Temperature Parameters Research Articles

IoT-based design for plant cultivation experiments and data logging

Abstract Recent advancements in low-cost microcontroller platforms and Internet of Things (IoT) sensors have enabled the development of automated, modular growth chambers for precise environmental control in plant studies. This study presents a cost-effective, scalable chamber equipped with Wi-Fi-enabled sensors for real-time monitoring of temperature, humidity, CO2, light intensity, and soil parameters. The system integrates an automated irrigation mechanism, programmable lighting, and cloud-based data management system. The design ensures independent sensor operation, preventing system-wide failures while maintaining experimental reproducibility. The results demonstrate that this approach significantly reduces costs while maintaining a reliable working system, providing high-resolution environmental control and making it suitable for diverse agricultural and botanical research applications.

Tracing the evolutionary stage of low-mass dense cores by using the abundance ratio of SO to SO2

Abstract Sulfur-bearing molecules are frequently detected in star-forming regions. The abundance ratio of SO to SO$_2$ (hereafter, SO/SO$_2$ ratio) has been proposed as a potential evolutionary tracer of star formation because tentative correlations have been found between SO/SO$_2$ ratio and evolutionary stages. Nonetheless, the lack of high-resolution observations and extensive statistical analyses of large samples raises questions regarding its efficacy as an evolutionary tracer. In this study, we analyze 31 protostellar cores with both SO and SO$_2$ detection to investigate the relationship between the SO/SO$_2$ ratio and two evolutionary parameters of chemical richness factor ($CRF$) and gas temperatures. 
The correlations between SO/SO$_2$ ratio and evolutionary parameters have been found, but these correlations are mainly contributed by low-mass cores rather than high-mass cores.
Our findings suggest that the SO/SO$_2$ ratio serves as a reliable evolutionary tracer for low-mass dense cores. In high-mass cores, the relationship between the SO/SO$_2$ ratio and evolutionary stage remains ambiguous. This likely arises from both vigorous stellar feedback effects and the potential absence of distinct evolutionary stages analogous to those in low-mass cores. Consequently, higher-resolution observations with expanded statistical samples are required to assess the viability of SO/SO$_2$ ratio as an evolutionary tracer for high-mass cores.

Combined sequential hypothermic oxygenated and normothermic machine perfusion for liver transplant from an expanded criteria donor: first clinical application in Russia

Objective: to analyze a clinical case series and evaluate the safety and efficacy of a sequential machine perfusion protocol combining dual hypothermic oxygenated perfusion (D-HOPE) and normothermic machine perfusion (NMP) for conditioning and viability assessment of liver grafts retrieved from expanded criteria donors (ECD) in routine clinical practice.Materials and methods. Between November and December 2024, two sessions of combined D-HOPE followed by NMP were conducted at Shumakov National Medical Research Center of Transplantology and Artificial Organs («Shumakov Research Center») using liver allografts obtained from ECD after brain death. Following an initial period of static cold storage (SCS), machine perfusion was initiated using a circulatory assist device. A histidine-tryptophan-ketoglutarate (HTK)-based perfusate was used during the DHOPE phase, while a red blood cell (RBC) suspension was used during the NMP stage. Throughout perfusion, temperature and hemodynamic parameters were continuously monitored and maintained. Laboratory parameters were assessed at designated intervals, in accordance with the institutional protocol developed at Shumakov Research Center.Results. Allograft #1 was deemed non-viable due to elevated lactate levels after 3 hours of perfusion and lack of glucose metabolism. The preservation times were as follows: SCS – 424 minutes, D-HOPE – 120 minutes, NMP – 300 minutes, totaling 844 minutes. Allograft #2 met the viability criteria and was successfully transplanted. Preservation times were: SCS – 260 minutes, D-HOPE – 124 minutes, NMP – 480 minutes, with a total preservation time of 884 minutes. Post-transplant peak levels of AST, ALT, and total bilirubin in the recipient were 922.5 U/L, 613 U/L, and 63.3 μmol/L, respectively. The only postoperative complication was acute kidney injury, managed with two sessions of hemodialysis. The patient was discharged after 14 days of hospitalization without need for readmission. At the time of writing, the patient is alive and complication-free, with a follow-up period of 3 months.Conclusions. Combined machine perfusion of liver grafts appears to be a safe and effective strategy to mitigate ischemia-reperfusion and preservation-related injury in liver transplantation. It also facilitates viability assessment of marginal liver grafts, reduces potential recipient complications, and expands the donor pool through the use of allografts from ECD.

Abstract B021: Current oncological large language model research lacks reproducibility, transparency, and long term support

Abstract Large Language Models (LLMs) have been adopted increasingly in oncology, for example, in structuring data from clinical notes, inferring diagnoses from free text or imaging data, and anonymizing of data. Due to the rapid development pace of LLMs, best practices for conducting and reporting oncological research in these applications have yet to be fully established.We queried PubMed for oncology-related LLM research with the last cutoff set at Dec 31st 2024. We investigated 179 papers. Of these, 131 were removed due to omission criteria, and 48 were structured and reported here. Inclusion criteria were oncology-related research and full research articles. Structured fields included date of submission, acceptance, and publishing, the granularity of model reporting (model family, model snapshot), reporting of key LLM model parameters, availability of source code and data, and programming language and API details. We noted an almost exponential growth of LLM-related publications in oncology, with a relatively short time from authors’ submission to publicly available publication (median 3.7 months, IQR 2.5-5.9 months). Interestingly, despite the relatively short processing time, in 25% of cases, the exact model essential to the publication had been deprecated by the model service providers or a newer version was available at the time of publishing. 35.4% of published research relied solely on a graphical user-interface (GUI) of LLMs such as ChatGPT, while 37.5% reported programmatically API-use, with Python as the most common language. While most publications either fully or partially reported the utilized prompts (75%), only 22.9% reported the exact key model parameters, such as temperature. Even when the temperature parameter was available, 45.4% of these publications used a temperature value larger than 0, resulting in more stochastic answers. Source code was made publicly available in 18.7% of publications that reported using a programming language such as Python or R. While practically all publications (97.9%) reported the used model families such as GPT-4o, Claude 3.5 Sonnet or Llama 3-70B, only 27% reported the exact model snapshot usage such as GPT-4o with snapshot options available for May 13th, August 6th or November 20th in 2024. We exemplify and report shortcomings of recent LLM adoption in oncological research. To alleviate these issues, we propose a checklist to improve reproducibility, transparency, and longevity of LLM research directed at researchers and journals. We propose the following preliminary checklist: exact reporting of model snapshot and model parameter bound to a specific snapshot instead of latest release, API usage instead of GUI chatbots, temperature-parameter equal to 0, assessment of variability across runs, session restarts to avoid biases, and caution in researching models that are bound to be deprecated due to the short turn-around time in LLMs. Additionally, rigorous prompt engineering and especially few-shot learning show potential in optimizing interactions with LLMs, also in oncology. Citation Format: Tolou Shadbahr, Antti S. Rannikko, Tuomas Mirtti, Teemu D. Laajala. Current oncological large language model research lacks reproducibility, transparency, and long term support [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B021.

Dual-axis Solar Tracker with PID Control and LoRa-based Environmental Sensing for Improved Solar Energy Output

The paper introduces the design, construction, and testing of a precision dual-axis solar tracking system to achieve optimal solar energy yield. The system features a Proportional-Integral-Derivative (PID) control algorithm for precise sun tracking and combines LoRa-based environment sensing for real-time monitoring and adaptive performance under changing conditions. The suggested solar tracker, having a PID controller-driven servo motor, provides continuous and accurate tracking of the sun throughout the day, thus efficiently overcoming the disadvantage of conventional fixed solar panels in terms of sun misalignment and decreased energy output. The designed methodology utilizes LoRa wireless communication and environmental sensors to gather live environmental parameters of solar irradiance, temperature, and humidity for remote monitoring, adaptive control, and optimization of the panel's orientation under diverse weather conditions. The use of LoRa technology in the system ensures low-power, long-distance communication, best suited for off-grid and remote installations. The scalable nature facilitates IoT integration, which is an additional consideration towards the creation of efficient, reliable, and intelligent solar tracking systems.

Estimation of rail-wheel contact forces and wear number using wheel tread temperature rise

Temperature rise at the rail-wheel contact is directly correlated to the contact forces and wheel wear which are central to railway vehicle safety and stability. Temperature rise at the rail-wheel contact surface is influenced by the contact patch shape, size, location, contact forces and slip at the rail-wheel contact. In this work, a deep neural network (DNN) model is developed to predict the various contact parameters using temperature rise at the rail-wheel contact. Input parameters to the DNN model include operating (speed and acceleration), track (twist, elevation, gradient, curvature and superelevation) and temperature parameters (maximum temperature, full width half maxima and temperature peak location along the lateral direction), while, output to the DNN model include normal load, derailment coefficient, wear number and contact location. Data for training and test was obtained using a multibody vehicle dynamics model built in commercial software SIMPACK. The multibody vehicle dynamics model was validated using data from oscillation trials on a metro train operating on east-west Kolkata metro track. Wheel surface temperature rise is obtained using a two-step method. First, for each wheel, a two-dimensional boundary element model (BEM) is used to estimate heat partitioning at rail-wheel interface as a function of time. Next, frictional dissipation in contact patch along with heat partitioning information is fed into a three-dimensional BEM to obtain wheel surface temperatures. Multiple linear regression (MLR) and DNN models are then used to correlate temperature, track, and operating parameters to contact parameters. The developed DNN model accurately predict the contact parameters with R 2 greater than 90%. MLR model is only able to predict wear number accurately.

Determination of Thermodynamic Properties via Partition Function Approaches Derived from the Non-Relativistic Schrödinger Equation

A critical challenge in molecular thermodynamics is accurately predicting how variations in dimensionless potential parameters influence macroscopic properties such as magnetisation, specific heat capacity, and molecular vibrational entropy. Traditional approaches often fail to fully capture these dependencies, limiting their predictive accuracy. In this study, we address this gap by developing a rigorous framework based on partition function formulations derived directly from the non-relativistic Schrödinger equation. Our main aim is to investigate the impact of dimensionless potential parameters ( and ) and the temperature parameter (β) on energy spectrum and key thermodynamic and magnetic properties. We systematically compute the energy spectrum for a wide range of potential parameter values and analyse their effects on magnetisation, magnetic susceptibility, internal energy, free energy, entropy, and specific heat capacity. The findings reveal that energy spectrum exhibit strong dependence on these parameters, with ​ generally reducing energy levels while ​ enhances them. Magnetisation typically decreases with ​ but increases with ​ and higher temperatures, while magnetic susceptibility shows complex patterns of enhancement and suppression. Vibrational thermodynamic properties, including internal energy and free energy, also display significant variations tied to the interplay of potential parameters. This study provides a robust, first-principles-based method for understanding and predicting how microscopic potential parameters govern macroscopic thermodynamic behaviour, advancing the fundamental knowledge and practical capabilities of molecular thermodynamics

A Monte Carlo Simulation of Measurement Uncertainty in Radiation Thermometry Due to the Influence of Spectral Parameters

While radiation thermometry is well-developed for laboratory calibrations using high-emissivity sources, the effect of spectral emissivity in real-world conditions, where emissivity ranges from 0 to 1, is usually not considered. Spectral parameters that influence non-contact temperature measurements are often neglected even in laboratory conditions. These parameters become more important with decreasing emissivity and at lower temperatures, leading to increased uncertainty contributions to the measurement result. In this manuscript, we analyze the impact of various influential spectral parameters using the constructed spectral Monte Carlo simulation of radiation thermometry. The investigation covers the influence of spectral and related parameters, namely spectral emissivity, reflection temperature, spectral sensitivity and atmospheric parameters of temperature, relative humidity and distance of the path in the atmosphere. Simulation results are compared to experimental results, overestimating sensitivity to humidity by 23–27% and sensitivity to emissivity and reflected temperature within 10% at given conditions. Multiple cases of radiation thermometer (RT) use are simulated for measurement uncertainty: high temperature RT use as the reference in calibration by comparison, the use of a flat plate calibrator for RT calibration, measurements with a RT using emissivity input data from literature with relatively high uncertainty and temperature measurements with a RT using emissivity data, obtained with FTIR spectroscopy with relatively low uncertainty. Findings suggest that spectral uncertainty contributions are often unjustifiably underestimated and neglected, nearing extended uncertainty contribution of 1.94 °C in calibration practices using flat plate calibrators with emissivity within 0.93 and 0.97 and 1.72 °C when radiation thermometers with spectral ranges, susceptible to atmospheric humidity, are used on black bodies.

Investigation of the Influence of Deformation, Force, and Geometric Factors on the Roll Gripping Capacity and Stability of the Rolling Process

This research developed a complex physical and mathematical model of the flat rolling theory problem. This model takes into account the influence of many parameters affecting the roll’s gripping capacity and the overall stability of the entire rolling process. It is important to emphasize that the method of the argument of functions of a complex variable does not rely on simplifying assumptions commonly associated with: the linearized theory of plasticity; or the decoupled solution of stress and strain fields. Furthermore, it does not utilize the rigid-plastic material model. Within this method, solutions are developed based on the complete formulation of the system of equations in terms of stresses and strains, incorporating constitutive relations, thermal effects, and boundary conditions that define a well-posed problem in the theory of plasticity. The presented applied problem is closed in nature, yet it accounts for the effects of mechanical loading and satisfies the system of equation. For this purpose, such factors as roll geometry, physical and mechanical properties of the rolled metal (including its fluidity, hardness, plasticity, and structure heterogeneity), rolling speed, metal temperature, roll lubrication, and many other parameters that can influence the process have been taken into account. Based on the developed mathematical model, a new, previously undescribed force factor significantly affecting the capture of metal by rolls and the stability of the rolling process was identified and investigated in detail. This factor is associated with force stretching of metal in the lagging zone—the area behind the rolls, where the metal has already left the deformation zone, but continues to experience residual stress. It was shown that this stretching, depending on the process parameters, can both contribute to the rolling stability and, on the contrary, destabilize it, causing oscillations and non-uniformity of deformation. The qualitative indicators of transient regime stability have been determined for various values of the parameter α. Specifically, for α = 0.077, the ratio f/α ranges from 1.10 to 1.95; for α = 0.129, the ratio f/α ranges from 1.19 to 1.95; and for α = 0.168, the ratio f/α ranges from 1.28 to 1.95.

Exploring a green and precise approach based on graphene oxide-cobalt ferrite nanocomposite for detection and quantification of vitamin B9 and antibacterial assessment

In this study, magnetic cobalt ferrite/graphene oxide (CoFe2O4/GO) sorbent was synthesized and used in magnetic dispersive micro-solid phase extraction (MD-µ-SPE) combined to spectrofluorometric as a green, precise, and effective method to separate and detect of vitamin B9 in food and biological samples. The structure, morphology and magnetic properties of CoFe2O4/GO nanocomposite was characterized by Fourier transform infrared (FT-IR) spectroscopy, X-Ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), and vibrating sample magnetometer (VSM). Four parameters of pH, sorbent dose, temperature, and sonication time were selected as effective variables on the process and optimized by central composite design (CCD). Under optimized conditions, the linear range, limit of detection, intra-day (repeatability) and inter-day (intermediate) precision were obtained 15–750 ng mL−1, 3.96 ng mL−1, 3.3–3.8%, and 4.2–4.9%, respectively. The material’s antimicrobial potency also evaluated, showcasing notable inhibitory action against both Gram-negative Escherichia coli bacteria, with a zone of inhibition measuring 17.5 mm at the highest concentration, and Gram-positive Staphylococcus aureus bacteria, which displayed a 14.5 mm ZIO at similar concentrations. To the best of our knowledge, this is the first report on using CoFe₂O₄/GO for vitamin B9 extraction and detection. This work introduces a novel, eco-friendly, and effective analytical platform that combines the magnetic properties of CoFe2O4 with the high surface area and functional groups of GO for targeted vitamin analysis. These results demonstrate that the synthesized nanocomposite possesses both excellent adsorption capability and antibacterial activity.

Optimizing Broiler Performance and Feed Cost Efficiency: Impact of 1,3-Diacylglycerol Supplementation at Different Energy Levels

We evaluated the effects of supplementing 1,3-diacylglycerol (1,3-DAG) in diets with different energy levels on the growth performance, nutrient digestibility, excreta scores, rectal temperature, meat quality, and blood parameters of broilers. A total of 576 one-day-old Ross 308 broilers (initial BW: 47.65 ± 0.51 g) were used in a 35-day feeding trial. The broilers were randomly assigned to four treatment groups (144 birds per group), with eight cages per group and 18 birds per cage, consisting of 9 males and 9 females. A 2 × 2 factorial design was employed, with two dietary energy levels (normal and reduced by 100 kcal/kg) with or without 0.075% 1,3-DAG supplementation. The results showed that compared with the diets without 1,3-DAG, the broilers receiving 1,3-DAG supplementation exhibited significantly greater body weight gain (BWG) and overall body weights (BWs) from days 10 to 35, along with a lower feed conversion ratio (FCR) (p < 0.05). In contrast, the low-energy diets without 1,3-DAG supplementation resulted in reduced growth performance, an increased FCR, higher drip loss, and lower total cholesterol levels. Notably, the rectal temperature and excreta scores were not affected by dietary energy levels or 1,3-DAG supplementation. In conclusion, while low-energy diets negatively impact growth and meat quality, 1,3-DAG supplementation enhances energy digestibility and growth performance, partially alleviating the adverse effects of reduced-energy diets and potentially lowering feed costs without compromising growth.

Advanced upcycling of spent electrolyte: Detoxification and selective alkene production via vacuum co-pyrolysis catalyzed by in-situ generated alkyl lithium-synergized ZSM-5.

Advanced upcycling of spent electrolyte: Detoxification and selective alkene production via vacuum co-pyrolysis catalyzed by in-situ generated alkyl lithium-synergized ZSM-5.

Assessment of Acute Toxicity, Behaviours and Water Parameter Correlations in Nile Tilapia (Oreochromis niloticus) Exposed to Iron Salts

The study determined the acute toxicity levels and behaviors of Nile tilapia after 96 h exposure to iron salts. It determined the significant correlations (Pearson’s r) between the water parameters, behaviors, and toxicity levels at varied time intervals and concentrations. This study adopted an experimental design framework, randomized triplicate sampling, and OECD Test Guideline No. 203. The acute toxicity test was done in a static method, behavioral clinical signs and opercular beats. Water parameters of pH, temperature, dissolved oxygen (DO), and total organic carbon (TOC) were measured using standard procedures of the OECD Test Guideline No. 203 and Association of Official Analytical Chemists (AOAC). The findings showed the lethal concentration (LC50) and lethal time (LT50) of total ferrous and ferric sulfate were 135.6 mg/L, 9.9 mg/L, and 340 h and 216 h, respectively, through probit analysis. Behaviors were abnormal bottom distribution, hypoventilation, abnormal pigmentation, and bioaccumulation of ferric ions. Opercular beats (OB) decreased as the concentration increased after 96-h exposure. Strong correlations (α = 0.05) were observed between concentration and the water parameters OB and pH in the ferric sulfate solutions, between pH, hardness, TOC, OB, and DO in ferrous sulfate solutions. The findings showed that ferric sulfate poses a greater risk than ferrous sulfate to test animals, implying its use and discharge should be limited in aquatic environments. The water parameters that significantly affected the toxicity levels must be monitored and regulated to mitigate potential adverse effects on aquatic ecosystems.

Actigraphy for quantitative evaluation of health

An analysis of large-scale studies utilizing actigraphy and wearable devices for continuous monitoring of physiological measures was conducted. Data had been obtained from the UK Biobank and the National Health and Nutrition Examination Survey (NHANES) for use in the studies. The link between circadian rhythm deviations and health status was investigated. The amplitude and phase of circadian rhythms, along with variability parameters of physical activity, skin temperature, and light exposure, were found to be informative markers of key health indicators, including morbidity, mortality, and life expectancy. These findings highlight the critical role of circadian markers – amplitude, phase, and variability – in predicting health risks related to metabolic and sleep disorders, emotional well-being, and cognitive function. By exploring nonlinear associations between chronotype and health risks, we propose that personalized adjustment of sleep-wake phase, based on light hygiene and physical activity regimes and taking into account age, sex, and genetic cofactors, may contribute to the advancement of health-preserving technologies and the extension of human lifespan.

Innovative materials and technologies in the production of sleep pillows

This article is devoted to developing and comprehensively evaluating innovative materials and technologies for pillows to optimize thermoregulation, moisture wicking, and barrier protection against allergens. The relevance of the work is determined by growing evidence of the influence of sleep quality on cardiovascular health and overall vitality, as well as the insufficiency of traditional fillings (memory foam, polyester fibers) in providing a comfortable and hypoallergenic microclimate. The study aims to establish a new paradigm for pillow design that combines phase-change materials, hydrogel and gel inserts, nanofiber membranes, antimicrobial nanocomposites, and embedded sensor systems. The novelty of the work lies in the cross-analysis of laboratory characteristics, heat-and-mass transfer modeling, and field trials, which made it possible to reveal synergistic effects of the combined use of adaptive thermal accumulators, highly conductive gels, and nanotechnological barriers. The main findings demonstrate that microencapsulated paraffin cores increase the dynamic heat capacity of polyurethane foam by 10–35-fold and accelerate heat transfer sevenfold; hydrogels raise the thermal conductivity of air-foam matrices by an order of magnitude; and electrospun nanofiber membranes provide allergen filtration with ≈ 99% efficiency without compromising air permeability. Antimicrobial coatings based on Ag and ZnO ions retain activity after multiple washes, thus breaking the “moisture → microbiome → allergen” chain. Integrating piezoelectric and PVDF sensors with machine-learning algorithms enables continuous monitoring and automatic adjustment of temperature, humidity, and respiratory parameters. This article will be helpful to bedding engineers, materials researchers, and manufacturers of “smart” sleep accessories.

DEVELOPMENT OF A SYSTEM FOR GENERATING HEAT AND COLD FOR THE NEEDS OF PHARMACEUTICAL COMPANIES

The article investigates the efficiency of using a heat pump to simultaneously generate heat and cold at a pharmaceutical enterprise in order to reduce energy costs. The proposed scheme, where the source of heat is a refrigeration network with constant parameters, ensures the stability of temperature conditions required for technological processes and product storage. The economic feasibility of using a heat pump in comparison with such a technology as freezing is analyzed at different periods of the day. Particular attention is paid to the automatic regulation of the system through the SCADA system, which minimizes the human factor and guarantees continuous maintenance of the required conditions. Taking into account the requirements of pharmaceutical production, including the need to comply with strict temperature parameters, a water-to-water heat pump was chosen. This scheme allows efficient use of energy between the cooling and heating systems, which ensures stable climate conditions in production and storage facilities. During periods of downtime, the heat pump maintains the required temperature in the refrigeration network, preventing the use of energy-intensive chillers. This solution increases energy efficiency, reduces operating costs, and meets the requirements of good manufacturing practice. Promising areas for further research include the development of algorithms for automatic control of heat pumps, as well as the integration of energy storage systems to improve efficiency under variable load conditions.

КЛИНИКО-ТОКСИКОЛОГИЧЕСКАЯ ХАРАКТЕРИСТИКА ОТРАВЛЕНИЙ ЭТИЛЕНГЛИКОЛЕМ И ДИЭТИЛЕНГЛИКОЛЕМ

This article is a comprehensive study of the toxicological properties and clinical manifestations of poisoning with ethylene glycol (EG) and diethylene glycol (DEG), industrially valuable but extremely dangerous polyatomic alcohols for humans. The work provides a detailed analysis of the physico-chemical characteristics of these compounds, including temperature parameters, solubility and other properties that determine both their industrial use and high toxicological hazard. Special attention is paid to the mechanisms of toxic action of ethylene glycol and diethylene glycol. The processes of metabolism of these substances in the human body are examined in detail, highlighting the formation of highly toxic metabolites (glycolic, glyoxylic, oxalic and 2-hydroxyethoxyacetic acids), which trigger a cascade of pathological reactions. The article describes in detail the three-stage clinical picture of intoxication, including the main symptoms characteristic of poisoning with ethylene glycol or diethylene glycol: metabolic acidosis, renal, liver failure and neurological disorders. The problem is becoming particularly relevant due to the widespread practice of using industrial liquids containing ethylene glycol and diethylene glycol as surrogate alcohol. The article emphasizes that the sweetish taste of these compounds and their relative availability create an extremely high risk of both accidental and intentional poisoning, especially among socially vulnerable segments of the population. It is pointed out that there is a need for an integrated approach to poisoning prevention, including increased control over the turnover of industrial liquids containing ethylene and diethylene glycol, active public awareness of the risks associated with the use of these substances, as well as strict compliance with safety measures when working with glycols.

Activation Volume During Creep of ASME Grade T91 Steel

Activation volume is an essential factor to determine the strain rate of martensitic steel, however it has not been discussed extensively. The activation volume during creep is analytically formulated as a function of average activated moving dislocation density (ΚρaX) inside a small region, e.g., a sub-grain, assuming the slip motion of dislocations. For Grade T91 steel, the activation volumes (V’s) for time to a specific creep strain and time to rupture are calculated by applying an exponential law to the temperature, stress, and time parameters during creep. The ΚρaX’s obtained using the calculated activation volumes are compared with the observed dislocation densities (Ρob’s). ΚΡaX at a strain of 0.2% is roughly 10% of the initial ρob, and the ratio of ΚρaX / ρob increases toward a value smaller than 1 at rupture because of recovery. These results and the strain energy consideration indicate that all dislocations inside a limited number of lath martensite begin to slip or a considerable number of packets remain undeformed immediately after loading. Subsequently, a large number of dislocations are accumulated on the boundaries of the concerned lath martensite, block, and packet, promoting the recovery and precipitation reactions around the crept area. Consequently, heterogeneity of deformation at the beginning of creep is mitigated gradually with progressing creep. The above documents coincide with a proposed model of the activation process for creep.

Iklim Kerja di PT Pelindo Terminal Petikemas TPK Bitung: Implikasi Terhadap Kesehatan dan Keselamatan Kerja

Work climate, particularly high ambient temperatures, significantly affects workers’ health and productivity. Extreme heat exposure may increase the risk of health issues, including cardiovascular diseases. This study aimed to assess the work climate conditions at PT Pelindo Terminal Petikemas TPK Bitung and evaluate the potential health risks for workers. This was a descriptive study using a quantitative approach. The variable measured was work climate, assessed using a Heat Stress Monitor during daytime shifts (08:20–19:40 WITA) with measurements taken every 40 minutes. Univariate analysis was used to describe the distribution of temperature parameters. The average work climate temperature was 29°C, with a minimum of 26°C and a maximum of 35.5°C. Based on the Indonesian Ministry of Manpower Regulation No. 5 of 2018, a Wet Bulb Globe Temperature (WBGT) of 29°C is within the safe threshold for light to moderate workloads but may exceed the Threshold Limit Value (TLV) under heavy physical exertion, especially under direct sunlight. The work climate at PT Pelindo Terminal Petikemas TPK Bitung presents varying levels of occupational health risk depending on the intensity and type of work performed. Preventive interventions are necessary to reduce the risk of heat stress.

Remote Sensing and GIS‐Based Study to Predict Risk Zones for Mosquito‐Borne Diseases in Cuttack District, Odisha, India

Tropical and sub‐tropical regions mostly provide favorable conditions for the spread of vector‐borne diseases especially those transmitted by mosquito vectors. Viral disease outbreaks such as dengue, chikungunya, Japanese encephalitis; and parasitic diseases such as malaria, and filariasis; are common health problems caused by mosquitos. According to the World Health Organization (WHO), globally around 40% of people are at high risk of mosquito‐borne diseases (MBD) (WHO Fact‐sheets, 2020, https://www.who.int/news‐room/fact‐sheets/detail/vector‐borne‐diseases). In the present study, Remote Sensing methods integrated with Geographic Information System (GIS) have been used to predict the risk zones for MBD in Cuttack, a district of Odisha, the eastern state of India. The findings of this study could be utilized to develop and implement MBD control and prevention strategies in identified high‐risk areas. Under this study, the Landsat‐8 multispectral temporal images from 2018 to 2021 were used to identify and demarcate the water‐logged areas and sites favorable for mosquito breeding. The goal is to identify risk zones for MBD using different indices such as Normalized Difference Water Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index, and Land Surface Temperature by evaluating water, moisture, vegetation, and temperature parameters. Applying Arc GIS software analysis models,we found 11,730.07 Ha. as a high‐risk zone, 28,053.99 Ha. as a medium‐risk zone, and 12,669.69 Ha. as a low‐risk zone. This study has the potential to enable informed decision‐making and proactive mosquito‐borne disease prevention.