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This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond. Full article

The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management. Full article

This research has developed a heat stroke warning system prototype for athletes utilizing the following sensors: DHT22, GY-906-BAA MLX90614, MAX30102. The device calculates the heat stroke risk and notifies users. The data is recorded, stored, displayed on a free-access website which graphs body temperature, ambient temperature, humidity, heart rate and heat stroke risk, and provides notifications for athletes engaged in outdoor activities. The researchers recorded sensors data (n = 1) for two sessions (12 min/session) in a closed room, at the sixth-minute marker, with an air conditioner activated to observe the changes observed by the sensors. For accuracy, the researchers employed Criterion-Related Validity, comparing sensor against standard equipment measurement. For reliability, we utilized Test-Retest Reliability, comparing sensor data from the first and second measurements. Accuracy and reliability were evaluated using the Pearson Correlation Coefficient, with significance set at p < 0.01. The DHT22 sensor demonstrates very high accuracy (r = 0.923) in ambient temperature and (r = 0.774) humidity measurements. It showed no significant reliability (r = 0.489) in temperature and (r = 0.185) humidity measurements. The GY-906-BAA MLX90614 sensor exhibited very high accuracy (r = 0.923) and reliability (r = 0.866) in body temperature measurements. The MAX30102 sensor lacked significant accuracy (r = 0.179) and reliability (r = 0.171) in heart rate measurements. The development of accuracy and reliability of sensors are important for preventing heat stroke in future applications. Full article

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms. Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips. Full article

Message exchange for acknowledgment of receipt (e.g., for services that provide recipients with certain documents, such as sanctions, summonses, or requirements, which are often sent by public administrations) has traditionally been provided through protocols based on the use of trusted third parties (TTPs). More recently, solutions have been proposed that totally or partially replace the TTP with a blockchain. However, an analysis of these proposals reveals that they have one or more of the following drawbacks: failure to meet confidentiality requirements, the use of TTPs, and susceptibility to attacks that make them unfair. In this paper, we present a method of providing fair delivery for receipts based on a blockchain that does not suffer from the above disadvantages: it is fair and confidential and does not use a TTP. In addition, we demonstrate the feasibility of the protocol through a cost analysis and a proof-of-concept implementation, showing that our solution is more economical than existing solutions, and can be considered cost-effective even under low time requirements. Full article

Count data often exhibit discrepancies in the frequencies of zeros, which commonly occur across various application domains. These data may include excess zeros (zero inflation) or, less frequently, a scarcity of zeros (zero deflation). In regression models, both situations can arise at different levels of covariates. The zero-modified power series regression model provides an effective framework for modeling such count data, as it does not require prior knowledge of the type of zero modification, whether zero inflation or zero deflation, and can accommodate overdispersion, equidispersion, or underdispersion present in the data. This paper proposes a Bayesian estimation procedure based on the stochastic gradient Hamiltonian Monte Carlo algorithm, effectively addressing many challenges associated with estimating the model parameters. Additionally, we introduce a measure of Bayesian efficiency to evaluate the impact of prior information on parameter estimation. The practical utility of the proposed method is demonstrated through both simulated and real data across different types of zero modification. Full article

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds. Full article

Background/Objectives: Evidence suggests that older adults who survived COVID-19 were exposed to greater functional dependence in their daily living activities. This study aims to examine the prevalence of functional dependence and associated factors among Brazilian older people with functional dependence 12 months after COVID-19 infection. Methods: A cross-sectional study was carried out involving people aged 60 years or older in the state of Paraná, Brazil. One year after notification or hospital discharge due to COVID-19, between June 2021 and March 2022, participants responded to a questionnaire via telephone call about sociodemographic data and data on functionality using the Measure of Functional Independence (FIM). The outcome variable “assessment of functional capacity” was divided into functional dependence (FIM Total < 104) and functional independence (FIM Total ≥ 104). Results: A total of 768 older adults participated, with an average age of 68.03 ± 6.8 years (range between 60 and 100). A majority of them were female (50.3%), white (46%), with low education (37.4%), had a partner (56.3%), did not live alone (72.4%), and had their own home (52.2%). The prevalence of functional dependence was 7.2%. On average, participants scored 5.4 points lower on FIM one year after COVID-19 infection compared with those in the acute phase of COVID-19 (125.5 vs. 120.1; p < 0.001). Functional dependence was higher (p < 0.05) among women when compared to men (aOR = 2.28); in people who changed their work situation due to COVID-19 when compared to those with no change (aOR = 5.27); in people with fair/poor/bad self-reported health compared to those with excellent/good health (aOR = 2.97); in people with cardiovascular symptoms compared to those without cardiovascular symptoms (aOR = 3.37); and among the most severe cases of the disease (treatment in ICU) compared to mild cases (outpatient treatment) (aOR = 10.5). Conclusions: Most participants presented functional independence 12 months after COVID-19 infection. Cases of functional dependence were influenced by multidimensional factors, including physical health, economic, and psychosocial aspects. Full article

Background: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. This study aimed to map the spatial distribution of DR-TB treatment outcomes at a local level and identify sociodemographic and environmental factors associated with poor treatment outcomes in Hunan Province, China. Methods: A spatial analysis was conducted using DR-TB data from the Tuberculosis Control Institute of Hunan Province, covering the years 2013 to 2018. The outcome variable, the proportion of poor treatment outcomes, was defined as a composite measure of treatment failure, death, and loss to follow-up. Sociodemographic, economic, healthcare, and environmental variables were obtained from various sources, including the WorldClim database, the Malaria Atlas Project, and the Hunan Bureau of Statistics. These covariates were linked to a map of Hunan Province and DR-TB notification data using R software version 4.4.0. The spatial clustering of poor treatment outcomes was analyzed using the local Moran’s I and Getis–Ord statistics. A Bayesian logistic regression model was fitted, with the posterior parameters estimated using integrated nested Laplace approximation (INLA). Results: In total, 1381 DR-TB patients were included in the analysis. An overall upward trend in poor DR-TB treatment outcomes was observed, peaking at 14.75% in 2018. Deaths and treatment failures fluctuated over the years, with a notable increase in deaths from 2016 to 2018, while the proportion of patients lost to follow-up significantly declined from 2014 to 2018. The overall proportion of poor treatment outcomes was 9.99% (95% credible interval (CI): 8.46% to 11.70%), with substantial spatial clustering, particularly in Anxiang (50%), Anren (50%), and Chaling (42.86%) counties. The proportion of city-level indicators was significantly associated with higher proportions of poor treatment outcomes (odds ratio (OR): 1.011; 95% CRI: 1.20 December 2024 001–1.035). Conclusions: This study found a concerning increase in poor DR-TB treatment outcomes in Hunan Province, particularly in certain high-risk areas. Targeted public health interventions, including enhanced surveillance, focused healthcare initiatives, and treatment programs, are essential to improve treatment success. Full article

Smartphone push notifications aim to provide time-sensitive information to their users. However, notifications are often transmitted in ill-timed situations, causing users to be interrupted, annoyed, and stressed. This ultimately affects the overall notification experience as it does not consider the external contexts the users are situated in. This study aims to shed light on how users manage notifications in their daily lives and how they perceive the experience as a whole. A total of 28 participants took part in a 5-day mixed-method diary study, which logged a total of 135 entries. Based on this, six types of characteristics emerged. These characteristics were formed from the combination of three main categories: notification related, day related, and user related. The findings of this study highlight implementing different strategies for each type of characteristic to mitigate the adverse effects notifications have on users. Full article

Monitoring natural phenomena using a variety of methods to predict disasters is a trend that is growing over time. However, there is a great disunity among methods and means of data analysis, formats and interfaces of storing and providing data, and software and information systems for data processing. As part of a large project to create a planetary observatory that combines data from spatially distributed geosphere monitoring systems, the efforts of leading institutes of the Russian Academy of Sciences are also aimed at creating an information and computing ecosystem to unite researchers processing and analyzing the data obtained. This article provides a brief overview of the current state of publications on information ecosystems in various applied fields, and it also proposes a concept for an ecosystem on a multiagent basis with unique technical features. The concept of the ecosystem includes the following: the ability to function in a heterogeneous environment on federal principles, the parallelization of data processing between agents using Petri nets as a mechanism ensuring the correct execution of data processing scenarios, the concept of georeferenced alarm events requiring ecosystem reactions and possible notification of responsible persons, and multilevel information protection allowing data owners to control access at each stage of information processing. Full article

Plastic particles, including microplastics, are increasingly common contaminants of the food chain, raising concerns over human health effects. The objective of this work was to contribute to a better understanding of their presence in food and feed based on notifications of plastic foreign bodies in the Rapid Alert System for Food and Feed (RASFF) of the European Union. Visible plastics accounted for 25 notifications per year from 2020 to 2023 (four years), becoming the third most common foreign body after glass and metal. Contamination is likely to originate during processing and packaging. Even though these results confirm the presence of plastics in the European food chain, notifications provide limited information and only visible particles may be reported. Regulations must establish active monitoring and limits for plastic particles in foods and feeds (e.g., in an amendment to Commission Regulation (EC) no. 1881/2006), including for smaller particle sizes (i.e., microplastics). However, the establishment of regulations is limited by knowledge gaps in analytical methods, foodstuff contamination, and toxicity. Research studies should prioritize knowledge gaps needed to support regulatory action and, ultimately, human health protection. Full article

Agricultural loss due to the overpopulation of Sika deer poses a significant challenge in Japan, leading to frequent human–wildlife conflicts. We conducted a study in Muroran, Hokkaido (42°22′56.1″ N–141°01′51.5″ E), with the objective of monitoring Sika deer and notifying farmers and locals. We deployed a Sika deer detection model (YOLOv8-nano) on a Raspberry Pi, integrated with an infrared camera that captured images only when a PIR sensor was triggered. To further understand the timing of Sika deer visits and potential correlations with environmental temperature and humidity, respective sensors were installed on Raspberry Pi and the data were analyzed using an ANOVA test. In addition, a buzzer was deployed to deter Sika deer from the study area. The buzzer was deactivated in the first 10 days after deployment and was activated in the following 20 days. The Sika deer detection model demonstrated excellent performance, with precision and recall values approaching 1, and a bounding box creation latency of 0.82 frames per second. Once a bounding box was established after Sika deer detection, alert notifications were automatically sent via email and the LINE messaging application, with an average notification time of 0.32 s. Regarding the buzzer’s impact on Sika deer, 35% of the detected individuals reacted by standing upright with alert ears, while 65% immediately fled the area. Analysis revealed that the time of day for Sika deer visits was significantly correlated with humidity (F = 8.95, p < 0.05), but no significant association with temperature (F = 0.681, p > 0.05). These findings represent a significant step toward mitigating human–wildlife conflicts and reducing agricultural production losses through effective conservation measures. Full article