforms was not possible, due to similar protection rates in IFNAR^-/- mice. In conclusion, vector-based CCHFV N protein expression proved to constitute an effective approach for the vaccine development pipeline and BoHV-4 emerged as a strong alternative to previously-used virus vectors.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

Needlestick and other sharp injuries (NSIs) are critical occupational hazard for healthcare workers. Exposure to blood and body fluids through NSIs increases the risk of transmission of blood-borne pathogens among them. The objectives of this study were to estimate the one-year incidence of NSIs and investigate its associated factors among the healthcare workers in Saudi Arabia. A cross-sectional online survey was conducted between October and November 2021. A total of 361 healthcare workers participated in the survey from all over Saudi Arabia. The one-year incidence of NSIs among healthcare workers is estimated at 22.2%. More than half of the injury event (53.8%) was not reported to the authority by the healthcare worker. Incidence of NSIs was highest among the physicians (36%) and is followed by nurses (34.8%), dentists (29.2%), and medical technologists (21.1%). The likelihood of injury is higher (OR: 2.51; 95% CI: 1.04, 6.03) among the works aged 26 – 30 years compared to the 20 – 25 years age group and the workers directly deal with needles or other sharp objects while working (OR: 5.90; 2.69, 12.97). The high incidence rate of injury and low reporting rate highlight the needs of an education program targeting healthcare providers with higher risk.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman &amp; Williams, 2015; Gebhardt &amp; Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

The efficacy of an intranasal (IN) bovine respiratory syncytial virus (BRSV) administered in the face of passive immunity was assessed. Pooled colostrum was administered by intubation to fifty (50) beef-dairy crossbred calves the day they were born. The calves were transported to a research facility and were blocked by age and sex and randomly assigned into two groups, a sham-vaccinated intranasally with a placebo (sterile water) or vaccinated with a tri-valent modified live viral (MLV) vaccine. The calves were 92 days old when vaccinated (day 0). The calves were challenged by aerosolized BRSV on day 80 and day 81 as a respiratory challenge. The study was terminated on day 88. Lung lesion scores (LLS) were significantly lower for calves vaccinated with tri-valent MLV vaccine than those for calves that were sham-vaccinated. Serum neutralization (SN) antibody against BRSV in calves vaccinated with the tri-valent MLV vaccine demonstrated an anamnestic response on day 88. After challenge, the calves sham-vaccinated with the placebo lost weight while those vaccinated with the tri-valent MLV vaccine gained weight. In this study colostrum-derived antibodies did not interfere with the immune response or protection provided by one dose of the tri-valent MLV vaccine.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Assessing the quality of corn seeds necessitates evaluating their content of water, fat, protein, and starch. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using both the original spectra and the extracted characteristic wavelengths, four content detection models were constructed: PLSR, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LSSVM). The experimental results demonstrate that the DT-CARS-LSSVM model exhibits exceptional accuracy and stability in predicting these four key components, with determination coefficients of 0.9877, 0.9344, 0.9827, and 0.9592, respectively. This study not only provides a scientific basis for evaluating the quality of corn seeds but also opens up new avenues for the development of non-invasive detection technology in related fields.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

Measles is targeted for elimination since 2001, with significant reduction in cases recorded worldwide, but outbreaks occur periodically due to immunization gaps. The present study analyzes the evolution of vaccination coverage rates (VCR) in Romania, a EU country with large measles epidemics during the last two decades, including an ongoing outbreak in 2023. Vaccination against measles is included in the National Immunization Program from 1979, initially as a single dose, and since 1994, with 2 doses. The vaccine uptake was initially high, with national VCR of &gt;97%. However, from 2010 onwards, the VCR dropped and remained constantly under 90%. This decline was caused by several factors, including a decreasing number of general practitioners, discontinuities in vaccine supply, emigration, with increased numbers of children lost to follow-up and a surge in vaccine hesitancy. Additional decreases in the already low levels of VCR were recorded during the COVID-19 pandemic: in 2022 the VCR reached the lowest level in the last decade, with 83.4% for the first dose (6.6% decrease compared to 2019) and 71.4% for the second dose (4.4% decrease compared to 2019), with important subregional differences. The constant suboptimal vaccination coverage rate in Romania threatens the progress toward measles elimination.

Since the beginning of the coronavirus disease 2019 (Covid-19) pandemic, there have been multiple peaks of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus virus 2) infection, mainly due to the emergence of new variants, each with a new set of mutations in the viral genome, which have led to changes in the pathogenicity, transmissibility, and morbidity. The Omicron variant is the most recent variant of concern (VOC) to emerge and was recognized by the World Health Organization (WHO) on November 26, 2021. The Omicron lineage is phylogenetically distinct from earlier variants, including the previously dominant Delta SARS-CoV-2 variant. Previous research has reported the most common clinical manifestations of the Omicron variant to be fever, runny nose, sore throat, severe headache, and fatigue. The reverse transcription-polymerase chain reaction (RT-PCR) test, rapid antigen assays, and chest computed tomography (CT) scans can help diagnose those with the Omicron variant. Furthermore, many agents are expected to have therapeutic benefits for those infected with the Omicron variant, including TriSb92, molnupiravir, nirmatrelvir, and their combination, corticosteroids, and interleukin-6 (IL-6) receptor blockers. Despite being milder than previous variants, the Omicron variant threatens many lives, particularly among the unvaccinated, due to its higher transmissibility, pathogenicity, and infectivity. This review summarizes the essential features of the Omicron variant, including its history, genome, transmissibility, clinical manifestations, diagnosis, management, and the effectiveness of existing vaccines against this VOC.

COVID-19 has become a part of everyone's day-to-day life, since the outbreak in 2019 the novel coronavirus disease (COVID-19) has caused more than 4.5 million deaths with over 200 million cases reported globally. Currently, the number of infections and deaths are gradually lowering in different countries however the underlying challenges still exist. COVID-19 threatens human life, social functioning and development. Although numerous studies have been carried out in the past to highlight the key challenges very limited studies have been conducted from an ordinary person's viewpoint. In the fight against COVID-19, humanity has been pushed to a level which cannot be accepted where establishing that balance is a priority. This study focuses on highlighting the common issues faced by the ordinary public in the current era. Five key areas were identified to be the most essential; education, technological adaptation, transportation, mental health and gender-based violence (GBV).

Background: South Africa’s public healthcare sector is overburdened, especially its under-resourced primary healthcare delivery system. This burden could be relieved by alleviating the population’s ill-health, focusing on the social determinants of health. These include living conditions and levels of social cohesion. In an attempt to address the aforementioned ‘challenge landscape’, this article considers socio-economic empowerment of those marginalised members of society living at the base of the pyramid (BOP) to improve factors contributing to poor health. We propose that Innovation Platforms (IPs) offer opportunities to achieve this by drawing diverse stakeholders together, which should include marginalised individuals, to pool resources and knowledge and collaborate around a specific set of challenges. Method: A Grounded Theory approach is utilised to develop the framework comprising concept definition from a systemized literature review. It is evaluated through various progressive stages through three phases of evaluation: 1) the initial framework was subjected to scrutiny in a theoretical case study, 2) a first-pass semi-structured interview and later four more semi-structured interviews with subject matter experts, and 3) an instrumental case study to refine the framework and to understand its application in a particular situation (this included four stakeholder interviews and a workshop and feedback session with the project champion). Results: This article contributes to the extant literature by addressing the lack of guidance on stakeholder engagement practices critical to the proper functioning of IPs in the context of overcoming the complex challenges associated with social determinants of health. The final output of the study is a refined management tool for stakeholder engagement in IPs. The tool provides practical recommendations to support policy makers, researchers and practitioners in 1) establishing IPs, 2) identifying areas for improvement and 3) identifying reasons for an IP’s failure and lessons to learn.

The last half of the 20th century was characterized by the most impactful ecosystem changes throughout regulations and guidelines minimalizing environmental harms. Many of them emphasize the importance of enterprise sustainability for renewable energy policy. When dealing with numerous alternatives for the implementation of renewables-based policy, decision-makers need to analyze their impacts on environmental, economic, and social dimensions from a long-term perspective. The aim of this paper is to highlight a challenge to apply the multi-criteria sustainability decision-making framework (MDCA) to mainstream energy policy. The integrative framework covers an approach for the needs of analyzing factors influencing the energy technology selection. In this study, the MDCA-approach combines lifecycle-based methods, Analytical Hierarchy Process, PESTEL (Political, Economic, Social, Technological, Environment and Legal). To demonstrate the applicability and usability of the method three photovoltaic technologies are assessed. The results showed that string ribbon technology is the most sustainable along its life cycle, with a 0.503 sustainability score. The study highlighted the need to embody robust methodologies assessing sustainability-oriented technologies, not used anymore for supporting energy policies. This challenge regarding example evidence can give energy decision-makers a tool to realize their energy policy and in consequence, enhance enterprise sustainability.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

The Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is a significant secondary pest of apple trees in Northeast Asia. To better understand its population dynamics, a population model based on temperature-developmental relationships was constructed. This model includes three sub-models: spring emergence, immature stage transition, and adult oviposition. Field data were collected from sex-pheromone baited traps in apple orchards in Andong, Korea, dur-ing 2015 and 2016 to validate the model. Simulations under six pesticide-natural enemy scenari-os showed that the population size of each generation was best simulated when weighted mor-tality factors for pesticides and natural enemies were applied. Using daily temperature inputs, the model demonstrated that P. ringoniella typically undergoes five generations per year, with peak times predicted within a seven-day margin of field data. Sensitivity analyses revealed that population size was influenced by total fecundity and the larval stage model, but peak times remained consistent despite parameter changes. Higher temperatures led to earlier adult peak dates, especially in summer generations. This model serves as a fundamental tool for estimating population dynamics and abundance changes of P. ringoniella and can guide the timings of pesti-cide application. Further validation is necessary to test the model's efficacy in controlling pests in apple orchards.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

In this work model of mirror elements equilibrium of the micromechanical components is developed, the behavior analysis of the mirror element of micromechanical mirrors in case changing of control voltages of electrostatic actuators is carried out, an expression for determining the maximum value of deflection voltage at which the snap-down effect will take the following form is obtained in case of the influence of the coefficient of the electrostatic rigidity of electrostatic actuators. The developed equilibrium model of mirror elements and the obtained results of modeling can be used at design of micromechanical mirrors with internal suspensions.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Multiple inflammation-related signaling pathways can lead to ferroptosis, an iron-dependent mechanism of programmed cell death. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal or a low iron diet starting three weeks before arthritis induction. From day four after the collagen-booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. The complete blood count was determined as well as Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) in whole paw tissue by ELISA. Quantitative PCR was performed from whole paw tissue for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to com-pare markers of iron metabolism and ferroptosis as well as nonlinear regression models for longitu-dinal data for signs of arthritis. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p

During the last decade, Bangladesh has been experienced a significant climatic anomalies which lead to an increase in magnitude and frequency of various climate relate extreme events. Climate scientists substantiate that global temperature and precipitation pattern is expected to change, which may result in consequential impact on agriculture, livelihood, and ecosystem. Bangladesh is therefore likely to face substantial challenges in the coming decades. In order to adequately understand this complex, dynamic phenomena, Analyzing historical Climate change scenarios as well as projecting its future trends is a great concern for researcher. This study aims to analyzes/ historical climatic data from (1901-2020), and predict future temperature and precipitation patterns in Bangladesh using CMIP6 data. Data used in this study (Observed data is from CRU TS 4.05 and future data is from CMIP6) have been acquired from WorldClim v2.1. Various methods including correlation, regression, standard deviation, correlation matrix, percentiles, cell statistics, and IDW interpolation were performed to analyze the trends, variability and spatial patterns of temperature and rainfall. This study revealed that Over the historical study period (1901-2020) Bangladesh has been experienced a significant warming trend with an average increase in temperature 2°C and with annual decline of the total precipitation 607.26 mm along with a shift towards drier conditions despite weak correlation with hotter years. Projected climate models represents that Bangladesh minimum temperature is expected to increase from 1°C to 4.4°C as well as maximum temperatures from 1°C to 4.1°C by 2100. Moreover, projected precipitation is expected to increase by 480.38 mm, with the biggest rises during monsoon months. Regional variations in temperature and precipitation are again expected, with the Southeast (SE) likely experiencing the most significant warming and the Northeast (NE) seeing the most substantial increase in precipitation.

COVID-19 infection is extremely dangerous for persons with comorbidities such as kidney disease and diabetes. Although many people of all ages and socioeconomic backgrounds have suffered due to this pandemic, those with underlying medical conditions like kidney disease and diabetes are more vulnerable to becoming infected with COVID-19 related to weak immunity. However, the role of vaccination in the co-infection of COVID-19 with diabetics is not available in the literature. Therefore, we examine the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on its disease transmission dynamics. A mathematical modelling technique is to be employed in this work to analyse the dynamics of COVID-19 outbreaks with seven compartments, including vaccination with comorbidities such as diabetes. We also investigated analytically by displaying the solutions' existence, boundedness, positivity and sensitivity. After calculating the basic reproduction numbers, the stability analysis of model equilibrium points is also covered. If the reproduction numbers are less than unity, the disease-free equilibrium points of the model appear to be both locally and globally stable. The findings of this study are that as the vaccination rate rises, the incidence of COVID-19 and its co-infections with diabetes decreases. Effective disease treatment strategies may be based partly on the possible impact of vaccination on the co-infection of COVID-19 related to diabetic disease insights.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT) – Indonesia. The study was conducted in Central Sumba District of ENT Province. Classification and regression tree (CART) for land cover classification has been analyzed using machine learning techniques using Google Earth Engine. A survey with Focus Group Discussion (FGD) and followed with in-depth interviews were conducted for primary data collection involving a total of 871 respondents. The Socio-economic data analyzed statistics descriptively and non-parametric tests. The study showed that: 1). There has been a substantial land use change during the devo-lution era that has both positive and negative implications for food security and envi-ronmental services. 2). There has been population pressure in the fertile or agricultur-al land as the direct impact of the development of city infrastructures; and 3). Nation-al intervention through the Food Estate program has fostered and shaped land use change and land management in Central Sumba District. The study highlights the importance that the devolution spirit should help to well manage the limited ara-ble/agricultural land in dominated semi-arid to ensure food security and environment services.

This study aims to evaluate the effectiveness of inactivating Salmonella enteritidis in fresh chicken breast by irradiation using a combination of short-wave UV (0, 3, 6, 9, 12 and 15 J / cm2) and a natural antimicrobial such as caffeine (0, 5, 10, 15 and 20 nM/g) as alternative proposals to conventional techniques to reduce pathogens in food. The temperatures used were from 2 to 22°C. The most suitable models were double Weibull in 60% of cases with an adjustment of R2 0.9903-0.9553 and Weibull + cola, in 46.67%, with an adjustment of R2 of 0.9998-0.9981. Noting that the most effective combination in the reduction of Salmonella was 12 J/cm2 of UV light and 15 nM/g of caffeine, with a reduction of 6 CFU/g and an inactivation rate of 0.72. The synergistic effect was observed by increasing caffeine and UV light. Furthermore, the physic-chemical characteristics of the food matrix were not affected by the combination of both technologies. Therefore, these results suggest that this combination can be used in the food industry to effectively inactivate Salmonella enteritidis, without deteriorating product quality.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Periodontitis is a chronic inflammatory disease which results in the destruction of the tooth’ s supporting tissues and the alveolar bone resorption. The complement becomes a major link between infection and inflammatory pathology including periodontitis. Gingipians as important virulence factors of P. gingivalis have the activity of C5 convertase, could cleave C5 into fully functional C5a to activate C5aR. The above process could be blocked by the C5aR antagonist (PMX-53) to suppress local periodontal inflammation, and then achieves the purpose of treatment of periodontitis. Nanoparticles incorporated within gelatin are promising carrier system for drug delivery in recent years. This study aimed to investigate whether gelatin nanoparticles loaded with PMX-53 prevents alveolar bone resorption in miniature swines model of periodontitis. Four miniature swines were placed ligatures around the maxillary and mandibular fourth premolar and first molar on both sides for seven weeks to induce periodontitis. Then, animals were assigned randomly to four groups: minocycline-treated group, gelatin with PMX53-treated group, gelatin-treated group and a sham control group. They were treated with 1ml related drugs respectively, into gingival sulcusl for 4 times at one-week intervals. We showed that local treatments with gelatin nanoparticles loaded with PMX-53 could inhibit alveolar bone loss of periodontitis. Our study revealed that gelatin nanoparticles loaded with PMX-53 prevented alveolar bone resorption miniature swines model of periodontitis. In addition, provided proof-of-concept for local targeting of gelatin nanoparticles loaded with PMX-53 as a powerful candidate for the treatment of periodontitis.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

This manuscript is aimed at encouraging those interested in biofilms and their effects on microbial communities to view the study of chronic airways disease as one of the most challenging and potentially rewarding areas to focus on. To do this it is necessary to review how we have reached 2023 having made so little progress. It is more than two hundred years ago since the term bronchitis was proposed to describe diseases affecting the airways rather than the respiratory zone of the lungs. The main histological features were described in Laennec’s text of 1819. By the mid C20th it was recognised that the two great drivers of chronic respiratory symptoms were inhaled toxins and chronic bacterial infections. The widespread availability of antibiotics in subsequent years had a dramatic impact on mortality from pneumonia, greatly reduced the prevalence of chronic symptoms after acute lower respiratory tract infections, transformed the lives of many with bronchiectasis and appeared to play a major role in the dramatic fall in prevalence of bronchiectasis. Unfortunately, during the following 70 years the importance of dealing with a chronic bacterial bronchitis was largely forgotten. While two artificial diseases ‘COPD’ and ‘bronchiectasis’ have been the subject of numerous guidelines, chronic bacterial bronchitis has been out in the cold. Antibiotics have in theory been reserved for those with established damage in the form of bronchiolitis and impaired lung function as defined by an arbitrary FEV1/FVC ratio and even then there is a disconnect with a minimum of 2 weeks being prescribed for exacerbations of bronchiectasis and the use of long term inhaled and oral macrolides frequently being advocated while a 5 day course is reserved for an exacerbation in a patient with COPD. The loss of focus on the role of antibiotics has a number of causes – a desire to ration antibiotic as part of ‘antibiotic stewardship’, the potential cost of treatment, the failure to ‘cure’ patients with chronic bronchitis and the difficulty in dealing with a disease whose morbidity at any given time can range from a mild cough and production of phlegm in the morning with no obvious impact on daily function to troublesome cough, sputum production and significant ill health. This was compounded by the vagaries of culture based diagnosis and the observation that more than one potential pathogen was often present. As a result chronic bacterial bronchitis was relegated to the ‘too hard to deal with’ box and many of the lessons of the past were forgotten. Revisiting the field from the perspective of current understanding of bacterial behaviour in biofilms and how these alter the resident microbiota of the airways should ensure that a chronic bacterial bronchitis returns to centre stage in order to minimise respiratory morbidity and mortality. This review charts the history of our understanding and mis-understanding of the role of chronic bacterial bronchitis in airways disease and the pernicious effect of language in helping to ensure that this common and important condition became the elephant in the room. Focusing on the nature of chronic bacterial bronchitis is essential if we are to find ways of identifying it accurately and treat it effectively – new approaches will be extremely slow unless we recognise its importance and the challenges it poses.