The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.

Every year, tropical hurricanes affect North and Central American wildlife and people. The ability to forecast hurricanes is essential in order to minimize the risks and vulnerabilities in North and Central America. Machine learning is a newly tool that has been applied to make predictions about different phenomena. We present an original framework utilizing Machine Learning with the purpose of developing models that give insights into the complex relationship between the land–atmosphere–ocean system and tropical hurricanes. We study the activity variations in each Atlantic hurricane category as tabulated and classified by NOAA from 1950 to 2021. By applying wavelet analysis, we find that category 2–4 hurricanes formed during the positive phase of the quasi-quinquennial oscillation. In addition, our wavelet analyses show that super Atlantic hurricanes of category 5 strength were formed only during the positive phase of the decadal oscillation. The patterns obtained for each Atlantic hurricane category, clustered historical hurricane records in high and null tropical hurricane activity seasons. Using the observational patterns obtained by wavelet analysis, we created a long-term probabilistic Bayesian Machine Learning forecast for each of the Atlantic hurricane categories. Our results imply that if all such natural activity patterns and the tendencies for Atlantic hurricanes continue and persist, the next groups of hurricanes over the Atlantic basin will begin between 2023 ± 1 and 2025 ± 1, 2023 ± 1 and 2025 ± 1, 2025 ± 1 and 2028 ± 1, 2026 ± 2 and 2031 ± 3, for hurricane strength categories 2 to 5, respectively. Our results further point out that in the case of the super hurricanes of the Atlantic of category 5, they develop in five geographic areas with hot deep waters that are rather very well defined: (I) the east coast of the United States, (II) the Northeast of Mexico, (III) the Caribbean Sea, (IV) the Central American coast, and (V) the north of the Greater Antilles.

The monograph “Expert assessments in decision making: risks and safety” presents studies that address the problems of expert assessments and the formation of decisions based on expert opinions in some areas of human activity. The results of decisions based on such assessments have a direct impact on safety and the risks that may follow. The problems are considered in the following aspects. An innovative approach to the formation of a list of types of forensic examinations and expert specialties.It has been established that the existing classification of forensic examinations by branches of specific examination used in their conduct is outdated and does not meet modern requirements. This creates difficulties not only for forensic experts and persons wishing to become them, but also for the court, participants in the trial and all citizens interested in obtaining a forensic expert's opinion. A discrepancy has been established between the types of forensic examination and the specialties of experts for which the qualification of a forensic expert is assigned in departmental lists. It has been proposed to unify the interdepartmental approach to the classification of forensic examinations in order to avoid errors that could lead to illegal and unfounded court decisions. The issue of unification can be resolved by developing a general approach to the classification of types and types of forensic examinations, enshrined in an interdepartmental regulatory act, which should be based on the criteria of the general theory of forensic examination. The creation of a unified list of types of forensic examinations and their corresponding expert specialties can open up ways to solve many issues facing the expert community. Taking into account the fact that at the time it is planned to provide opinions by experts in electronic form, the creation of a forensic expert’s office and its integration with the Unified Judicial Information and Telecommunication System, the creation of a modern unified list by types of forensic examinations and their corresponding expert specialtiesis on the agenda. Blood cell image recognition using texture and neural networks for leukemia diagnosis.Morphological analysis of blood cell images is usually performed manually by an expert, but this method has many disadvantages, including slow analysis, low accuracy, and the results depend on the skill of the operator. This reduces the chance of a correct diagnosis in detecting acute lymphoblastic leukemia, a potentially fatal blood cancer if left untreated. The study developed and presented an automated method for identifying and classifying leukocytes using microscopic images of peripheral blood smears. The proposed neural random threshold classifier achieved a recognition rate of 98.3% when the data was divided into 80% training set and 20% test set. The proposed system can be implemented as a computational tool to detect other diseases in which blood cells undergo changes, such as Covid-19. This will eliminate the subjective factor that is invariably inherent in the case of assessment by experts, focusing only on their experience. Therefore, the system is important for conducting expert assessments in medical diagnostics, as part of a decision support system that reduces the likelihood of risks of incorrect diagnosis of diseases. Analysis of the capabilities of the Internet of Things in monitoring the physiological state and location of personnel on an offshore oil platform.The possibilities of using the Internet of Things (IoT) to ensure personnel safety on an offshore oil platform were explored. For this purpose, IoT applications and technologies for monitoring the physiological state and location of personnel are analyzed. The use of cloud technologies, big data technologies and artificial intelligence for the development of systems that allow monitoring and, if necessary, making appropriate decisions through systematic monitoring of personnel status is considered. The basis for decision making is expert assessments of deviations of real-time parameter values from the norm. Practical problems associated with the use of Internet of Things technologies in various areas of healthcare are presented. Methodological approaches to intelligent management of human factors on offshore oil and gas platforms.The problems of increasing the efficiency of managing the safety and health of shift workers in the offshore oil and gas industry through the prism of the human factor have been studied. The environmental features, hazards and risks, working and professional conditions in the offshore sector are taken into account. The concept of a person-centered approach to personnel safety and health management is proposed, which involves the inclusion of employees in the management loop as the main component of their contextual environment. This involves constant remote monitoring of vital health indicators of employees and, at the same time, parameters of the context-dependent environment of each of them, as well as an expert assessment of the deviation of these parameters from the norm. Decision support in a remote health monitoring system for shift workers on an offshore oil platform.A methodological approach to the synthesis of solutions in a geographically distributed intelligent health management system for oil workers working in the maritime industry is proposed. A functional model of the health management system for workers employed on offshore oil platforms has been developed and implemented in three stages: monitoring and assessment of health indicators and environmental parameters of each employee, as well as decision-making. These interacting operations integrate layers of a distributed intelligent healthcare management system. Appropriate approaches to implementing decision support processes are presented and one of the possible methods for assessing generated data and making decisions using fuzzy pattern...

A new daily composite of the solar flare index (SFI) and the hemispherically‐resolved versions (hSFI) are presented for 1937 to 2024. The data set confirms that the northern hemisphere (NH) dominated solar flare activity during Solar Cycles 17 to 21, but that the southern hemisphere has dominated from Solar Cycle 22 to present. That said, the highest SFI value occurred in the NH during the recent superstorm of May 2024. In sunspot activity, the “Gnevyshev‐Ohl rule” shows that the sum of sunspot numbers during even‐numbered cycles is related to those of adjacent odd‐numbered cycles. A similar rule appears to apply to SFI. The “Gnevyshev gap” phenomenon where solar maximum activity sometimes has two peaks separated by up to 1–2 years of a gap is confirmed for SFI. Although our data set represents the longest continuous daily data set for solar flare activity to‐date, it is known that stronger solar flare events occurred before 1937. Therefore, a brief discussion of earlier solar flare events in the historical record is also provided for context. The statistics of the SFI and hSFI series are compared to other solar and geomagnetic activity indices, including the May and October 2024 solar storms. Statistical analysis of past geomagnetic storms confirms they are more frequent during active cycles and less frequent during solar minima. Strong geomagnetic storms are also more likely to occur during the positive phase of a 1.7 year's quasi‐biennial oscillation in solar activity. The likelihood of low‐magnetic latitude aurorae seems to have a 30 year periodicity component.

The study of building materials is important for a better conservation of built heritage. Worldwide, volcanic stones (including basalt, andesite and dacite) are among the least studied building materials. In this research, the decay of a red basalt due to wet atmospheric deposition was studied. Red basalt was exposed to artificial rain solutions, prepared from rain samples collected weekly from 2014–2019. In this research, the decay of stone-built heritage was indirectly studied emulating wet atmospheric accelerated weathering under three different volume weighted mean (VWM) compositions: global, acid and no-acid categories. Lixiviates were analyzed to better understand the deterioration mechanisms taking place inside the material. Decay was quantified as mass difference, water absorption capacity (WAC) and open porosity (OP) changes. Results show that the methodology used is suitable to research the decay of built heritage. The studied basalt is indeed prone to decay by wet atmospheric deposition. The main decay mechanisms are the washing of insoluble compounds, dissolution of minerals, salt crystallization and cation exchange. WAC and OP showed promising results of their appropriateness as monitoring variables of decay in situ. Acid conditions produce the most severe decay, but Ph effect is not as important as precipitation volume. Non-linear equations relating volume of precipitation with mass difference in red basalt are presented.

Densely populated urban megacities, such as the Metropolitan Area of Mexico City, face the ongoing deterioration of air quality. Emissions from industrail factories and internal combustion vehicles are the main sources of pollutants. We have evaluated different transition trends from internal combustion engine vehicles as bus, truck and van, and motorcycle to electric vehicles through 2050. The total vehicle growth follows a second-degree polynomial trend. Bus growth exhibits a linear trend. Truck and van growth display a second-degree polynomial trend. Motorcycle growth also follows a second-degree polynomial trend. We found that the most significant reductions in transportation emissions are observed in $C{O}_2$, followed by $N{O}_x$, volatile organic compound (VOC), and particulate matter, with light and heavy vehicles being the primary contributors to total emissions. Mexico City serves as a pilot laboratory where both the challenges and potential solutions to an issue affecting millions of citizens can be observed. If proven effective and practical, these solutions could be applied to other megacities.