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Background: It has been more than a decade since fusion prostate biopsy (FPB) has been used in the diagnosis of prostate cancer (PCa). Therefore, patients with a previous history of negative FPB and ongoing suspicion of PCa are beginning to emerge. This study investigated whether the first biopsy type (standard or fusion) should be effective in deciding on a second biopsy. Methods: Male patients aged 40–75, with a serum prostate-specific antigen (PSA) value of less than 10 ng/mL and a negative biopsy history within the last 24 months, who underwent FPB in our clinic due to persistent PSA elevation and/or suspicious multiparametric prostate magnetic resonance imaging (MpMRI) findings were included to the study. Patients were divided into groups according to the type of first biopsy (Group 1; those whose first biopsy was FPB, Group 2; those whose first biopsy was standard prostate biopsy). Some demographic and clinical data of the groups, as well as PCa detection rates, were compared. A p value of less than 0.05 was considered statistically significant. Results: A total of 275 patients (Group 1: 84, Group 2: 191) were included in this study. The groups were similar in terms of age, PSA values before the first biopsy, PSA values before the second biopsy, family history of PCa, and prostate volume. PCa was detected at a higher rate in Group 2 than Group 1 (23% vs 15.5%, p = 0.044). Concluison: The data obtained from this study indicate that the type of initial biopsy should be taken into account when deciding on FPB in secondary patients with a previous negative biopsy history.

In this paper we propose a new paradigm for encoding the geometry of dense point cloud sequences, where a convolutional neural network (CNN), which estimates the encoding distributions, is optimized on several frames of the sequence to be compressed. We adopt lightweight CNN structures, we perform training as part of the encoding process and the CNN parameters are transmitted as part of the bitstream. The newly proposed encoding scheme operates on the octree representation for each point cloud, consecutively encoding each octree resolution level. At every octree resolution level, the voxel grid is traversed section-by-section (each section being perpendicular to a selected coordinate axis), and in each section, the occupancies of groups of two-by-two voxels are encoded at once in a single arithmetic coding operation. A context for the conditional encoding distribution is defined for each two-by-two group of voxels based on the information available about the occupancy of the neighboring voxels in the current and lower resolution layers of the octree. The CNN estimates the probability mass functions of the occupancy patterns of all the voxel groups from one section in four phases. In each new phase, the contexts are updated with the occupancies encoded in the previous phase, and each phase estimates the probabilities in parallel, providing a reasonable trade-off between the parallelism of the processing and the informativeness of the contexts. The CNN training time is comparable to the time spent in the remaining encoding steps, leading to competitive overall encoding times. The bitrates and encoding-decoding times compare favorably with those of recently published compression schemes.

To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about choosing the middle category or extreme categories are largely determined by midpoint RS (MRS) and extreme RS (ERS). One limitation of traditional IRTree models is the assumption that all respondents utilize the same set of RS in their response strategies, whereas it can be assumed that the nature and the strength of RS effects can differ between individuals. To address this limitation, we propose a mixture multidimensional IRTree (MM-IRTree) model that detects heterogeneity in response strategies. The MM-IRTree model comprises four latent classes of respondents, each associated with a different set of RS traits in addition to the substantive trait. More specifically, the class-specific response strategies involve (1) only ERS in the "ERS only" class, (2) only MRS in the "MRS only" class, (3) both ERS and MRS in the "2RS" class, and (4) neither ERS nor MRS in the "0RS" class. In a simulation study, we showed that the MM-IRTree model performed well in recovering model parameters and class memberships, whereas the traditional IRTree approach showed poor performance if the population includes a mixture of response strategies. In an application to empirical data, the MM-IRTree model revealed distinct classes with noticeable class sizes, suggesting that respondents indeed utilize different response strategies.

This paper is the first part of a two-part series in which we present results from one of the first worldwide experimental demonstrations of frequency regulation in a commercial building test facility. We demonstrate that commercial buildings can track a frequency regulation signal with high accuracy and minimal occupant discomfort in a realistic environment. In addition, we show that buildings can determine the reserve capacity and baseline power a priori, and identify the optimal tradeoff between frequency regulation and energy efficiency. In part I, we introduce the test facility and develop relevant building models. Furthermore, we design a hierarchical controller for the heating, ventilation, and air conditioning system that consists of three levels: 1) a reserve scheduler; 2) a building climate controller; and 3) a fan speed controller for frequency regulation. We formulate the reserve scheduler as a robust optimization problem and introduce several approximations to reduce its complexity. The building climate controller is comprised of a robust model predictive controller and a Kalman filter. The frequency regulation controller consists of a feedback and a feedforward loop, provides fast responses, and is stable. Part I presents building model identification and controller tuning results. Specifically, we find out that with an appropriate formulation of the model identification problem, a two-state model is accurate enough for use in a reserve scheduler that runs day-ahead. In part II, we report results from the operation of the hierarchical controller under frequency regulation.

This paper is the second part of a two-part series presenting the results from an experimental demonstration of frequency regulation in a commercial building test facility. In part I, we developed relevant building models and designed a hierarchical controller for reserve scheduling, building climate control, and frequency regulation. In part II, we introduce the communication architecture and experiment settings, and present extensive experimental results under frequency regulation. More specifically, we compute the day-ahead reserve capacity of the test facility under different assumptions and conditions. Furthermore, we demonstrate the ability of model predictive control to satisfy comfort constraints under frequency regulation, and show that fan speed control can track the fast-moving RegD signal of the Pennsylvania, Jersey, and Maryland power market very accurately. In addition, we discuss potential effects of frequency regulation on building operation (e.g., increase in energy consumption, oscillations in supply air temperature, and effect on chiller cycling), and provide suggestions for real-world implementation projects. Our results show that hierarchical control is appropriate for frequency regulation from commercial buildings.

To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about choosing the middle category or extreme categories are largely determined by midpoint RS (MRS) and extreme RS (ERS). One limitation of traditional IRTree models is the assumption that all respondents utilize the same set of RS in their response strategies, whereas it can be assumed that the nature and the strength of RS effects can differ between individuals. To address this limitation, we propose a mixture multidimensional IRTree (MM-IRTree) model that detects heterogeneity in response strategies. The MM-IRTree model comprises four latent classes of respondents, each associated with a different set of RS traits in addition to the substantive trait. More specifically, the class-specific response strategies involve (1) only ERS in the "ERS only" class, (2) only MRS in the "MRS only" class, (3) both ERS and MRS in the "2RS" class, and (4) neither ERS nor MRS in the "0RS" class. In a simulation study, we showed that the MM-IRTree model performed well in recovering model parameters and class memberships, whereas the traditional IRTree approach showed poor performance if the population includes a mixture of response strategies. In an application to empirical data, the MM-IRTree model revealed distinct classes with noticeable class sizes, suggesting that respondents indeed utilize different response strategies. [ABSTRACT FROM AUTHOR]

This paper presents a novel demand response estimation framework for residential and commercial buildings using a combination of EnergyPlus and two-state models for thermostatically controlled loads. Specifically, EnergyPlus models for commercial and multi-dwelling residential units are applied to construct exhaustive datasets (i.e., with more than 300M data points) that capture the detailed load response and complex thermodynamics of several building types. Subsequently, regression models are fit to each dataset to predict DR potential based on key inputs, including hour of day, set point change and outside air temperature. For single residential units, and residential thermostatically controlled loads (i.e. water heaters and refrigerators) a two-state model from the literature is applied. The proposed framework is then validated for commercial buildings through a comparison with a dataset composed of 11 buildings during 12 demand response events. In addition, the use of the proposed simplified DR estimation framework is presented in terms of two cases (1) peak load shed prediction in an individual building and (2) aggregated DR up/down capacity from a large-scale group of different buildings.

Cosimulation platforms are necessary to study the interactions of complex systems integrated in future smart grids. The Virtual Grid Integration Laboratory (VirGIL) is a modular cosimulation platform designed to study interactions between demand-response (DR) strategies, building comfort, communication networks, and power system operation. This paper presents the coupling of power systems, buildings, communications, and control under a master algorithm. There are two objectives: first, to use a modular architecture for VirGIL, based on the functional mockup interface (FMI), where several different modules can be added, exchanged, and tested; and second, to use a commercial power system simulation platform, familiar to power system operators, such as DIgSILENT PowerFactory. This will help reduce the barriers to the industry for adopting such platforms, investigate and subsequently deploy DR strategies in their daily operation. VirGIL further introduces the integration of the quantized state system (QSS) methods for simulation in this cosimulation platform. Results on how these systems interact using a real network and consumption data are also presented.

In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 37% (median) when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25. kW. h (~2.8%) of energy per non-residential EV charging session from peak periods (12. PM-6. PM) to off-peak periods (after 6. PM) in Northern California for the year of 2013.

Although the detrimental ecological and economic effects of introducing freshwater fish species have been extensively documented, non-native freshwater fishes continue to be introduced in large numbers globally to enhance fisheries and aquaculture. In Turkey, stocking of common carp Cyprinus carpio is practised to provide food security for people and job security for artisanal fishers, resulting in a country-wide distribution. These stockings, however, increase the risk of accidental introductions and have led to introductions and subsequent invasions of gibel carp Carassius gibelio, a globally invasive and highly detrimental fish species. Here, we assessed the growth types, body conditions and trophic interactions via bulk carbon and nitrogen stable isotope analysis of common and gibel carp in both natural and artificial water bodies in Turkey. The results indicated that common and gibel carp express similar growth types and body conditions in all waters and have similar trophic ecologies. This leads to substantial trophic niche overlaps in waters where they co-occur, with the potential for strong interspecific competition. Considering the ongoing stocking of common carp for fishery enhancement, we recommend to specifically target these stockings in waters where gibel carp has already become invasive. Our findings, indeed, suggest that common carp releases have the potential to hamper invasive gibel carp populations by increasing the competitive interactions between the two species.

Abstract Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at Düzce University in Türkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

Abstract We have previously demonstrated that the intracellular, non-GPI anchored CD59 isoforms IRIS-1 and IRIS-2 (Isoforms Rescuing Insulin Secretion 1 and 2) are necessary for insulin secretion from pancreatic β-cells. While investigating their expression across human tissues, we identified IRIS-1 and IRIS-2 mRNA in the human brain, though their protein expression and function remained unclear. This study shows the presence of both IRIS-1 and 2 proteins in the human brain, specifically in neurons and astrocytes. In the neuroblastoma cell line (SH-SY5Y), both isoforms are intracellular, and their expression increases upon differentiation into mature neurons. Silencing IRIS-1 and 2 in SH-SY5Y cells reduces the SNARE complex formation, essential for synaptic vesicle exocytosis, leading to a reduction in noradrenaline secretion. Notably, we observed diminished expression of neuronal IRIS-1 and 2 in patients with Alzheimer’s disease (AD) and non-demented individuals with type 2 diabetes (T2D). In SH-SY5Y cells, knockdown of all isoforms of CD59 including IRIS-1 and 2 not only elevates phosphorylated tau but also increases cyclin-dependent kinase 5 (CDK5) expression, known promoter of hyperphosphorylation and accumulation of tau, a key pathological feature of AD. Additionally, we found that prolonged exposure to high glucose or cytokines markedly reduces the expression of IRIS-1 and 2 in SH-SY5Y cells, suggesting a link between AD pathology and metabolic stress through modulation of these isoforms.

Abstract In this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance‐constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance‐constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

Abstract The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon’s test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.

Bu çalışma, tasavvuf düşüncesinde önemli bir yere sahip olan sefer kavramını, sûfîlerin manevî eğitim yöntemi olarak bu kavramı nasıl değerlendirdiğini ve bu yöntemin tasavvufî eğitimdeki yerini incelemektedir. Hicrî 6. asra kadar olan dönemde sûfîlerin yazılı eserlerine dayanarak seferin nefis terbiyesi, içsel dönüşüm ve Allah’a yakınlaşma amacı taşıyan bir yöntem olarak tasavvufta nasıl bir rol oynadığı analiz edilmiştir. Sefer, sûfîler tarafından bir yolculuk şeklinde ele alınmakla birlikte yalnızca fiziksel değil, aynı zamanda içsel bir dönüşüm aracı olarak da görülmüştür. Çalışmada seferin, nefis terbiyesi, manevî yükseliş ve Allah’a yakınlaşma amacı taşıyan bir yöntem olarak değerlendirilmesi, sûfîlerin bu yolculuğa atfettiği farklı anlamlar ve uygulama biçimleri analiz edilmektedir. Araştırmanın kapsamı, sefer kavramının tasavvuf tarihinde hicrî 6. yüzyıla kadar örneklem kabul ettiğimiz sûfî-müelliflerden hareketle geçirdiği dönüşümleri ve sûfîler arasında seferin kabul görme veya reddedilme gerekçelerini incelemektedir. Örneklem olarak seçilen müellifler tasavvuf klasiklerini yazan sûfîlerden oluşmaktadır. Çalışmada, tasavvuf tarihinde maddî ve manevî olarak sefer ayrımı olduğu, maddî seferlerin ilmî yolculuk, ibadet, şeyh ve âlim ziyaretleri gibi pratiklerde; manevî seferin ise seyr ü sülûk olarak adlandırılan nefisle mücadele ve içsel arınma süreçlerinde olduğu gözlemlenmiştir. Çalışmanın sonucunda seferin, sûfîlerin bireysel gelişimde ve nefis terbiyesinde sistematik bir yöntem olarak kabul edildiği; bununla birlikte sûfîler arasında yöntemin zorunluluğu konusunda farklı yaklaşımların bulunduğu ortaya konulmaktadır.

İslam tarihi boyunca yaşanmış en trajik olaylardan biri şüphesiz Kerbelâ faciasıdır. Hz. Hüseyin ve aile fertlerinin Kerbela'da Emevi güçleri tarafından katledilmesi, sadece yaşandığı dönemde değil, sonrasında da derin izler bırakan bir trajedi olarak tarihe geçmiştir. Bu elim olay, İslam dünyasında büyük bir yankı uyandırmış ve siyasi ve dini açıdan önemli sonuçlara yol açmıştır. Kerbelâ hadisesinin her aşamasında Mezhicoğulları'nın faaliyetleri dikkat çekici bir şekilde ön plana çıkmıştır. Bu faaliyetler, Kerbelâ olayının anlaşılması açısından büyük önem taşımaktadır. Kerbelâ yolculuğunun başlangıcında, Kûfe'den gelen ve onu şehre davet eden mektuplar önemli bir rol oynamıştır. Mezhicoğulları bu mektuplardan bazılarını gönderenler arasında yer almışlardır. Mezhicoğulları'nın Hz. Hüseyin'e olan bağlılıkları ve onu Kûfe'ye davet etmeleri, ilk bakışta samimi bir destek ve bağlılık göstergesi olarak yorumlanabilir. Fakat Kerbelâ'ya yaklaştıkça ortaya çıkan tablo, beklenmedik bir çelişkiyi gözler önüne sermektedir. Mezhicoğulları, Hz. Hüseyin'e olan desteğinden geri adım atmış ve onu Emevi ordusunun tehdidiyle karşı karşıya bırakmıştır. Bu çelişkili tutumun arkasında yatan nedenler tam olarak bilinmemekle birlikte, muhtemel bazı faktörler bulunmaktadır. Kûfelilerin desteğini alamama endişesi, Emevi valisinin baskısı ve tehdidi, Hz. Hüseyin'in zafer kazanacağına dair umutsuzluk gibi unsurlar, Mezhicoğulları'nın tavrını etkilemiş olabilir. Bu davetin önemli bir kısmını oluşturan ve Hz. Hüseyin'i Kûfe'ye çağıran kişilerden bazılarının Mezhicoğulları'na mensup olması, bu kabileyi olayın merkezine taşımaktadır. Bu durum, Mezhicoğulları'nı Kerbela olayında sadece bir figürden öte, Hz. Hüseyin'in Kûfe'ye gidişinin temelini oluşturan bir etken haline getirir. Kabilenin, Hz. Hüseyin'e olan bağlılıklarını ve onun davetine olan inançlarını Mekke'ye kadar giderek açıkça göstermeleri, olayın siyasi ve dini boyutlarını da daha da derinleştirmektedir. Mezhicoğulları'nın bu cesur ve önemli adımı, Hz. Hüseyin'in Kûfe'de karşılaşacağı zorluklara rağmen, hak ve adalet yolunda mücadele etme kararlılığını da pekiştirmiştir. Bu sayede Mezhicoğulları, Kerbela olayında sadece bir kabilenin ötesinde, Hz. Hüseyin'in davasını ve ilkelerini temsil eden bir sembol haline gelmiştir. Müslim b. Akil'in Kûfe yolculuğu sırasında toplantı yeri olarak kullandığı evin Mezhicli Hânî b. Urve'ye ait olması, çalışmanın şekillenmesi açısından büyük önem taşımaktadır. Bu durum, olayın arka planını ve tarafların ilişkilerini daha iyi anlamamızı sağlayan önemli bir ayrıntıdır. Bu yolculukta yaşanan en çarpıcı olaylardan biri de Mezhicli Ubeydullah el-Cu'fî'nin Hz. Hüseyin'e yardım etmeyi reddetmesi ve sonradan duyduğu pişmanlıktır. Bu olay, hem o anki siyasi atmosferin gerginliğini hem de Ubeydullah'ın sonradan yaşadığı pişmanlığı gözler önüne serer. Bu kabile, katliamın her iki cephesinde de yer alarak, olayın karmaşıklığını ve çelişkilerini gözler önüne sermektedir. Kerbelâ faciası sona erdiğinde Mezhicoğulları, yedi kesik baş ile Ubeydullah’ın huzuruna çıkmışlardır. Kerbelâ faciasından sonra ortaya çıkan bazı siyasi ve dini hareketlerde, aynı kabile üyelerinin Muhtâr es-Sekafî'nin isyanında da öncü rol oynadıkları görülmektedir. Bu durum, Kerbelâ'nın yarattığı derin etkiyi ve kabile üyelerinin bu olaydan nasıl etkilendiklerini göstermesi açısından oldukça önemlidir. Kerbelâ faciasının intikamı olarak nitelendirilen Muhtâr'ın isyanında komutanlık görevini üstlenenler de Mezhicoğulları'ndan kişilerdi. Kerbelâ faciasının öncesinde ve sonrasında aktif bir şekilde rol oynayan bazı Mezhic şahsiyetleri, bu çalışmanın Mezhic açısından önemini artıran unsurlar arasında yer almaktadır. Bu şahsiyetler sırasıyla Amr b. Haccâc, Hânî b. Urve, Sinân b. Enes, Nâfi’ b. Hilâl ve İbrahim b. Mâlik el-Eşter'dir. Mezhic kabilesinin Kerbelâ olayında topyekûn bir hareket sergilememesi, bireysel değerlendirmeleri zorunlu kılmıştır. Bu bağlamda, kabilenin her bir üyesinin olaydaki rolü ve tutumu ayrı ayrı ele alınacaktır. Bu çalışmada, Kerbelâ olayının öncesinden başlayarak sonuca giden yolda kabile üyelerinin almış oldukları tavırlar incelenecektir. Olay, başlangıcından itibaren ele alınacak ve her aşamada kabilenin nasıl tepki verdiğine odaklanılacaktır. Çalışmanın amacı, kabile üyelerinin tutumlarının olayı nasıl etkilediğini ve nihai sonuca nasıl katkıda bulunduğunu analiz etmektir.

Abstract Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.

Abstract Wastewater is a by-product of numerous industrial processes that have been demonstrated to have adverse effects on human and natural health due to the pollutants it contains. The pollutants in these substances are organic or inorganic molecules and heavy metal ions that significantly harm the environment and human health. A variety of techniques have been devised for the removal of heavy metal ions from wastewater. The adsorption process has attracted significant attention due to its straightforward implementation, cost-effectiveness, and the environmentally friendly production of adsorbent materials using biocompatible substances. In this study, the removal of Cu2+ ions from wastewater was conducted using chitosan pullulan, a biocompatible and biodegradable polymer. In addition to chitosan and pullulan, waste refractory materials from a furnace used in iron and steel production were added to these polymer materials to increase the adsorption capacity. The initial step involved grinding the waste refractory brick material. Subsequently, chitosan was dissolved in acetic acid. After that, the refractory material was suspended in this solution, facilitating the formation of hydrogel beads using a NaOH solution. The obtained hydrogels were coated with pullulan to produce polyelectrolyte gel. Pullulan was oxidized to 6-carboxypullulan by the TEMPO (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl) oxidation method and the negatively charged groups in its structure interacted with the positively charged groups in the chitosan structure to produce a complex gel. The chemical structure, morphological analysis, thermal analysis, and water release analysis of the produced waste refractory brick material added chitosan/oxidized pullulan complex gels were examined. The impact of the 6-carboxypullulan coating on the gels’ properties was elucidated. Furthermore, the adsorption of Cu2⁺ was conducted using solutions containing 100, 500, and 1000 ppm Cu2⁺ ions. It has been observed that the material can clean water with over 98% efficiency, even in solutions that exceed the standards set for wastewater. The material’s efficacy in cleaning solutions with concentrations above the standard for wastewater cleaning is evidence of its high performance. Furthermore, the kinetics and isotherm of the adsorption reaction were examined. The kinetics were determined to be consistent with the Pseudo Second Order (chemical reaction controlled) and aligned with the Langmuir and Freundlich Isotherm (mixed adsorption occurred on homogeneous and heterogeneous surfaces).

Smart cities represent the forefront of combining technological innovation with urban management to enhance the quality of life and sustainability of urban environments. While existing studies have focused on individual smart city evaluations, there is a notable gap in systematic classification approaches that can handle uncertain and incomplete data in developing countries. As urban populations continue to grow, the strategic integration of smart technologies in city planning and management becomes crucial, necessitating more sophisticated evaluation methodologies. These technologies offer promising solutions to urban challenges by improving efficiency, economic growth, and citizen engagement. This research addresses this gap by proposing a novel framework that combines Interval Valued Neutrosophic Sets (IVNS) with the EDAS Method, specifically designed to handle the complexities and uncertainties inherent in developing country contexts.The study extensively reviews existing literature and methodologies applied in similar contexts, identifying key limitations in current approaches and building a robust framework that incorporates both new and established criteria. Through the systematic application of IVNS-EDAS methodology across multiple urban environments, this study develops a comprehensive classification system that accounts for both quantitative metrics and qualitative assessments of smart city capabilities. The results showcase a dynamic classification framework that effectively handles data uncertainty while providing clear, actionable insights for urban planners and policymakers. The paper concludes by validating the effectiveness of the proposed approach through a detailed computational study involving diverse stakeholders, confirming its applicability and utility in refining smart city strategies globally, particularly in developing country contexts where data reliability and completeness may be challenging.The study provides specific policy guidelines for each city classification, offering policymakers a structured framework for resource allocation and strategic planning, ranging from foundational infrastructure development in emerging cities to advanced technology integration in metropolitan areas.

The aim of this study is to determine the factors that hinder participation in leisure and recreational activities in tourist destinations. Within this framework, an attempt was made to reach out to the local residents living in the Sinop destination and determine their opinions through a prepared questionnaire. During the data collection process, a total of 431 questionnaires were collected using the convenience sampling method. The collected data were analyzed using appropriate statistical programs. As a result of the analysis, it was determined that the local residents consider time restraints, transportation problems, inadequacy of facilities, individual psychology, lack of companionship and lack of information as obstacles to their participation in leisure and recreational activities. It was found that the majority of local residents have 6-10 hours of leisure time per week, sometimes find it difficult to use their leisure time, and spend most of their leisure time participating in household activities and social events. The majority of the participants in the study consider the recreational facilities of the Sinop destination to be partially sufficient, use the recreational facilities of their destination for 1-5 hours per week, and spend most of their leisure time outside the destination. In addition, the results of the analysis were interpreted within the framework of theoretical and practical contributions, and some recommendations were made for the industry and future research.

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

Abstract A blood turbine-pump system (iATVA), resembling a turbocharger was proposed as a mechanical right-heart assist device without external drive power. In this study, the iATVA system is investigated with particular emphasis on the blood turbine flow dynamics. A time-resolved 2D particle image velocimetry (PIV) set-up equipped with a beam splitter and two high speed cameras, allowed simultaneous recordings from both the turbine and pump impellers at 7 different phased-locked instances. The iATVA prototype is 3D printed using an optically clear resin following our earlier PIV protocols. Results showed that magnetically coupled impellers operated synchronously. As the turbine flow rate increased from 1.6 to 2.4 LPM, the rotational speed and relative inlet flow angle increase from 630 to 900 rpm, and 38 to 55% respectively. At the trailing edges, backflow region spanned 3/5 of the total passage outlet flow, and an extra leakage flow was observed at the leading edge. For this early turbine design, approximately, 75% of the turbine blade passage was not contributing to the impulse operation mode. The maximum non-wall shear rate was ~ 2288 s−1 near to the inlet exit, which is significantly lower than the commercial blood pumps, encouraging further research and blood experiments of this novel concept. Experimental results will improve the hydrodynamic design of the turbine impeller and volute regions and will be useful in computational fluid dynamics validation studies of similar passive devices.

Purpose: This study aimed to examine the prognostic value of perioperative NLR level in terms of mortality in the first postoperative year in elderly patients undergoing hip fracture surgery. Materials and Methods: The study included 236 patients (68.6% female and 31.4% male) who met the specified criteria and had undergone hip fracture surgery. Complete blood counts were recorded at admission and on the fifth postoperative day. Neutrophil-to-lymphocyte ratios (NLR) at admission (NLR-D0) and on the fifth postoperative day (NLR-D5) were identified as the primary variables. Results: The mean age of the participants was 80.1±7.9 years (min: 65 years to max: 99 years). The mean NLR-D0 and mean NLR-D5 values of surviving patients were 6.54 and 4.55, respectively. The mean NLR-D0 and mean NLR-D5 values of patients who died were 6.99 and 8.90, respectively. A significant difference was found in the NLR variability of the patients who survived and those who died. The examination of the effects of NLR-D0 and NLR-D5 parameters on mortality revealed no significant difference in NLR-D0 values between the patients who lived and those who died (p>0.05), but the NLR-D5 value was significantly higher in the patients who died. The mortality rate at the end of the first postoperative year was 35.2%. Conclusion: A high NLR value (NLR-D5>7.85) on the fifth postoperative day in elderly patients undergoing hip fracture surgery may be a predictor of 1-year mortality after hip fracture surgery.

Abstract With heavy reliance on fossil fuels, countries like Saudi Arabia face challenges in reducing carbon emissions from urban bus transportation. Herein, we address the gaps in evaluating proton-exchange membrane fuel cell buses and develop a globally relevant life-cycle assessment model using Saudi Arabia as a case study. We consider various bus propulsion technologies, including fuel cell buses powered by grey and blue hydrogen, battery electric buses, and diesel engines, and include the shipping phase, air conditioning load, and refuelling infrastructure. The assessment illustrates fuel cell buses using blue hydrogen can reduce emissions by 53.6% compared to diesel buses, despite a 19.5% increase in energy use from carbon capture and storage systems. Battery electric buses are affected by the energy mix and battery manufacturing, so only cut emissions by 16.9%. Sensitivity analysis shows climate benefits depend on energy sources and efficiencies of carbon capture and hydrogen production. By 2030, grey and blue hydrogen-powered fuel cell buses and battery electric buses are projected to reduce carbon emissions by 19.3%, 33.4%, and 51% respectively, compared to their 2022 levels. Fully renewable-powered battery electric buses potentially achieve up to 89.6% reduction. However, fuel cell buses consistently exhibit lower environmental burdens compared to battery electric buses.

Abstract Introduction Acute kidney injury (AKI) is linked to disease severity and prognosis in patients with coronavirus disease 2019 (COVID-19), and mortality increases even with milder stages. This study primarily investigated the effects of continuous renal replacement therapy (CRRT) timing on intensive care unit (ICU) mortality in patients with COVID-19 with acute respiratory distress syndrome (ARDS) and AKI. Secondary goals were secondary goals for the ICU, days without life support treatment, and change in post-CRRT day biomarker levels, the length of ICU and overall hospital stay. Methods In this retrospective study, patients with COVID-19 with ARDS and AKI were divided into CRRT initiated at AKI stages 1 and 2, early-CRRT (E-CRRT) and AKI stage 3, late-CRRT (L-CRRT) and followed until discharge or death. Results E-CRRT had 20 patients and L-CRRT had 18 patients. No association between CRRT timing and ICU mortality was detected (p = 0.724). Moreover, the timing was not associated with ICU, total hospital stay, or days without life support treatment. However, it was associated with D-dimer levels for both groups and ferritin and C-reactive protein (CRP) levels for E-CRRT. There were no associations for other markers, such as procalcitonin, troponin T, pro-brain natriuretic peptide (pro-BNP), interleukin-6, fibrinogen, or antithrombin III levels. Conclusions CRRT timing was not associated with ICU mortality, total hospital stay, or days without life support treatment in this cohort. For E-CRRT, ferritin and CRP levels, and for both groups, D-dimer levels, were associated with CRRT timing. Randomized controlled trials are needed to examine the effects of CRRT timing in patients with COVID-19 with ARDS and AKI.

This study combined high-speed optical diagnostics and numerical simulation to investigate soot formation in n-dodecane sprays under conditions characterized by fuel pyrolysis and low oxygen concentrations. Numerical models were employed to predict the evolution of polycyclic aromatic hydrocarbons (PAHs), while the experiments focused on soot formation. A 186-µm single-hole orifice nominal diameter injector was employed to inject well-controlled fuel sprays into a constant-volume chamber operating at 76 bar. We use a short injection duration of approximately 100 µs to maximize the residence time of the fuel, with variations in the ambient gas temperature within the range of 1,400 to 1,700 K, and the oxygen concentration was ranged from 0 to 5 %. Additionally, we conducted closed-homogeneous-reactor and two-stage Lagrangian simulations with various kinetic mechanisms to predict PAH formation and compared the results with experimental data. The experimental results revealed that variations in the ambient temperature and oxygen percentage significantly influenced the pyrolysis and oxidation processes. Soot onset occurred at 1,450 K for oxygen levels of 0, 1, and 3 %, whereas at 5 % oxygen, soot formed at temperatures below 1,400 K. Interestingly, higher oxygen concentrations increased the rates of soot formation at all temperatures tested. By contrast, elevated temperatures reduce the total soot mass owing to enhanced oxidation. The present study also evaluates the influence of fuel composition on soot formation and observes that a higher aromatics content in the fuel leads to a lower soot onset temperature and increased soot mass. Notably, similar trends for both ethanol and n-dodecane fuels are identified in this study. Furthermore, the numerical calculations revealed distinct trends in PAH formation. Although the different mechanisms reasonably captured the trends in benzene formation, they differed in their predictions of the formation rate of pyrene, resulting in potential differences in soot processes. This disparity highlights the need for a comprehensive review and potential modification of the current soot modeling approach.