Bhupesh Chand, Frezer Ayele, Ian Pineiro-Dakers
et al.
The growing number of older bridges has resulted in an increase in structural flaws, demanding frequent inspections and maintenance. Structural degradation accelerates post-damage recovery, emphasizing the necessity of preventive interventions. The use of Uncrewed Aerial Vehicle Systems (UASs) for bridge inspections represents a significant development in structural health monitoring (SHM). Traditional inspection methods are labor-intensive, time-consuming, expensive, and require access to high or difficult-to-reach areas, posing safety risks to inspectors. This study focuses on identifying drones that can efficiently support bridge inspection activities. Key factors influencing UAS selection include flight performance, flying modes, cost, sensor capabilities, payload capacity, and controller communication. The primary objective of this paper is to provide guidance to inspectors and transportation agencies regarding the capabilities and limitations of commercially available drones. It also outlines potential cost considerations associated with drone selection, including pilot skill level, platform cost, and sensor integration. These factors may vary depending on the type and complexity of the bridge being inspected. By addressing these aspects, this paper aims to assist decision-makers in making informed choices regarding the use of UASs for bridge inspection applications.
This paper estimates the effect of Hurricane Harvey on wages and employment in the construction labor industry across impacted counties in Texas. Based on data from the Quarterly Census of Employment and Wages (QCEW) for the period 2016-2019, I adopted a difference-in-differences event study approach by comparing results in 41 FEMA-designated disaster counties with a set of unaffected southern control counties. I find that Hurricane Harvey had a large and long-lasting impact on labor market outcomes in the construction industry. More precisely, average log wages in treated counties rose by around 7.2 percent compared to control counties two quarters after the hurricane and remained high for the next two years. Employment effects were more gradual, showing a statistically significant increase only after six quarters, in line with the lagged nature of large-scale reconstruction activities. These results imply that natural disasters can generate persistent labor demand shocks to local construction markets, with policy implications for disaster recovery planning and workforce mobilization.
Forest characteristics estimation is a vital task for ecological monitoring and forest management. Forest owners make decisions based on timber type and its quality. It usually requires field based observations and measurements that is time- and labor-intensive especially in remote and vast areas. Remote sensing technologies aim at solving the challenge of large area monitoring by rapid data acquisition. To automate the data analysis process, machine learning (ML) algorithms are widely applied, particularly in forestry tasks. As ground truth values for ML models training, forest inventory data are usually leveraged. Commonly it involves individual forest stand measurements that are less precise than sample plots. In this study, we delve into ML-based solution development to create spatial-distributed maps with volume stock using sample plot measurements as reference data. The proposed pipeline includes medium-resolution freely available Sentinel-2 data. The experiments are conducted in the Perm region, Russia, and show a high capacity of ML application for forest volume stock estimation based on multispectral satellite observations. Gradient boosting achieves the highest quality with MAPE equal to 30.5%. In future, the proposed solution can be used by forest owners and integrated in advanced systems for ecological monitoring.
Amid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions.
Adrien Soula, Florian Leseigneur, Amna Anwar
et al.
Adeno-associated viral (AAV) vectors have been established as a safe and effective delivery vehicle for gene therapy. However, current methods for AAV production using adherent approaches are suboptimal due to their reliance on a substantial number of plastic-based flasks, manual labor, and a significant manufacturing footprint. Consequently, a protocol for generating AAV2 was developed on the Quantum, a semi-automated closed hollow-fiber bioreactor platform. In this system, Human Embryonic Kidney 293T cells were successfully expanded and transfected to produce an average crude AAV2 titer of 4.92 × 1014 viral particles and 6.81 × 1013 viral genomes from 1.2 L of harvested cell lysate. The application of a standard AAV downstream process confirmed normal processability of the material. A cost of goods model comparing the Quantum bioreactor with the current standard HYPERStack36 and Corning CellSTACK 10-layer systems demonstrated that the Quantum bioreactor reduced the number of open steps by more than 40-fold, production time by up to 3.6-fold (HYPERStack36) and 7.5-fold (CellSTACK 10-layer), and costs by up to 2-fold (HYPERStack36) and 20.7-fold (CellSTACK 10-layer). Therefore, the Quantum bioreactor is an effective alternative to plastic flasks for the manufacturing of AAVs at both R&D and early translational scale, as it reduces production time, operating costs, and process risk.
Juan José Maldonado-Miranda, Domingo Martínez-Soto, Juan Gilberto Ceballos-Maldonado
et al.
Organic vanilla production in Mexico holds significant promise but faces substantial challenges that impact its sustainability and market competitiveness. As the native region of <i>Vanilla planifolia</i>, Mexico is endowed with rich biodiversity and a deep cultural heritage surrounding vanilla cultivation. Organic production systems in the country predominantly rely on traditional agroforestry practices, manual pollination, and artisanal curing methods, all of which enhance the quality and distinctiveness of Mexican vanilla. However, production is hindered by critical factors, including low genetic diversity and susceptibility to phytopathogenic diseases, particularly stem and root rot caused by <i>Fusarium oxysporum</i>. In recent years, the application of in vitro micropropagation techniques has shown great potential for obtaining pathogen-free plants and conserving germplasm, offering a sustainable alternative to strengthen organic systems and reduce pressure on wild populations. The labor-intensive processes, yield variability, and the complexity of adhering to organic certification standards are additional challenges to overcome. Shifts in consumer preferences toward natural and sustainably produced goods have increased demand for organic vanilla, offering Mexican producers an opportunity to gain a more prominent position in the global market. Advancing research into disease management, fostering genetic conservation, and integrating scientific advances with traditional know-how are vital strategies for overcoming current limitations. In this context, organic vanilla production represents not only an economic opportunity but also a means to conserve biodiversity, support rural communities, and maintain the legacy of one of Mexico’s most emblematic agricultural products. This review was conducted using a qualitative, narrative analysis of recent scientific literature, technical reports, and case studies related to organic vanilla production in Mexico.
Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e.g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes. The method uses a finite partitioning of the noise domain to construct an interval Markov chain (IMC) abstraction of the system via transition probability intervals. Noise partitioning allows for a general class of distributions and structures, including multiplicative and mixture models, and admits both known and data-driven systems. The partitions required for optimal transition bounds are specified for systems that are monotonic with respect to the noise, and explicit partitions are provided for affine and multiplicative structures. By the soundness of the abstraction procedure, verification on the IMC provides guarantees on the stochastic system against a temporal logic specification. In addition, we present a novel refinement-free algorithm that improves the verification results. Case studies on linear and nonlinear systems with non-Gaussian noise, including a data-driven example, demonstrate the generality and effectiveness of the method without introducing excessive conservatism.
Devi Padmavathi Alagarswamy, Christian Berghoff, Vasilios Danos
et al.
Various mobility applications like advanced driver assistance systems increasingly utilize artificial intelligence (AI) based functionalities. Typically, deep neural networks (DNNs) are used as these provide the best performance on the challenging perception, prediction or planning tasks that occur in real driving environments. However, current regulations like UNECE R 155 or ISO 26262 do not consider AI-related aspects and are only applied to traditional algorithm-based systems. The non-existence of AI-specific standards or norms prevents the practical application and can harm the trust level of users. Hence, it is important to extend existing standardization for security and safety to consider AI-specific challenges and requirements. To take a step towards a suitable regulation we propose 50 technical requirements or best practices that extend existing regulations and address the concrete needs for DNN-based systems. We show the applicability, usefulness and meaningfulness of the proposed requirements by performing an exemplary audit of a DNN-based traffic sign recognition system using three of the proposed requirements.
We study the dynamic effects of fires on county labor markets in the US using a novel geophysical measure of fire exposure based on satellite imagery. We find increased fire exposure causes lower employment growth in the short and medium run, with medium-run effects being linked to migration. We also document heterogeneous effects across counties by education and industrial concentration levels, states of the business cycle, and fire size. By overcoming challenges in measuring fire impacts, we identify vulnerable places and economic states, offering guidance on tailoring relief efforts and contributing to a broader understanding of natural disasters' economic impacts.
Several sources of uncertainty have to be taken into account in the analysis and design of CPS. The set of parameters used in the model of the physical plant of a CPS may be uncertain due, for example, to manufacturing processes that are precise up to some bounded tolerance. Physical quantities are sensed by electronic components that add noise to the sensed signals. Abstraction of the physical world, which is often necessary to limit the complexity of the models used in analysis and at run-time in decision-making, leads to non-determinism. The cyber side of a CPS, which includes both hardware and software components, exposes several types of uncertainty such as failures, latency, and implementation errors. Design processes and tools allow engineers to minimize the impact of these types of uncertainty, and to deliver systems which can be operated with an acceptable level of risk. In the past several years, cyber-physical systems have evolved, primarily due to pervasive connectivity, miniaturization, cost-effectiveness of hardware, and advances in the area of Artificial Intelligence. This new class of applications features an environment that is much more complex to model than traditional physical systems due not only to their scale, but also to new sources and types of uncertainty. Consider, for example, the typical case of echo chambers which is attributed to the effect that machine learning algorithms have on the bias of people. Such behavior is not easily predictable because of high uncertainty in the environment (people), which is only approximately represented by machine learning models, but that is inherently due to lack of knowledge. New models and analysis methods are therefore needed to capture different types of uncertainties, and to analyze these new classes of systems.
This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a predefined finite time horizon, and the probability of reaching a set of specified states at a particular time instant. The barrier functions presented in this manuscript are developed either by relaxing a parabolic partial differential equation that characterizes the exact reachability probability or by applying the Grönwall's inequality. In comparison to the prevailing construction method, which relies on Doob's non-negative supermartingale inequality (or Ville's inequality), the proposed barrier functions provide stronger alternatives, complement existing methods, or fill gaps.
Nicolae Daniel FÎȚĂ, Mila Ilieva OBRETENOVA, Adina TĂTAR
et al.
The most effective way to ensure safety and protect the health of workers at work is to
eliminate sources of danger. It is an action whose effects are maximal if carried out in the design
and construction of work systems, but which is also beneficial afterwards, but with one condition:
a methodology is in place to allow analysis, identification and determination of the treatment of
these sources. The development of theory and applications in the field of systems security has
provided a scientific basis and useful tools for the work of preventing professional risks. All
methods designed to achieve system security have been taken over and adapted to determine with
their help the possibilities of protecting the safety and health of workers. The development,
implementation and promotion of an integrated, proactive approach to OSH management in high
and very high voltage power substations has long been supported by policies and practices
established at international, european and national level, including strategies, legal provisions,
standards, guidelines, programs and campaigns initiated and undertaken by various stakeholders,
such as international organizations, EU bodies, governments, trade unions and employers
asociations, labor inspectorates and insurance institutions.
Chen Rothschild, Eliahu David Aflalo, Inbar Kedem
et al.
To meet the increasing demand for aquacultural products, it is necessary to increase cultured fish production and to ensure that the fish species grow with maximum efficiency. A significant component of aquacultural production is the counting process, a task that is time and labor intensive. To address this problem for the crustacean species Macrobrachium rosenbergii, two computer vision systems that automatically detect and count larvae were developed. In the first system, images were acquired from an indoor recirculating system under two different illumination conditions—room lighting or illumination over the top of the growth tanks. Two experiments were performed with this system. In the first experiment, 200 images were acquired in a single day of larvae in developmental stages Z9-Z10 (length of 6.07–7.05 mm) with an iPhone 11 camera. In the second experiment, a larviculture recirculating system was photographed along 11 distinct days (representing the 11 developmental stages from hatching to metamorphosis into post larvae) with two different devices, an iPhone 11 camera and a SONY DSCHX90V camera. For the iPhone 11 camera, two different illumination conditions were tested, and in each condition, 110 images were acquired. In the second system, a DSLR Nikon D3500 camera was used to acquire a total of 700 images of day-1 larvae held in petri dishes at seven different larval densities. An algorithm that automatically detects and counts the number of larvae was developed for both systems based on the YOLOv5s convolution neural network model. The first experiment in the first system was used to find the best hyperparameters and network weights for the data set. These were used as is in the second experiment with no additional training. With the same algorithm, the second system was trained from scratch and new hyperparameters were derived. Results of the first experiment that included larvae from a single day for the indoor recirculating system gave 97% accuracy with a mean average precision (mAP) of 0.961 in object detection resulting with a mean absolute error (MAE) of 1.45 in counting in the first experiment (one day). Results of the second experiment that include larva from 11 larval stages yielded an accuracy of 88.4% with a mAP of 0.855 in object detection and a MAE of 4.288 in counting. The second system for counting one-day-old larvae in petri dishes gave an accuracy of 86% with a mAP of 0.801 and a MAE of 4.35.
Introduction
The negative consequences of the stigmatization of mental illness significantly impair health care system, society, patients and their families. It has been established, that more than 40% of patients with schizophrenia suffer from self-stigmatization (E. Brohan et al., 2010), what determines the relevance of research aimed at it’s reduction.
Objectives
To assess the level, components of self-stigmatization and associated with it factors in patients with schizophrenia, receiving psychosocial treatment in the community; to propose and implement a complex of interventions for destigmatization.
Methods
The battery of instruments was used: Self-stigmatization questionnaire (V.S. Yastrebov, I.I. Mikhailova et al., 2005), revealing the patient’s tendency to explain their problems in the main areas of psychosocial functioning as manifestations of the disease or the prejudice against them; Emotional intelligence questionnaire (D.V. Lyusin, 2006); Quality of life questionnaire (J.E. Ware et al., 1995); Montreal Cognitive Assessment (Z.S. Nasreddine, 1996). 40 patients with schizophrenia (ICD-10 F.20), receiving psychosocial treatment in a non-profit organization in community, were examined.
Results
The overall level of self-stigmatization in the studied patients constituted 42.8% or an average level of self-stigmatization. Using Self-stigmatization questionnaire, nine components of self-stigmatization were revealed. The most pronounced indicators were in following components: “Reassessment of self-realization”, “Readiness to distance from the mentally ill in the social sphere”, “Reassessment of internal activity” (56.2%, 56.5%, 55.1% correspondingly). By the forms of self-stigmatization demonstrated that patients with autopsychic form (the justification of their failure by the disease) constituted the largest proportion or 41%. The compensatory form (denial of one’s incompetence with its exaggeration in other mentally ill people) and socio-reversive form (explaining incompetence by the prejudice against them) had similar rates in 29% and 30% of patients, correspondingly. Inverse strong correlations with some of scales of the Emotional intelligence questionnaire, Cognitive scale and the Quality of life questionnaire were established. Destigmatization training for patients with schizophrenia based on cognitive behavioral psychotherapy was worked out. A set of destigmatization interventions was proposed and implemented.
Conclusions
A complex of different interventions taking into account the form of self-stigmatization and it’s main components, should be used. These interventions have to include psychoeducation, cognitive trainings, self-esteem trainings and special destigmatization trainings.
Keywords: schizophrenia, self-stigmatization, destigmatization trainings
Disclosure of Interest
None Declared
Iodine deficiency in the biosphere, mainly in the soil, leads to endemic goiter and other iodine deficiency disorders. As shown by numerous studies, this disease affects over 1 billion people in the world. Endemic goiter and much of the territory of Ukraine, including the Vinnytsia region. Presented current research study of recipes and technologies on system-cooked sausages. Currently, boiled sausage enjoys relatively high demand among the population because it is significant for most people, giving it an advantage. Innovation activity represents one of the most effective directions. It should facilitate the development of innovative activity that promotes penetration into new markets and production growth. Today, the most essential and effective innovative organizational and technical measures introduced in sausage production can be considered are the use of new recipes for manufacturing products and the installation of new equipment, which can significantly reduce the cost of manual labor and power inputs and improve product quality and reduce the share of marriage. Sausage production is seen as a thermochemical method of preserving meat products. For each type of sausage, set production process-approved technological instructions and recipes. Strict adherence to recipes, specialized instructions, and sanitary regimes is a prerequisite for obtaining high-quality links. Crucial in the production of sausages is raw. The primary raw materials are beef and pork. Occasionally, use lamb and horsemeat. Equally important is the creation of a new generation of products that have general strengthening and preventive action. The components of these products can protect the body from the harmful effects of the environment and the emergence of human diseased cells. The constant lack of iodine leads to a reduction in the synthesis and secretion of the primary thyroid hormone - thyroxine. The role of thyroid hormones in the body is extremely high, and most of the vital functions are performed by their participation. The main physiological effects of thyroid hormone are stimulating synthesis, growth, development, and differentiation of tissues. Developed products to include microorganisms that can synthesize biologically active structures (antibodies, receptors, hormones) that contribute to the removal or destruction of harmful decay and systems, thereby preventing human disease.
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.
Alexander Tarnavsky Eitan, Shirly Someck, Mario Zajac
et al.
In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and micro-LEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving micro-LEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a micro-LED probe, the system allows linear control of light output power, up to 10 micro-W per micro-LED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple micro-LEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.
This paper focuses on the effective use of smart inverters for Volt/Var control (VVC) on a distribution system. New smart inverters offer Var support capability but for their effective use they need to be coordinated with existing Volt/Var schemes. A new VVC scheme is proposed to facilitate such coordination. The proposed scheme decomposes the problem into two levels. The first level uses Load Tap Changer (LTC) and Voltage Regulators (VRs) and coordinates their control with smart inverters to adjust the voltage level on the circuit to keep the voltages along the circuit within the desired range. The second level determines Var support needed from smart inverters to minimize the overall power loss in the circuit. The results of the supervisory control are sent to the devices which have their local controllers. To avoid frequent dispatch, smart inverters are supervised by shifting their Volt/Var characteristics as needed. This allows for the smart inverters to operate close to their optimal control while meeting the limited communication requirements on a distribution system. A case study using the IEEE 34 bus system shows the effectiveness of this supervisory control scheme compared to traditional volt/var schemes.
Objectives. The main purpose of the work is to study the advantages of an activity-based approach, or functional approach in comparison with a subject-based approach, or lecture-based approach for creating a model of a professional field for adaptive educational process. The relevance of the problem being solved is caused by due to the fact, that adaptive educational technologies, thanks to the extensive computerization and informatization of all spheres of human activity, as well as the innovative development of artificial intelligence, have become dominant in teaching systems at all levels, from preschool to higher education institutions. Without a highly effective model of the professional field, it is impossible to successfully implement adaptive learning.Methods. To achieve the goals and to prove the statements formulated, the method of comparative analysis of the most widely used ways for creating models of the professional field for adaptive learning in modern systems, was used. To do this, we studied: the effectiveness of using the traditional approach, which is based on subjectspecific or lecture-seminar signs of structuring educational material; as well as the effectiveness of using an activity-based approach based on the concept of "Modules of labor competencies" for the formation of educational material.Results. The analysis of the quality of educational material (a model of a professional field for an adaptive educational process) obtained using the traditional approach, and educational material (the same model for its intended purpose) obtained on the basis of an activity approach in accordance with the concept of "Modules of labor competencies". The results of the analysis are compared and theoretical statements are summarized, taking into account the examples of developed modular materials, as well as the pilot implementation of previously developed modular programs.Conclusion. The information given in the article can be useful for specialists, researchers and heads of educational institutions of various levels, when planning and creating adaptive learning systems. The conclusions and recommendations fully comply with the requirements of the development of the education system of the Republic of Belarus.