Search Results (201)

This study explores the application of federated learning (FL) in security camera surveillance systems to overcome the structural limitations inherent in traditional centralized artificial intelligence (AI) training approaches, while simultaneously enhancing operational efficiency and data security. Conventional centralized AI models require the transmission of raw surveillance data from individual security camera units to a central server for model training, which poses significant challenges, including network congestion, a heightened risk of personal data leakage, and inadequate adaptation to localized environmental characteristics. These limitations are particularly critical in high-security environments such as military bases and government facilities, where reliability and real-time processing are paramount. In contrast, FL enables decentralized training by retaining data on local devices and sharing only model parameters with a central aggregator, thereby improving privacy preservation, reducing communication overhead, and facilitating adaptive, context-aware learning. This paper does not present a new federated learning algorithm or original experiment. Instead, it synthesizes existing research findings and applies the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical factors for deploying FL in surveillance systems. By combining literature-based evidence with structured expert judgment, this study provides practical guidelines for real-world application. This paper identifies four key performance metrics—detection accuracy, false alarm rate, response time, and network load—and conducts a comparative analysis of FL and centralized AI-based approaches in the recent literature. In addition, the AHP is employed to evaluate expert survey data, quantitatively prioritizing eight critical factors for effective FL implementation. The results highlight detection accuracy and data security as the most significant concerns, indicating that FL presents a promising solution for future smart surveillance infrastructures. This research contributes to the advancement of AI-powered surveillance systems that are both high-performing and resilient under stringent privacy and operational constraints. Full article

Regulatory changes related to the policy of reducing CO₂ emissions from natural gas are leading to an increase in the share of hydrogen in gas transmission and utilization systems. In this context, the impact of the change in composition on thermal radiation zones should be assessed for flaring during startups, scheduled shutdowns, maintenance, and emergency operations. Most existing models are calibrated for hydrocarbon flare gases. This study assesses how the CH₄–H₂ blends affect thermal radiation zones using a developed solver based on the Brzustowski–Sommer methodology with composition-dependent fraction of heat radiated (F) and range-dependent atmospheric transmissivity. Five blends, 0–50% (v/v) H₂, were analyzed for a 90 m stack at wind speeds of 3 and 5 m·s⁻¹. Comparisons were performed at constant molar (standard volumetric) throughput to isolate composition effects. Adding H2 contracted the radiation zones and reduced peak ground loads. Superposition analysis for a multi-flare layout indicated that replacing one 100% (v/v) CH₄ flare with a 10% (v/v) H₂ blend reduced peak ground radiation. Emission-factor analysis (energy basis) showed reductions of 3.24/3.45% at 10% (v/v) H₂ and 7.01/7.44% at 20% (v/v) H₂ (LHV/HHV); at 50% (v/v) H₂, the decrease reached 22.18/24.32%. Hydrogen blending provides coupled safety and emissions co-benefits, and the developed framework supports screening of flare designs and operating strategies as blends become more prevalent. Full article

Background: Hospitalized patients’ expectations about their treatment play a key role in therapeutic adherence, satisfaction with care, and clinical outcomes. However, there is a lack of brief, psychometrically validated instruments in Spanish-speaking contexts that adequately assess this construct. Objective: The objective of this study is to culturally adapt and validate the Hospitalized Patients’ Expectations for Treatment Scale-Patient Version (HOPE-P) in a Peruvian population. Methods: A methodological, cross-sectional study was conducted with 277 hospitalized patients aged 18 to 85 years (M = 45.87; SD = 17.09). The adaptation process included translation, back-translation, expert review, and pilot testing. Confirmatory factor analysis (CFA) was performed to assess the factor structure, and reliability and validity indices were calculated. Results: The bifactorial model showed good fit (CFI = 0.97, TLI = 0.94, RMSEA = 0.06). One item with a low factor loading was removed to improve the model. Convergent and discriminant validity were confirmed through acceptable values of Average Variance Extracted (0.60 and 0.55) and inter-factor correlation (φ² = 0.23). Internal consistency was strong for both dimensions (α = 0.76–0.77; ω = 0.76–0.77). Conclusions: The Spanish version of the HOPE-P is a valid, reliable, and culturally appropriate instrument for evaluating treatment expectations in hospitalized Peruvian patients. Its implementation in clinical settings could enhance physician–patient communication, support shared decision-making, and contribute to better therapeutic outcomes, especially in high-demand healthcare environments. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

Analyses of student mobility are typically conducted in an urban environment and are informed by socio-demographic or trip attributes. The prevailing focus is on individual modes of transport, different groups of commuters travelling to campus, students’ behavioural perceptions, and the totality of student trips. This paper starts with the identification of the determinants of student mobility that have received insufficient research attention. Utilising surveys, the study captures the mobility patterns of a sample of 1014 students and calculates their carbon footprint (CF; in kg/academic year) to assess whether the factors neglected in previous studies influence differences in the actual environmental load of student commuting. A regression analysis is employed to ascertain the significance of these factors as predictors of sustainable student mobility. This study exclusively focuses on the group of student commuters to campus and analyses the trips associated with compulsory activities at a suburban campus that is distant from the university centre and student facilities, which changes the mobility context in terms of commuting options. The under-researched factors identified in this research have not yet been quantified as CF. The findings confirm that only some of the factors neglected in previous research are statistically significant predictors of the local environmental load of student mobility. Specifically, variables such as student employment, frequency of class attendance, and propensity for ride-sharing could be utilised to forecast and regulate students’ mobility towards more sustainable patterns. However, all of the under-researched factors (including household size, region of origin (i.e., past experiences), residing at term-time accommodation while studying, and the availability of a family car) have an influence on the differences in CF magnitude in the studied campus. Full article

Objectives: Odontogenic sinusitis (ODS) is common but frequently overlooked condition that differs from rhinogenic sinusitis (CRS) and should be suspected in each case of unilateral sinusitis. Clinical symptoms such as foul smell, congestion, rhinorrhea, and unilateral maxillary sinus opacification with overt dental pathology on radiological scans are more suggestive of ODS than CRS, but the distinctive microbiological flora are another clinical factor in diagnosis. The aim of this study was to compare the microbiological load of ODS and CRS and their clinical presentation for better disease recognition and its predisposing factors. Methods: Adult patients scheduled for endoscopic sinus surgery were included in the study. Clinical data and radiological images were analyzed. The otolaryngologist assessed nasal endoscopy for mucopurulence or edema in middle meatus or sinuses, whereas dental specialist confirmed or ruled out the dental cause. Microbiological samples were collected after endoscopic maxillary antrostomy. After irrigation with 0,9% saline, the aspirated rinse was collected into sterile sets and sent for culturing. Results: The study group consisted of 84 patients, 55 with CRS and 29 with ODS. Streptococcus spp prevailed in the CRS group, whereas Staphylococcus spp prevailed in the ODS group. Statistically significant differences between the groups were found in type of discharge, degree of edema, and presence of polyps. However, no statistical correlations were noted for presence of bacteria in the culture and endoscopic or radiological findings. Conclusions: ODS and CRS share some common features: ODS more often presents with purulent discharge, localized maxillary involvement, and the presence of oral pathogens, and Staphylococcus spp in microbial profile. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

The increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO₂ footprint over the vehicles’ lifetime and improved resource efficiency through the recycling of high-voltage batteries. At the same time, the growing EV share presents challenges, such as ensuring sufficient power supply for the simultaneous charging of EVs within existing distribution grids. The scientific community has conducted numerous studies on the interaction between EVs and distribution grids, employing increasingly complex modeling techniques. However, the benefits of more complex modeling are rarely quantified. This study aims to address this gap by evaluating the impact of modeling complexity on transformer peak loads and busbar voltage for three communities with real-world distribution grid data. Since numerous stochastic factors influence EV charging patterns, this paper introduces a modular framework that accounts for the interconnection of these factors through microsimulation. The framework models charging events of battery electric vehicles (BEVs) and comprises modules for synthetic population generation, weekly mobility pattern assignment, and energy demand modeling based on vehicle class and ambient conditions. The findings reveal that cost-optimized charging strategies and seasonal factors, such as cold weather, have a significantly greater impact on the distribution grid than the detailed modeling of sociodemographic mobility patterns or detailed modeling of a diversified vehicle fleet. Full article

Robust dynamic equivalents of large power networks are essential for fast and reliable stability analysis of bulk power systems. This is because the dimensionality of modern power systems raises convergence issues in modern stability-analysis programs. However, even with modern computational power, it is challenging to find reduced-order models for power systems due to the following factors: the tedious mathematical analysis involved in the classical reduction techniques requires large amounts of computational power; inadequate information sharing between geographical areas prohibits the execution of model-dependent reduction techniques; and frequent fluctuations in the operating conditions (OPs) of power systems necessitate updates to reduced models. This paper focuses on a measurement-based approach that uses a deep artificial neural network (DNN) to estimate the dynamics of an external system (ES) of a power system, enabling stability analysis of a study system (SS). This DNN technique requires boundary measurements only between the SS and the ES. However, machine learning-based techniques like this DNN are known for their extensive training requirements. In particular, for power systems that undergo continuous fluctuations in operating conditions due to the use of renewable energy sources, the applications of this DNN technique are limited. To address this issue, a Deep Transfer Learning (DTL)-based technique is proposed in this paper. This approach accounts for variations in the OPs such as time-to-time variations in loads and intermittent power generation from wind and solar energy sources. The proposed technique adjusts the parameters of a pretrained DNN model to a new OP, leveraging symmetry in the balanced adaptation of model layers to maintain consistent dynamics across operating conditions. The experimental results were obtained by representing the Queensland (QLD) system in the simplified Australian 14 generator (AU14G) model as the SS and the rest of AU14G as the ES in five scenarios that represent changes to the OP caused by variations in loads and power generation. Full article

The market share of Last-Mile Delivery Robots (LMDRs) has grown rapidly over the past few years. These robots are mostly autonomous and supported remotely by human operators. As part of a broader shift toward sustainable urban logistics, LMDRs are seen as a promising low-emission alternative to conventional delivery vehicles. While there is a large body of literature about the technology, little is known about the real-world experiences of operating these robots. This study investigates the operational challenges faced by remote operators (ROs) of LMDRs, aiming to enhance their efficiency and safety. Through interviews with industry professionals, we explore the scenarios requiring human intervention, the strategies employed by ROs, and the unique challenges they encounter. Our findings not only identify key intervention scenarios but also provide a thorough examination of the teleoperation ecosystem, operational workflows, and how they affect the ways the ROs manage their interactions with robots. We found that ROs’ involvement varies from monitoring to active intervention to support the robots in completing their tasks when they face connectivity issues, blocked routes, and various other interruptions on their journeys. The findings highlight the importance of intuitive user interfaces (UIs) and decision-support systems to reduce cognitive load and improve situational awareness. This research contributes to the literature by offering a detailed examination of real-world teleoperation practices and focusing on the human factors influencing LMDR scalability, sustainability, and integration into future-ready logistics systems. Full article

Background: Compared to omnivorous diets, vegetarian diets are generally linked to improved body weight, blood pressure, lipid profiles, and glycemic control. Despite having the highest global prevalence of vegetarianism, South Asians in Canada exhibit disproportionately high rates of cardiovascular disease (CVD) and diabetes. This study examines the usual dietary intake and CVD risk factors among South Asian vegetarians and omnivores at risk of diabetes in British Columbia, Canada. Methods: Of a community sample of 100 South Asian adults at high risk of diabetes and recruited from 12 faith-based centers across the Metro Vancouver area, 96 completed the culturally adapted 163-item SHARE Food Frequency Questionnaire to determine their dietary status. CVD risk factors such as body mass index (BMI) and waist circumference (WC) were also assessed. The usual dietary intake metrics, including the total energy, macronutrient, and micronutrient consumption, were compared between vegetarians and omnivores. The associations between diet type, BMI, and WC were analyzed. Results: Of the 96 participants, 50 identified as vegetarians and 46 as omnivores. The mean age was similar between groups: 64.9 (±9.0) years for vegetarians and 65.6 (±10.5) years for omnivores. Females comprised a higher proportion of vegetarians (54.0% vs. 34.8%). Vegetarians reported a greater intake of carbohydrates and foods with a higher glycemic index and glycemic load. The fat intake was comparable between groups. Omnivores had a significantly higher intake of niacin, vitamin B-12, potassium, and zinc. Both groups exceeded the recommended sodium intake. Overall, 90.6% of the participants were classified as overweight or obese, with no significant association between vegetarianism and reduced adiposity. Conclusions: Both dietary groups exhibited an increased prevalence of overweight and obesity and had nutritional profiles that may be associated with elevated cardiometabolic risk. Factors such as dietary acculturation and a suboptimal diet quality may underlie these findings. Culturally tailored nutritional interventions are warranted to address the specific needs of South Asian Canadian communities. Full article

Background. Despite the high transmissibility of SARS-CoV-2, some individuals remain uninfected despite prolonged exposure to a high viral load, suggesting the involvement of an innate resistance mechanism, possibly underpinned by the host’s genetic factors. The angiotensin-converting enzyme-1 (ACE1), ACE2, and C-C Chemokine Receptor 5 (CCR5) polymorphisms have been shown to influence susceptibility to the infection. In this study, the role of ACE1, ACE2, and CCR5 gene polymorphisms in modulating susceptibility to SARS-CoV-2 infection within the context of intimate contact was evaluated. Methods. A cohort of heterosexual couples from Northern Sardinia, characterized by a homogenous genetic background, was recruited during the initial pandemic wave (March–June 2020). In each couple, one partner (index case) tested positive for SARS-CoV-2 by at least two consecutive independent molecular tests (real-time polymerase chain reaction: RT-PCR) on nasopharyngeal swabs. Bed-sharing partners of SARS-CoV-2 positive index cases, resistant and susceptible to the infection, were genotyped for ACE1 287 bp Alu repeat insertion/deletion (I/D) polymorphism, ACE2 G8790A (rs2285666) variant, and a 32-base pair deletion (Δ32) of CCR5. Resistant and susceptible partners to the infection were compared for polymorphisms. Results. Out of 63 couples, 30 partners acquired SARS-CoV-2 infection, while 33 remained uninfected despite intimate exposure. Clinical history was minimal for current or past illnesses. SARS-CoV-2-infected index spouses and partners who acquired the infection developed a mild disease, not requiring hospitalization. The observed distribution of ACE1 I/D and ACE2 G8790A genotypes was consistent with previously reported frequencies in Sardinia and across European populations. None of the study participants carried the CCR5-Δ32 variant. No statistically significant differences (p > 0.05) in the allelic or genotypic frequencies of these polymorphisms were observed between the infected and resistant partners. Conclusions. No differences in the distribution of ACE1, ACE2, and CCR5 polymorphisms between the two groups were detected. These findings suggest that resistance is likely multifactorial, involving a complex interplay of genetic, immunological, and environmental factors. Full article

The strength and conditioning literature examining neuromuscular physiology, bioenergetics, neuroendocrine factors, nutrition and metabolic factors, and the use of ergogenic aids, as well as physical and physiological responses and adaptations, have clearly identified the benefits of participating in regular resistance training programs for athletic populations, especially as it relates to improving muscular strength. Beyond evidence-based research, models for resistance training program implementation are of considerable value for optimizing athletic performance. In fact, several have been provided that address general to specific characteristics of athleticism (i.e., strength endurance, muscular strength, and muscular power) through resistance training over the decades. For instance, a published model known as the strength–endurance continuum that enhances dynamic correspondence (i.e., training specificity) in athletic populations by developing structural, metabolic, and neural capacities across a high-load, low-repetition and low-load, high-repetition range. Further models have been developed to enhance performance approaches (i.e., optimum performance training model) and outcomes (i.e., performance pyramid), even within specific populations, such as youth (i.e., youth physical development model). The ten, five, three, or 10-5-3 (TFT) model for strength and conditioning professionals synthesizes currently available information and provides a framework for the effective implementation of resistance training approaches to suit the needs of athletes at each stage of development. The model includes three key components to consider when designing strength and conditioning programs, denoted by the acronym TFT (ten, five, three). Over recent years, the model has gained much support from teams, coaches, and athletes, mainly due to the ability to streamline common knowledge within the field into an efficient and effective resistance training system. Furthermore, this model is distinctly unique from others as it prioritizes the development of strength–endurance, muscular strength, and muscular power concurrently. This paper explains the model itself and begins to provide recommendations for those interested in implementing TFT-based approaches, including a summary of points as a brief take-home guide to implementing TFT interventions. It is the author’s hope that this paper encourages other performance professionals to share their models to appreciate human ingenuity and advance our understanding of individualized approaches and systems towards the physical development of the modern-day athlete. Full article

Renewable energy and voltage source inverter-driven microgrids generally lack natural inertia to provide transient energy support during sudden load demands. To address this, the virtual synchronous generator (VSG) is a state-of-the-art control technique applied in power controllers to emulate virtual inertia during sudden load changes. This allows for stable power delivery from the source to the loads during sudden active power load demands. However, in systems with large inductively dominant load demands, conventional VSG-based power controllers may exhibit a delayed reactive power response due to their inertia-emulating characteristics, potentially affecting the overall power-sharing performance. To address this limitation of VSG control, this paper proposes an advanced control scheme in which the VSG is supported by appropriately designed voltage and current controllers. Conventionally, classical tuning techniques are used to design the controllers in the forward paths of the voltage and current controllers (CVAs). Thus, the conventional control scheme is a combination of a VSG and CVAs. Recently, a hybrid modified pole-zero cancellation technique has been discussed in the literature for the design of voltage and current controllers (HVAs) to improve the vector control of the inverter. This method supports better tuning for controllers of both forward and cross-coupling paths. Therefore, to improve the power delivery with VSG-based control when subjected to inductive load changes, this paper proposes an advanced control scheme that is based on the combination of VSG and HVA. The performance of both conventional and proposed control schemes is verified through simulation in MATLAB/Simulink under two different test load conditions, namely good and poor power factor loadings. Based on the results obtained during these test cases, the response and power delivery capability of the proposed control scheme is comparable with that of the conventional control scheme. The results verify that the power delivery capability of the microgrid with the proposed control scheme is improved by 25% compared to the conventional control scheme. Full article

Permeable concrete piles, which combine the advantages of rigid piles and drainage consolidation techniques, have been widely applied in soil foundation treatment. In this study, the optimal mix proportion of the permeable concrete pile material was first determined through laboratory experiments; subsequently, based on the experimental results, numerical simulations were employed to investigate the load-bearing characteristics of composite foundations reinforced with permeable concrete piles under applied loads. The experimental results indicate that when the designed porosity is set between 20% and 35%, and the water-to-cement ratio is 0.3, the actual porosity closely approximates the design value, achieving a favorable balance between compressive strength and permeability. Numerical simulation results reveal that as the axial force in the permeable concrete piles attenuates with depth, the side friction of piles exhibits an overall increasing trend. Compared with impermeable piles, the pile–soil stress ratio and the load-sharing ratio of permeable piles gradually decrease under high loads; furthermore, the settlement and pile–soil stress ratio of the composite foundation is significantly influenced by factors such as pile length, pile diameter, cushion modulus, inter-pile soil modulus, and the modulus of the pile material. Full article